Papers by He He
REST: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once (2026.acl-long)
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| Challenge: | Recent Large Reasoning Models (LRMs) lack a narrow evaluation paradigm . a single-question evaluation setup suffers from two major limitations . |
| Approach: | They propose a stress-testing framework that exposes LRMs to multiple problems simultaneously. |
| Outcome: | The proposed framework outperforms existing models on reasoning benchmarks and state-of-the-art models. |
VANE: Guiding High-Value Exploration in RLVR via Outcome-Process Novelty Shaping (2026.findings-acl)
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| Challenge: | Extensive experiments on large-scale mathematical reasoning and out-of-distribution tasks demonstrate the effectiveness and generalization of the proposed method. |
| Approach: | They propose a method that quantifies novelty across the outcome space and semantic process space by using reward or solution divergence. |
| Outcome: | Experiments on Qwen2.5-Math-7B demonstrate the proposed method is general and efficient. |
Enabling Self-Improving Agents to Learn at Test Time With Human-In-The-Loop Guidance (2025.emnlp-industry)
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Yufei He, Ruoyu Li, Alex Chen, Yue Liu, Yulin Chen, Yuan Sui, Cheng Chen, Yi Zhu, Luca Luo, Frank Yang, Bryan Hooi
| Challenge: | Existing large language model (LLM) agents are unable to adapt to changing domain knowledge and rules. |
| Approach: | They propose an LLM agent framework that continuously learns updated domain knowledge at test time. |
| Outcome: | The proposed agent improves on a customer due diligence name screening task on . the agent learns updated domain knowledge at test time. |
Insight Over Sight: Exploring the Vision-Knowledge Conflicts in Multimodal LLMs (2025.acl-long)
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| Challenge: | Existing approaches to mitigating vision-knowledge conflict in Large Language Models (MLLMs) are not effective and can be further scaled. |
| Approach: | They propose a framework to generate inputs to simulate and evaluate vision-knowledge conflict in Multimodal Large Language Models (MLLMs) using original images and 1,122 high-quality question-answer pairs, they propose 'a diagnostic benchmark' |
| Outcome: | The proposed framework, benchmark, and analysis contribute to the understanding and mitigation of vision-knowledge conflicts in Multimodal Large Language Models (MLLMs). |
NarrativePlay: Interactive Narrative Understanding (2024.eacl-demo)
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| Challenge: | Existing systems for interactive agents focus on specific capabilities in predetermined scenarios. |
| Approach: | They propose a novel system that allows users to role-play a fictional character and interact with other characters in narratives in an immersive environment. |
| Outcome: | The proposed system generates human-like responses guided by personality traits extracted from narratives. |
CE-RM: A Pointwise Generative Reward Model Optimized via Two-Stage Rollout and Unified Criteria (2026.findings-acl)
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| Challenge: | Existing studies have shown that rule-based evaluation methods are ineffective for open-ended natural language generation. |
| Approach: | They propose a pointwise generative reward model with a dedicated two-stage rollout method and unified query-based criteria that can be trained with 5.7K high-quality data. |
| Outcome: | The proposed model achieves superior performance on diverse reward model benchmarks, especially in Best-of-N scenarios, and delivers more effective improvements in downstream RL practice. |
Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder (2022.coling-1)
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| Challenge: | Existing methods for numerical reasoning are not flexible enough to handle diverse expressions. |
| Approach: | They propose a Relational Graph enhanced Hybrid table-text Numerical reasoning model with Tree decoder which captures relationship between numerical value, table schema, and text information on the encoder side. |
| Outcome: | The proposed model outperforms the baseline model and achieves state-of-the-art results on the publicly available tabletext hybrid QA benchmark. |
PECAN: LLM-Guided Dynamic Progress Control with Attention-Guided Hierarchical Weighted Graph for Long-Document QA (2025.findings-acl)
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| Challenge: | Long-document Question Answering (QA) challenges with large-scale text and long-distance dependencies. |
| Approach: | They propose a method that leverages large language models to control retrieval process . they propose 'attention-based' retrieval methods that construct hierarchical graphs . |
| Outcome: | The proposed method achieves LLM-level performance while maintaining computational complexity comparable to RAG methods. |
ExecVerify: White-Box RL with Verifiable Stepwise Rewards for Code Execution Reasoning (2026.acl-long)
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| Challenge: | Existing methods for code execution reasoning are limited by the difficulty of the training data. |
| Approach: | They propose a model that uses reinforcement learning to reward correct answers from execution traces. |
| Outcome: | The proposed model improves pass@1 by up to 5.9% on code generation tasks over strong baselines. |
Divergent Thoughts toward One Goal: LLM-based Multi-Agent Collaboration System for Electronic Design Automation (2025.naacl-long)
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| Challenge: | Electronic design automation (EDA) is indispensable for the design of integrated circuits. |
| Approach: | They propose a multi-agent collaboration system where multiple agents harbor divergent thoughts converge towards a common goal. |
| Outcome: | The proposed system shows superior performance compared to single-agent systems. |
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models (2025.acl-long)
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Yingshui Tan, Boren Zheng, Baihui Zheng, Kerui Cao, Huiyun Jing, Jincheng Wei, Jiaheng Liu, Yancheng He, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang
| Challenge: | Large language models have created significant safety concerns . factuality ability is crucial in determining whether they can be deployed and applied safely and compliantly within specific regions. |
| Approach: | They propose a benchmark to evaluate the factuality of large language models in China . they evaluate the models' ability to provide accurate and reliable information . |
| Outcome: | The proposed benchmark evaluates the factuality abilities of existing LLMs and compares them to LLM abilities. |
Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation (RAG) is critical for reducing hallucinations and incorporating external knowledge into Large Language Models (LLMs). |
| Approach: | They propose a framework that leverages an LLM to decompose questions into searchable triplets with placeholders. |
| Outcome: | Empirical results show that T2RAG outperforms state-of-the-art multi-round and Graph RAG methods while reducing retrieval costs by up to 45%. |
Large Language Models are good multi-lingual learners : When LLMs meet cross-lingual prompts (2025.coling-main)
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| Challenge: | Experimental results show that Large Language Models can generate rule-based data in long contexts without following all specified rules. |
| Approach: | They propose a novel prompting strategy Multi-Lingual Prompt which automatically translates the error-prone rule that an LLM struggles to follow into another language, thus drawing greater attention to it. |
| Outcome: | The proposed framework outperforms state-of-the-art prompting methods on public datasets across various tasks, with a specific case study in text-to-MIP instances. |
Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs (2026.acl-long)
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Yu Li, Xiaoran Shang, Qizhi Pei, Yun Zhu, Xin Gao, Honglin Lin, Zhanping Zhong, Zhuoshi Pan, Zheng Liu, Xiaoyang Wang, Conghui He, Dahua Lin, Feng Zhao, Lijun Wu
| Challenge: | High-quality post-training data is the primary engine driving LLM capabilities . datasets are often treated as isolated artifacts, overlooking their true developmental context . |
| Approach: | They propose a framework to reconstruct the evolutionary graph of dataset development using data lineage. |
| Outcome: | The proposed framework characterizes domain-specific structural patterns in Math-oriented datasets and general-domain corpora. |
Can Pre-trained Language Models Interpret Similes as Smart as Human? (2022.acl-long)
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| Challenge: | Simile interpretation is a crucial task in natural language processing. |
| Approach: | They propose a task to let PLMs infer the shared properties of similes by probing textual corpora and human-designed questions. |
| Outcome: | The proposed task outperforms pre-trained language models on simile interpretation tasks while still underperforming humans. |
ConfSpec: Efficient Step-Level Speculative Reasoning via Confidence-Gated Verification (2026.acl-long)
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| Challenge: | Existing approaches to chain-of-thought reasoning incur high inference latency due to long generation traces. |
| Approach: | They propose a confidence-gated cascaded verification framework that reduces the trade-off between generation and verification. |
| Outcome: | The proposed framework achieves 2.24 speedups while matching target-model accuracy. |
MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teaming (2025.acl-long)
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| Challenge: | Existing jailbreak techniques rely on single-round interactions, pro-Corresponding author. |
| Approach: | They propose a multi-turn safety alignment framework to address the challenge of securing large language models in multi-round interactions. |
| Outcome: | The proposed framework exhibits state-of-the-art attack capabilities while improving safety performance on safety benchmarks. |
Optimizing Code Retrieval: High-Quality and Scalable Dataset Annotation through Large Language Models (2024.emnlp-main)
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| Challenge: | Existing methods for code retrieval struggle to balance scalability and annotation quality. |
| Approach: | They propose a method that integrates functions called within the repository and information on third-party APIs to enhance the annotation context. |
| Outcome: | The proposed method improves the annotation context by incorporating functions called within the repository and information on third-party API functionalities. |
Neural Topic Modeling with Large Language Models in the Loop (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated promising capabilities in topic discovery, but their direct application to topic modeling suffers from issues such as incomplete topic coverage, misalignment of topics, and inefficiency. |
| Approach: | They propose a novel LLM-in-the-loop framework that integrates Large Language Models with Neural Topic Models (NTMs) global topics and document representations are learned through the NTM, while an LLM refines these topics using an Optimal Transport (OT)-based alignment objective. |
| Outcome: | The proposed framework improves topic interpretability while preserving the efficiency of existing NTMs. |
Sample Design Engineering: An Empirical Study on Designing Better Fine-Tuning Samples for Information Extraction with LLMs (2024.emnlp-industry)
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| Challenge: | Prompt Engineering (PE) is renowned for improving IE performance through prompt modifications, but the realm of sample design for downstream fine-tuning remains unexplored. |
| Approach: | They propose a methodical approach to enhancing LLMs’ post-tuning performance by refining input, output, and reasoning designs. |
| Outcome: | The proposed approach outperforms heuristic design strategies on three complex IE tasks with four additional LLMs. |
Semi-Supervised Reward Modeling via Iterative Self-Training (2024.findings-emnlp)
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| Challenge: | Reward models capture values and preferences of humans and are used in Reinforcement Learning with Human Feedback (RLHF) Traditionally, training large language models relies on extensive human-annotated preference data, which poses significant challenges in terms of scalability and cost. |
| Approach: | They propose a method that enhances RM training using unlabeled data. |
| Outcome: | The proposed approach improves reward models without incurring additional labeling costs on unlabeled datasets. |
Generation-Augmented Retrieval: Rethinking the Role of Large Language Models in Zero-Shot Relation Extraction (2025.findings-emnlp)
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| Challenge: | Recent advances in Relation Extraction (RE) emphasize Zero-Shot methodologies, aiming to recognize unseen relations between entities with no annotated data. |
| Approach: | They propose a plug-in retrieval adjuster that allows rapid fine-tuning without accessing LLMs’ parameters. |
| Outcome: | The proposed model demonstrates comparable performance on multiple benchmarks. |
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism (P18-1)
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| Challenge: | Existing methods focus on normal class and fail to extract relational triplets precisely. |
| Approach: | They propose an end-to-end model which can jointly extract relational triplets from sentences . they employ two different strategies in decoding process: employing only one united decoder or applying multiple separated decodeurs. |
| Outcome: | The proposed model outperforms the baseline method significantly in two datasets. |
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures (P18-1)
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| Challenge: | Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces complexity and fragility. |
| Approach: | They propose a novel sequence-to-sequence (seq2sequ) model which tracks dialogue believes and a two stage copynet instantiation which emonstrates good scalability. |
| Outcome: | The proposed framework outperforms state-of-the-art pipeline-based methods on large datasets and retains satisfactory entity match rate on out-of vocabulary (OOV) cases where pipeline-designed competitors totally fail. |
MDSEval: A Meta-Evaluation Benchmark for Multimodal Dialogue Summarization (2025.findings-emnlp)
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Yinhong Liu, Jianfeng He, Hang Su, Ruixue Lian, Yi Nian, Jake W. Vincent, Srikanth Vishnubhotla, Robinson Piramuthu, Saab Mansour
| Challenge: | Multimodal Dialogue Summarization (MDS) is a critical task with wide-ranging applications. |
| Approach: | They propose a meta-evaluation benchmark for multimodal dialogue summarization based on image-sharing dialogues, corresponding summaries and human judgments . |
| Outcome: | The proposed framework is the first to identify and formalize key evaluation dimensions specific to MDS. |
CE-DA: Custom Embedding and Dynamic Aggregation for Zero-Shot Relation Extraction (2025.coling-main)
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| Challenge: | Existing methods to predict relationships with given entity pairs are lacking in supervised methods. |
| Approach: | They propose a framework for zero-shot Relation Extraction that includes two modules: Custom Embedding and Dynamic Aggregation. |
| Outcome: | The proposed framework shows competitive performance on two ZSRE datasets. |
Two Heads Are Better Than One: Dual-Model Verbal Reflection at Inference-Time (2025.emnlp-main)
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| Challenge: | Existing LLMs struggle to reliably detect subtle reasoning errors in ASAS tasks. |
| Approach: | They propose a dual-model framework with a dedicated Critic model trained for effective reflection that generates precise verbal feedback. |
| Outcome: | The proposed framework outperforms existing ASAS benchmarks and provides valuable insights into the performance of the proposed framework. |
Dual Prompt Tuning based Contrastive Learning for Hierarchical Text Classification (2024.findings-acl)
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| Challenge: | Existing methods focus on hierarchy-aware text feature by exploiting explicit parent-child relationships, resulting in label confusion within each layer. |
| Approach: | They propose a dual-prompt tuning method which emphasizes discrimination among peer labels by performing contrastive learning on each hierarchical layer. |
| Outcome: | The proposed method outperforms existing methods on benchmark datasets and is available on github. |
BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation (2022.emnlp-main)
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| Challenge: | Currently, pre-trained language model (PLM) based metrics are widely adopted in text generation tasks. |
| Approach: | They propose to use PLMs to encode stereotypical societal biases in PLM-based metrics . they show that popular metrics exhibit higher social bias than traditional metrics based on 6 attributes . |
| Outcome: | The proposed method shows that PLM-based metrics exhibit higher social bias than traditional metrics on 6 attributes. |
Stop Looking for “Important Tokens” in Multimodal Language Models: Duplication Matters More (2025.emnlp-main)
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Zichen Wen, Yifeng Gao, Shaobo Wang, Junyuan Zhang, Qintong Zhang, Weijia Li, Conghui He, Linfeng Zhang
| Challenge: | Vision tokens in multimodal large language models often dominate computational overhead due to excessive length compared to linguistic modality. |
| Approach: | They propose a token pruning method which defines an importance criterion for vision tokens and prunes the unimportant vision token during inference. |
| Outcome: | The proposed method can prune 88.9% of vision tokens while maintaining comparable performance. |
VLA-Mark: A cross modal watermark for large vision-language alignment models (2025.emnlp-main)
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Shuliang Liu, Zheng Qi, Jesse Jiaxi Xu, Yibo Yan, Junyan Zhang, He Geng, Aiwei Liu, Peijie Jiang, Jia Liu, Yik-Cheung Tam, Xuming Hu
| Challenge: | Existing text watermarking methods disrupt visual-textual alignment, leaving semantic-critical concepts vulnerable. |
| Approach: | They propose a vision-aligned framework that embeds detectable watermarks into outputs . they combine localized patch affinity, global semantic coherence, contextual attention patterns . |
| Outcome: | The proposed framework shows lower PPL and higher BLEU than conventional methods with near-perfect detection (98.8% AUC). |
Encoding Spreadsheets for Large Language Models (2024.emnlp-main)
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Haoyu Dong, Jianbo Zhao, Yuzhang Tian, Junyu Xiong, Mengyu Zhou, Yun Lin, José Cambronero, Yeye He, Shi Han, Dongmei Zhang
| Challenge: | Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language models (LLMs). |
| Approach: | They propose a structural-anchor-based compression, inverse index translation, and data-format-aware aggregation module to compress spreadsheets effectively. |
| Outcome: | The proposed method outperforms the existing model in GPT4 and achieves a state-of-the-art 78.9% F1 score. |
AIDA-SEAT: Towards Reliable AI Doctor Assistant via State-Evaluation-Action Tree Enhanced LLMs in Online Hospital (2026.acl-industry)
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Lianxin Sun, Xiaoying Ying, Guangya Yu, Weiyan Zhang, Chenhao Guan, Hao He, Mingxi Shang, Jianhua Li, ChunMing Wang, Tong Ruan
| Challenge: | Existing systems rely on large language models or retrieval-augmented generation (RAG) but these methods lack the explicit logical pathways essential for multi-step reasoning. |
| Approach: | They propose an AIDA-SEAT framework to provide reliable clinical decision-making support by transforming and modifying medical documents and doctors' state-evaluation-action trees. |
| Outcome: | The proposed framework achieves 1.01% higher than current state-of-the-art (SOTA) baselines across five departments, including common RAG-based methods. |
How Do Humans Write Code? Large Models Do It the Same Way Too (2024.emnlp-main)
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| Challenge: | Program-of-Thought (PoT) replaces natural language-based Chain-ofThough (CoT) but introduces more reasoning errors, such as incorrect formulas or flawed logic, compared to CoT. |
| Approach: | They propose a method that integrates CoT and Program-of-Thought to achieve more accurate reasoning and reinforcement learning. |
| Outcome: | The proposed method achieves an average improvement of 6.5% on the Llama-Base model and 4.3% on the Mistral-Bass model across 8 mathematical calculation datasets. |
SumSurvey: An Abstractive Dataset of Scientific Survey Papers for Long Document Summarization (2024.findings-acl)
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| Challenge: | a growing need for long document summarization datasets with 16k input is causing problems. |
| Approach: | They propose to use a dataset to analyze salient information in long document summarizations. |
| Outcome: | The proposed dataset outperforms existing models and LLMs in the distribution form of salient information and the distribution of salinal information is an indicator of quality. |
MedEBench: Diagnosing Reliability in Text-Guided Medical Image Editing (2025.findings-emnlp)
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| Challenge: | Text-guided image editing has seen significant progress in natural image domains, but its application in medical imaging remains limited. |
| Approach: | a new benchmark is designed to diagnose reliability in text-guided medical image editing. a clinically grounded evaluation framework measures Editing Accuracy, Context Preservation, and Visual Quality. |
| Outcome: | a new benchmark is designed to diagnose reliability in medical image editing. |
Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder (2021.emnlp-main)
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Shuqi Lu, Di He, Chenyan Xiong, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk
| Challenge: | Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space. |
| Approach: | They propose a self-learning method that pre-trains the autoencoder using a weak decoder to push the encoder to provide better sequence representations. |
| Outcome: | The proposed model significantly boosts the effectiveness and few-shot ability of dense retrieval models on web search, news recommendation, and open domain question answering. |
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency (2024.emnlp-industry)
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Yuhang Yao, Han Jin, Alay Shah, Shanshan Han, Zijian Hu, Dimitris Stripelis, Yide Ran, Zhaozhuo Xu, Salman Avestimehr, Chaoyang He
| Challenge: | Large language models (LLMs) are widely used in commercial applications . low latency is crucial due to system latency, query concurrency, and computational resources constraints. |
| Approach: | They propose a system that can be resource-efficiently served by addressing bottlenecks beyond LLM inference . they propose 4.3 speed up over vLLM and 1.5 higher throughput . |
| Outcome: | The proposed system outperforms state-of-the-arts with 1.5 higher throughput . it achieves 4.3 speed up with 64 concurrent requests on Mixtral 8x7B . |
Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization (2026.findings-acl)
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Yueyang Cang, Xiaoteng Zhang, Erlu Zhao, Zehua Ji, Yuhang Liu, Yuchen He, Zhiyuan Ning, Chen Yijun, Wenge Que, Li Shi
| Challenge: | Recent approaches to optimize communication topology rely on single-sample policy gradients with absolute rewards. |
| Approach: | They propose a topology optimization framework that integrates Group Relative Policy Optimization. |
| Outcome: | The proposed topology optimization framework outperforms state-of-the-art methods on reasoning and code generation benchmarks. |
Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery (2022.emnlp-main)
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| Challenge: | Existing methods for finding out-of-domain intents suffer from in-domain overfitting problem . previous methods fail to transfer prior knowledge to downstream clustering . |
| Approach: | They propose a unified K-nearest neighbor contrastive learning framework to discover OOD intents . they propose IND pre-training objective to learn discriminative features while maintaining intra-class diversity . |
| Outcome: | The proposed framework improves on three benchmark datasets. |
Multi-Document Scientific Summarization from a Knowledge Graph-Centric View (2022.coling-1)
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| Challenge: | Multi-Document Scientific Summarization (MDSS) aims to produce concise and concise summaries for clusters of topic-relevant scientific papers. |
| Approach: | They propose a model that incorporates knowledge graphs into paper encoding and decoding processes and propose 'decoder' for generating knowledge graph information of summary in the form of descriptive sentences. |
| Outcome: | The proposed architecture improves on baselines on the Multi-Xscience dataset. |
Meta-learning via Language Model In-context Tuning (2022.acl-long)
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| Challenge: | Recent advances in large language models have reduced "task learning and prediction" to a simple sequence prediction problem. |
| Approach: | They propose a meta-learning method that recasts task adaptation and prediction as a sequence prediction problem. |
| Outcome: | The proposed method outperforms MAML on two classification tasks and improves on binaryClfs. |
Boundary-Aware LLM Augmentation for Low-Resource Event Argument Extraction (2026.eacl-long)
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| Challenge: | Event argument extraction (EAE) is a crucial task in information extraction but its performance heavily depends on expensive annotated data. |
| Approach: | They investigate argument replacement, adjunction rewriting, their combination, and annotation generation using four LLM-based augmentation strategies. |
| Outcome: | The proposed methods improve performance over boundary-agnostic methods and provide detailed analysis of quality from multiple perspectives. |
Agentic Rubrics as Contextual Verifiers for SWE Agents (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have rapidly advanced on coding tasks, enabling increasingly capable software engineering agents for real-time code editing and bug fixing. |
| Approach: | They propose to use a rubric checklist to create a context-grounded rubric for SWE agents. |
| Outcome: | The proposed rubrics achieve a score of 54.2% on Qwen3-Coder-30B-A3B and 40.6% on Qween3-332B . |
MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection (2026.acl-long)
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| Challenge: | Existing methods for multimodal stance detection face contextual grounding, cross-modal interpretation ambiguity, and single-pass reasoning fragility. |
| Approach: | They propose a multi-agent framework that integrates Retrieval Augmentation for contextual grounding, specialized Multimodal Analysis agents for nuanced interpretation, Reasoning-Enhanced Debate stage and Self-Reflection for robust adjudication. |
| Outcome: | Extensive experiments on five datasets show that the proposed framework outperforms state-of-the-art methods. |
Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models (2025.acl-long)
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Xinlin Zhuang, Jiahui Peng, Ren Ma, Yinfan Wang, Tianyi Bai, Xingjian Wei, Qiu Jiantao, Chi Zhang, Ying Qian, Conghui He
| Challenge: | composition of pre-training datasets for large language models remains undisclosed . current methods for evaluating data quality are limited by single-dimensional evaluation or redundancy-focused strategies. |
| Approach: | They propose a multi-dimensional data selection method that integrates dimensions with existing quality metrics through learned optimal weightings. |
| Outcome: | The proposed method doubles convergence speed for 1.3B model models and improves downstream task performance by 3.23%. |
From Isolated Scoring to Collaborative Ranking: A Comparison-Native Framework for LLM-Based Paper Evaluation (2026.findings-acl)
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Pujun Zheng, Jiacheng Yao, Jinquan Zheng, Chenyang Gu, Guoxiu He, Jiawei Liu, Yong Huang, Tianrui Guo, Wei Lu
| Challenge: | Large language models (LLMs) are currently used to evaluate scientific papers by assigning an absolute score to each paper independently. |
| Approach: | They propose a comparison-native framework for paper evaluation that integrates comparison into both data construction and model learning. |
| Outcome: | The proposed framework achieves an average relative improvement of 21.8% over the strong baseline DeepReview-14B, while exhibiting robust generalization to five previously unseen datasets. |
CORD: Bridging the Audio–Text Reasoning Gap via Weighted On-policy Cross-modal Distillation (2026.findings-acl)
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Hu Jing, Danxiang Zhu, Xianlong Luo, Dan Zhang, Shuwei He, Yishu Lei, Shikun Feng, Hai-Tao Zheng, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Large Audio Language Models (LALMs) exhibit a degradation in knowledge and reasoning capabilities . empirical results show that CORD significantly bridges the audio–text performance gap . |
| Approach: | They propose a framework that performs online cross-modal self-distillation to bridge the acoustic-semantic gap between LALMs and text-based models. |
| Outcome: | The proposed framework bridges the acoustic-semantic gap between LALMs and text-based models . it employs on-policy reverse KL divergence with importance-aware weighting . |
A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder (2024.lrec-main)
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| Challenge: | Existing methods for information extraction (IE) focus on training task-specific models, while common knowledge among different IE tasks is not explicitly modeled. |
| Approach: | They propose a regularization-based transfer learning method for IE via an instructed graph decoder which decodes various complex structures into a graph uniformly based on corresponding instructions. |
| Outcome: | The proposed method can learn common knowledge from existing datasets and transfer it to a new dataset with new labels. |
The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service (2020.lrec-1)
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| Challenge: | Existing datasets for human-like dialogue tasks are deficient due to the complexity of human conversations. |
| Approach: | They construct a large-scale Chinese E-commerce conversation corpus with 1 million dialogues, 20 million utterances, and 150 million words. |
| Outcome: | The proposed dataset includes 1 million multi-turn dialogues, 20 million utterances, and 150 million words. |
Token Pruning in Multimodal Large Language Models: Are We Solving the Right Problem? (2025.findings-acl)
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| Challenge: | Multimodal large language models have shown remarkable performance for cross-modal understanding and generation, yet suffer from severe inference costs. |
| Approach: | They propose to prune redundant tokens in MLLMs to reduce computation and storage costs. |
| Outcome: | The proposed method reduces the computational and storage costs of MLLMs by identifying redundant tokens and pruning them. |
Alignment-Enhanced Decoding: Defending Jailbreaks via Token-Level Adaptive Refining of Probability Distributions (2024.emnlp-main)
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| Challenge: | Existing defenses against jailbreaks focus on perturbing or inspecting inputs, but ignore competing objectives, the underlying cause of alignment failures. |
| Approach: | They propose a novel defense that employs adaptive decoding to address the root causes of jailbreak issues. |
| Outcome: | The proposed defense improves safety alignment while maintaining helpfulness. |
Enhancing Adversarial Transferability in Visual-Language Pre-training Models via Local Shuffle and Sample-based Attack (2025.findings-naacl)
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| Challenge: | Visual-Language Pre-training (VLP) models are vulnerable to adversarial examples . previous studies have focused on improving adversariality of models . |
| Approach: | They propose a local shuffle and sample-based attack that randomly shufts one of the local image blocks and generates adversarial images and samples around them. |
| Outcome: | The proposed attack outperforms other advanced attacks on Large Vision-Language Models and outperformed previous attacks on Visual-Langue Pre-training models. |
Composable Text Controls in Latent Space with ODEs (2023.emnlp-main)
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Guangyi Liu, Zeyu Feng, Yuan Gao, Zichao Yang, Xiaodan Liang, Junwei Bao, Xiaodong He, Shuguang Cui, Zhen Li, Zhiting Hu
| Challenge: | Existing approaches to composable text operations often require plug-and-play . a single LM can perform arbitrary text operation composition in the latent space . |
| Approach: | They propose an efficient approach for composable text operations in the latent space of text . they connect pretrained LMs to the laten space and adapt them to the space . |
| Outcome: | The proposed approach improves on existing methods in the latent space of text. |
Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition (2023.acl-long)
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| Challenge: | Multi-level implicit discourse relation recognition (MIDRR) aims at identifying hierarchical discourse relations among arguments. |
| Approach: | They propose a prompt-based multi-level implicit discourse relation recognition framework that leverages parameter-efficient prompt tuning to drive inputted arguments to match the pre-trained space. |
| Outcome: | The proposed framework achieves comparable results on PDTB 2.0 and 3.0 using about 0.1% trainable parameters compared with baselines. |
Automatic Model Selection with Large Language Models for Reasoning (2023.findings-emnlp)
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| Challenge: | Chain-of-Thought and Program-Aided Language Models offer different strengths and weaknesses. |
| Approach: | They propose a model selection method that uses a large language model to select between two different reasoning methods. |
| Outcome: | The proposed method shows significant performance improvements across eight reasoning datasets with Codex, ChatGPT, and GPT-4. |
Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System (2021.acl-long)
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| Challenge: | Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set. |
| Approach: | They introduce a task, Novel Slot Detection, in the task-oriented dialogue system. |
| Outcome: | The proposed task is based on two public NSD datasets and proposes strong baselines . it aims to identify a sequence of tokens and extract semantic constituents from user queries . |
HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST (2026.findings-acl)
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| Challenge: | Existing approaches to zero-shot Dialog State Tracking (zs-DST) are inadequate to generalize to new domains without extensive training. |
| Approach: | They propose a framework that enhances zero-shot slot inference through robust prompt alignment. |
| Outcome: | Experiments on multi-domain datasets show that HiCoLoRA outperforms baselines, achieving SOTA in zs-DST. |
EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification (2023.emnlp-main)
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| Challenge: | Existing methods to augment training data with counterfactuals fail to handle multi-hop fact verification due to their incapability to preserve complex logical relationships. |
| Approach: | They propose to augment training data with counterfactuals that alter causal features of the original data by preserving logical relationships. |
| Outcome: | The proposed method outperforms the baselines and can generate linguistically diverse counterfactuals without disrupting their logical relationships. |
Regular Expression Guided Entity Mention Mining from Noisy Web Data (D18-1)
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| Challenge: | Named Entity Recognition (NER) is a subtask of the broader problem of Information Extraction (IE) from text. |
| Approach: | They propose a framework that uses Regular Expressions to identify entities from web data . they combine expressive power of REs with ability of deep learning to learn from large data a human expert is asked to label a small set of documents . |
| Outcome: | The proposed framework achieves impressive accuracy while requiring modest human effort. |
MTA:A Merge-then-Adapt Framework for Personalized Large Language Models (2026.acl-long)
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Xiaopeng Li, Yuanjin Zheng, Wanyu Wang, Wenlin Zhang, Pengyue Jia, Yingyi Zhang, Haiying He, Mengyang Ma, Yiqi Wang, Maolin Wang, Xuetao Wei, Xiangyu Zhao
| Challenge: | Personalized Large Language Models (PLLMs) aim to align outputs with individual user preferences . current methods of fine-tuning a separate module for each user are unscalable . |
| Approach: | They propose a Merge-then-Adapt framework for Personalized Large Language Models . they construct a shared Meta-LoRA bank and propose an Adaptive LoRA Fusion stage . |
| Outcome: | The proposed framework outperforms existing SOTA methods on the LaMP benchmark. |
Dialog-Post: Multi-Level Self-Supervised Objectives and Hierarchical Model for Dialogue Post-Training (2023.acl-long)
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| Challenge: | a new method for dialogue representation and understanding is proposed . pre-trained language models (PLMs) are inappropriate for dialogue understanding tasks . |
| Approach: | They propose a method that trains pre-trained language models to fit dialogues . they use a hierarchical segment-wise self-attention network to model dialogues more comprehensively . |
| Outcome: | The proposed method outperforms existing models and achieves a 3.3% improvement on average. |
Better Process Supervision with Bi-directional Rewarding Signals (2025.findings-acl)
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Wenxiang Chen, Wei He, Zhiheng Xi, Honglin Guo, Boyang Hong, Jiazheng Zhang, Nijun Li, Tao Gui, Yun Li, Qi Zhang, Xuanjing Huang
| Challenge: | Existing processes that reward for each step are one-directional and lack a mechanism to model the distance to the final target. |
| Approach: | They propose a process supervision model that evaluates the correctness of previous steps and the probability of future success. |
| Outcome: | The proposed model outperforms existing supervision models like ORM and PRM on reasoning tasks and improves solution re-design. |
Augmenting Compliance-Guaranteed Customer Service Chatbots: Context-Aware Knowledge Expansion with Large Language Models (2025.emnlp-industry)
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| Challenge: | Retrieval-based chatbots leverage human-verified Q&A knowledge to deliver accurate, verifiable responses. |
| Approach: | They propose a similar question generation task for LLM training and inference to enable comprehensive semantic exploration and enhanced alignment with source question-answer relationships. |
| Outcome: | The proposed methods achieve 92% user satisfaction rate in a deployed chatbot system, reflecting an 18% improvement over the baseline. |
All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG (2026.acl-long)
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Dan Wang, Guozhao Mo, Yafei Shi, Cheng Zhang, Bo Zheng, Boxi Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Le Sun
| Challenge: | Existing mRAG systems suffer from a language bias during reranking, systematically favoring English and the query’s native language. |
| Approach: | They propose a language-agnostic utility-driven reranker alignment technique to mitigate language bias during re-ranking. |
| Outcome: | The proposed approach mitigates language bias and consistently improves mRAG performance across languages. |
MERaLiON-AudioLLM: Advancing Speech and Language Understanding for Singapore (2025.acl-demo)
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Yingxu He, Zhuohan Liu, Geyu Lin, Shuo Sun, Bin Wang, Wenyu Zhang, Xunlong Zou, Nancy F. Chen, AiTi Aw
| Challenge: | MERaLiON-AudioLLM is the first general-purpose audio-based large language model for multitask learning. |
| Approach: | They introduce MERaLiON-AudioLLM, a general-purpose audio-based large language model for multitask learning with a focus on Singlish understanding. |
| Outcome: | The proposed model exhibits strong generalization across a diverse set of tasks . it is a leading solution for region-specific AI applications. |
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation (2021.acl-long)
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Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jiawei Low, Lidong Bing, Luo Si
| Challenge: | Existing studies have shown that adapter-based tuning is more parameter-efficient than fine-tuning. |
| Approach: | They propose to add adapter modules to a pretrained language model and update the parameters of adapter module when learning on a downstream task. |
| Outcome: | The proposed method outperforms fine-tuning on low-resource and cross-lingual tasks and settings. |
Distilling ChatGPT for Explainable Automated Student Answer Assessment (2023.findings-emnlp)
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| Challenge: | Existing automated student answer assessment models lack explainable and faithful feedback. |
| Approach: | They propose a framework that leverages ChatGPT for student answer scoring and rationale generation. |
| Outcome: | The proposed method improves the overall QWK score by 11% compared to ChatGPT. |
ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)
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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao
| Challenge: | ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge. |
| Approach: | They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks . |
| Outcome: | ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering. |
Generative-to-Discriminative Test-Time Adaptation via Manifold-Aware Diffusion and Bayesian Distillation (2026.findings-acl)
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Boyun Zhang, Zequn Xie, Fangming Feng, Zihan Zhang, Yongbo He, Chuxin Wang, Sihang Cai, Tao Jin, Qifei Zhang
| Challenge: | Existing discriminative approaches suffer from "confident but wrong" failure mode, blindly adapting to OOD noise leading to error accumulation. |
| Approach: | They propose a TTA framework that harmonizes the robustness of generative diffusion models with the efficiency of discriminative regression networks via Bayesian Diffusion Distillation (BDD). |
| Outcome: | The proposed framework reduces MAE from 0.6872 to 0.5673 and boosts binary accuracy by 5.81 percentage points (reaching 57.33%) it also reduces the MAE of the MOSI to SIMS shift and achieves an 11.18-point gain over the baseline. |
Label Anchored Contrastive Learning for Language Understanding (2022.naacl-main)
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| Challenge: | a novel approach to contrastive learning for language understanding is not fully explored . contrastive training has been widely applied to self-supervised representation learning . |
| Approach: | They propose a label anchored contrastive learning approach for language understanding using a class label. |
| Outcome: | The proposed approach improves on GLUE and CLUE benchmarks by 4.1% compared to the state-of-the-art approaches . the proposed approach also improves under the few-shot and data imbalance settings . |
Beyond Online Sampling: Bridging Offline-to-Online Alignment via Dynamic Data Transformation for LLMs (2025.emnlp-main)
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| Challenge: | Direct Preference Optimization (DPO) eliminates complex reward modeling in aligning large language models with human preferences, but its online variant faces significant efficiency bottlenecks due to costly real-time preference sampling and the reward model annotation. |
| Approach: | They propose a framework that transforms static datasets into dynamically adaptive equivalents without the need for an explicit reward model. |
| Outcome: | The proposed approach matches or exceeds the performance of a fully online DPO. |
JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection (2022.acl-long)
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| Challenge: | Existing methods achieve promising performance in in-target stance detection when trained and tested on the same datasets. |
| Approach: | They propose a joint contrastive learning framework to generalize stance features for unseen targets. |
| Outcome: | The proposed framework achieves state-of-the-art on three benchmark datasets. |
Beyond the Textual: Generating Coherent Visual Options for MCQs (2025.findings-emnlp)
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| Challenge: | Multiple choice questions (MCQs) are crucial for deep thinking and knowledge integration in education. |
| Approach: | They propose a cross-modal options synthesis framework for generating MCQs with visual options. |
| Outcome: | The proposed framework produces a plausible and visually similar answer and distractor . it also includes a discrimination module to identify content suitable for visual options . |
MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models (2023.emnlp-demo)
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| Challenge: | MusicAgent integrates numerous music-related tools and an autonomous workflow to address user requirements. |
| Approach: | a new system is built to integrate music-related tools and an autonomous workflow . the system is based on large language models (LLMs) that can be used to organize and decompose requests . |
| Outcome: | the proposed system integrates numerous music-related tools and an autonomous workflow to address user requirements. |
Data Selection Curriculum for Abstractive Text Summarization (2023.findings-emnlp)
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| Challenge: | Abstractive Text Summarization (ATS) models are commonly trained using large-scale data that is randomly shuffled. |
| Approach: | They propose a data selection curriculum scoring system that measures the learning difficulty of an ATS model and expected performance on an instance. |
| Outcome: | The proposed system surpasses baselines on CNN/DailyMail dataset, utilizing 20% of available instances. |
SwiftPrune: Hessian-Free Weight Pruning for Large Language Models (2025.findings-emnlp)
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| Challenge: | a novel post-training pruning method relies on the Hessian matrix to perform pruning . current pruning methods are computationally intensive and lack performance due to second-order derivative calculations. |
| Approach: | They propose a Hessian-free weight pruning method that reduces computational burden . they use an Exponentially Weighted Moving Average technique to bypass weight sorting . |
| Outcome: | The proposed method achieves hardware-efficient model compression by eliminating computational intensive calculations. |
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2 (2021.acl-srw)
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| Challenge: | Experimental results show that pre-trained language model GPT2 can generate better continuations by learning to generate the in the fine-tuning stage. |
| Approach: | They conduct experiments on an English essay dataset using Chinese-GPT2 . they find that the model can generate better continuations by learning to generate the in the fine-tuning stage. |
| Outcome: | The pre-trained language model GPT2 can generate better continuations by learning to generate the in the fine-tuning stage. |
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) are gaining popularity due to their lack of knowledge hallucination and lack of a coherent model. |
| Approach: | They propose a self-supervised quantized representation method to compress KG structural and semantic knowledge into discrete codes that align the format of language sentences. |
| Outcome: | The proposed framework outperforms existing unsupervised methods producing more distinguishable codes on KG link prediction and triple classification tasks. |
On the Universal Truthfulness Hyperplane Inside LLMs (2024.emnlp-main)
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| Challenge: | Recent studies have explored hallucinations through the lens of internal representations, proposing mechanisms to decipher LLMs’ adherence to facts. |
| Approach: | They propose to train a universal truthfulness hyperplane that distinguishes the model’s factually correct and incorrect outputs on a diverse collection of over 40 datasets and examine its cross-task, cross-domain, and in-domain generalization. |
| Outcome: | The proposed model is able to distinguish factual outputs from incorrect outputs on a diverse collection of over 40 datasets. |
EvoSpark: Endogenous Interactive Agent Societies for Unified Long-Horizon Narrative Evolution (2026.acl-long)
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| Challenge: | Existing systems suffer from social memory stacking and narrative-spatial dissonance . long-horizon narratives suffer from conflicting relational states without resolution . |
| Approach: | They propose a framework to sustain logically coherent long-horizon narratives within endogenous interactive agent societies. |
| Outcome: | Experiments show that the framework outperforms baselines across paradigms. |
Look Twice before You Leap: A Rational Framework for Localized Adversarial Text Anonymization (2026.findings-acl)
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| Challenge: | Existing LLMs rely on remote API services, which creates privacy paradoxes and suboptimal solutions with severe utility collapse. |
| Approach: | They propose a localized and training-free framework with an Attacker-Arbitrator-Anonymizer architecture that allows attackers to filter out ghost leaks. |
| Outcome: | The proposed framework achieves superior privacy-utility trade-off compared to strong baselines. |
Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context (P18-1)
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| Challenge: | Recent studies have shed light on the information encoded by long-term memory networks. |
| Approach: | They propose to use a neural caching model to model the role of context in an LSTM LM . they analyze the increase in perplexity when prior context words are shuffled, replaced, or dropped . |
| Outcome: | The proposed model is highly sensitive to the order of words within the most recent sentence, but ignores word order in the long-range context, suggesting the distant past is modeled only as a rough semantic field or topic. |
Introducing Graph Context into Language Models through Parameter-Efficient Fine-Tuning for Lexical Relation Mining (2025.acl-long)
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| Challenge: | Pre-trained language models can effectively mine lexical relations between word pairs . however, graph features and semantic knowledge of pre-tried models are lacking in the task. |
| Approach: | They propose a parameter-efficient fine-tuning method which integrates graph features and semantic representations for lexical relation classification and lexic entailment tasks. |
| Outcome: | The proposed method integrates graph features and semantic representations for lexical relation mining tasks. |
Show Your Work with Confidence: Confidence Bands for Tuning Curves (2024.naacl-long)
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| Challenge: | a rush to scale up has left us with large, costly language models and little understanding of how different designs compare. |
| Approach: | They propose a method to construct valid confidence bands for tuning curves . they validated their method with ablations and analyze the effect of sample size . |
| Outcome: | The proposed method shows that bootstrap confidence bands do not approximate their target confidence. |
SKD-NER: Continual Named Entity Recognition via Span-based Knowledge Distillation with Reinforcement Learning (2023.emnlp-main)
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| Challenge: | Continual learning for named entity recognition (CL-NER) aims to enable models to continuously learn new entity types while retaining the ability to recognize previously learned ones. |
| Approach: | They propose a model that leverages knowledge distillation to retain memory and employs reinforcement learning strategies to optimize the soft labeling and distillation losses generated by the teacher model to effectively prevent catastrophic forgetting. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets showing that it significantly improves the performance of the CL-NER task. |
Crowdsourcing Question-Answer Meaning Representations (N18-2)
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| Challenge: | Existing datasets for predicate-argument relationships are lacking highly skilled and trained annotators. |
| Approach: | They propose a crowdsourcing scheme to generate question-answer pairs that represent predicate-argument relationships in sentences as a set of question-announcer pairs. |
| Outcome: | The proposed model covers the vast majority of predicate-argument relationships in existing datasets along with many previously under-resourced ones, including implicit arguments and relations. |
MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling (2024.findings-acl)
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| Challenge: | Existing methods for metaphor interpretation are slow due to lack of annotated datasets and effective pre-trained language models. |
| Approach: | They propose a large annotated dataset and a PLM for the metaphor interpretation task. |
| Outcome: | The proposed method improves on metaphor identification and interpretation with comparable baselines on the new dataset. |
Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model (P19-1)
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| Challenge: | Existing models for article comment generation are too long and often result in general and irrelevant comments. |
| Approach: | They propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. |
| Outcome: | The proposed model can generate coherent and informative comments compared with several strong baseline models. |
Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment (2025.findings-acl)
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| Challenge: | Medical dialogue systems (MDS) struggle to identify relevant medical knowledge and generate accurate responses. |
| Approach: | They propose a medical dialogue system that integrates knowledge refining and dynamic prompt adjustment to improve medical knowledge and accuracy. |
| Outcome: | The proposed system outperforms state-of-the-art systems in both generation quality and medical entity accuracy. |
FineLAP: Taming Heterogeneous Supervision for Fine-grained Language-Audio Pretraining (2026.acl-long)
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| Challenge: | Existing audio-language models excel at clip-level understanding but struggle with frame-level tasks. |
| Approach: | They propose a novel training paradigm that advances both clip- and frame-level alignment in CLAP with heterogeneous data. |
| Outcome: | The proposed training paradigm improves both clip- and frame-level alignment in CLAP with heterogeneous data. |
ChatAnime: Towards User-Centered Emotional Support in LLM-based Virtual Character Chat (2026.acl-long)
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| Challenge: | Existing research focuses on character consistency in fictional or game-based scenarios . ESRP framework is designed to align role-playing with real-world user scenarios based on emotional needs. |
| Approach: | They propose a framework to align role-playing with real-world user scenarios and emotional needs. |
| Outcome: | The proposed framework aligns role-playing with real-world user scenarios and emotional needs. |
Modeling Subjectivity in Cognitive Appraisal with Language Models (2025.findings-emnlp)
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| Challenge: | a new study explores how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods fail to achieve satisfactory performance . |
| Approach: | They investigate how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods often fail to achieve satisfactory performance . |
| Outcome: | The proposed model can quantify subjectivity in cognitive appraisal using fine-tuned models and prompt-based large language models. |
From Outcome to Process: Optimizing MoE Load Balancing with MCTS (2026.findings-acl)
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Wenjun Ke, Hengyuan Xu, Ziyu Shang, Yao He, Jiahao Wang, Zijie Xu, Peng Wang, Yuhang Lou, Jiajun Liu
| Challenge: | Existing balancing strategies focus on constraining the final distribution of expert usage, but overlook the routing decisions made at each layer. |
| Approach: | They propose a three-stage framework that leverages process-level rewards to guide balanced expert routing. |
| Outcome: | Extensive experiments show that LayerMoE improves the performance of state-of-the-art LoRA-MoA baselines, yielding an average accuracy gain of 1.39%. |
RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)
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Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu
| Challenge: | recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability. |
| Approach: | They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. |
| Outcome: | The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models. |
Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing studies focus on improving the performance of domain-specific models based on the target dataset. |
| Approach: | They propose a Large Language Model-based Continual Learning (LLM-CL) model for ABSA that learns the target domain’s ability while maintaining the history domains’ abilities. |
| Outcome: | The proposed model obtains new state-of-the-art over 19 datasets. |
The “Knowledge–Behavior Gap” in Cultural Taboo Safety of Large Language Models (2026.acl-long)
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Ying He, Sihang Jiang, Xingzhou Chen, Zhouhong Gu, Yiwei Gu, Minggui HE, Shimin Tao, null Mahongxia, Yanghua Xiao
| Challenge: | Existing cultural benchmarks assess cultural knowledge or values biases, but ignore cultural taboos. |
| Approach: | They propose a benchmark to evaluate and improve the cultural taboo safety of large language models. |
| Outcome: | The proposed benchmark spans 77 countries and regions, and includes over 2,020 taboos. |
Personalized Generation In Large Model Era: A Survey (2025.acl-long)
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Yiyan Xu, Jinghao Zhang, Alireza Salemi, Xinting Hu, Wenjie Wang, Fuli Feng, Hamed Zamani, Xiangnan He, Tat-Seng Chua
| Challenge: | Recent advances in large generative models have catalyzed a paradigm shift in content generation to Personalized Generation (PGen). |
| Approach: | They propose a multi-level taxonomy that systematically formalizes PGen's key components, core objectives, and abstract workflows. |
| Outcome: | The proposed taxonomy bridging PGen research across multiple modalities highlights open challenges and promising directions for future exploration. |
Unsupervised Learning of Syntactic Structure with Invertible Neural Projections (D18-1)
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| Challenge: | Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. |
| Approach: | They propose a generative model that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior. |
| Outcome: | The proposed model outperforms state-of-the-art models on part-of speech (POS) induction and unsupervised dependency parsing without gold POS annotation. |
LLMs Are Prone to Fallacies in Causal Inference (2024.emnlp-main)
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| Challenge: | Recent work shows that causal facts can be extracted from LLMs through prompting . but it is unclear if this success is limited to explicitly-mentioned causal facts in pretraining data . |
| Approach: | They fine tune LLMs on synthetic data and test whether they can infer causal relations . they find that LLM can correctly deduce absence of causal relations from temporal and spatial relations if order is randomized . |
| Outcome: | The proposed model outperforms existing methods on causal inference tasks. |
Addressing Troublesome Words in Neural Machine Translation (D18-1)
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| Challenge: | Neural machine translation (NMT) has weaknesses in handling lowfrequency and ambiguous words, which we refer to as troublesome words. |
| Approach: | They propose to use contextual memory to memorize which target words should be produced in which situations to translate troublesome words. |
| Outcome: | The proposed method outperforms baseline models on Chinese-to-English and English-to German translation tasks. |
Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models (2026.findings-acl)
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| Challenge: | Existing reinforcement learning methods for large reasoning models suffer from excessive verbosity, known as "overthinking." Existing models penalize generated tokens to promote conciseness, but these methods encounter two challenges: they may develop hacking behavior in later stages of training by discarding reasoning steps. |
| Approach: | They propose a framework that steers large reasoning models toward more efficient reasoning . they prioritize correctness while imposing penalties for redundant steps . |
| Outcome: | The proposed framework reduces token usage by 69.7% on AIME24. |
Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs (2026.acl-long)
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Zhenyu Liu, Xuanyu Zhang, Yunxin li, Qixun Teng, Shenyuan Jiang, Haolan Chen, Mingjun Zhao, Fanbo Meng, Yu Xu, Yancheng He, Baotian Hu, Haizhou Li, Min Zhang
| Challenge: | despite significant progress, full-duplex SLMs are constrained by severe modality interference, authors say . modality interferes with acoustic and semantic modeling, making them unintelligent and unnatural . authors propose a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers . |
| Approach: | They propose a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers while preserving cross-modality coherence via a dedicated semantic alignment channel. |
| Outcome: | The proposed method significantly advances the state of the art on full-duplex benchmarks . it decouples conflicting modalities in deep layers while preserving cross-modality coherence . |
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs (P19-1)
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| Challenge: | Existing models to tackle multi-hop reading comprehension (RC) are focusing on a single document or paragraph, but they lack the ability to do reasoning across multiple documents. |
| Approach: | They propose a heterogeneous document-entity graph with different types of nodes and edges to solve multi-hop RC problem. |
| Outcome: | The proposed model can do reasoning over the proposed graph with nodes representation initialized with co-attention and self-attention based context encoders. |
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning (2022.naacl-main)
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| Challenge: | Recent work has found that multi-task training with a large number of diverse tasks can uniformly improve downstream performance on unseen target tasks. |
| Approach: | They aim to disentangle the effect of scale and relatedness of tasks in multi-task representation learning by increasing the number of tasks and incorporating smaller sets of related tasks. |
| Outcome: | The proposed model improves on unseen target tasks by increasing the scale of multi-task learning to incorporate more tasks and developing similarity metrics to incorporate tasks related to the target task. |
Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World (2024.findings-acl)
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| Challenge: | Existing studies on how large language model agents collaborate with humans in equal roles emphasize the importance of coordination and communication. |
| Approach: | They propose to use chain-of-thought prompts to evaluate different collaboration perspectives, from independent to more complex, dependent tasks. |
| Outcome: | The proposed model significantly improves the evaluation metric. |
ToolSafety: A Comprehensive Dataset for Enhancing Safety in LLM-Based Agent Tool Invocations (2025.emnlp-main)
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| Challenge: | Current models exhibit notable vulnerabilities in maintaining safety during multi-step tool interactions and in indirect harm scenarios. |
| Approach: | They propose a safety fine-tuning dataset to fine- tune LLMs into assistants . they propose to use synthesized trajectories and realistic, context-aware sample generation . |
| Outcome: | The proposed model maintains safety in multi-step and indirect harm scenarios with little impact on helpfulness. |
Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering (2025.naacl-long)
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Yu Zhao, Alessio Devoto, Giwon Hong, Xiaotang Du, Aryo Pradipta Gema, Hongru Wang, Xuanli He, Kam-Fai Wong, Pasquale Minervini
| Challenge: | Large language models store factual knowledge in their parameters but their parametric knowledge can conflict with the information provided in the context. |
| Approach: | They propose a training-free representation engineering method that uses pre-trained sparse auto-encoders to control the knowledge selection behaviour of large language models. |
| Outcome: | The proposed method can control the use of both knowledge sources to resolve knowledge conflict in open-domain question-answering tasks surpassing existing representation engineering methods (+10%) and contrastive decoding methods (+5%). |
Forest for the Trees: Overarching Prompting Evokes High-Level Reasoning in Large Language Models (2025.naacl-long)
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| Challenge: | Recent advances in large language models (LLMs) have greatly propelled the progress of natural language process (NLP). |
| Approach: | They propose a deductive paradigm that decomposes the reasoning process and a prompting method that elicits high-level thinking of large language models (LLMs). |
| Outcome: | The proposed method improves ChatGPT and CoT by 19.0% and 3.1% on MMLU’s College Physics, 8.8% and 2.3% on GSM8k, and 10.3% and 2.5% on StrategyQA, respectively. |
ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models (2026.acl-long)
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| Challenge: | Existing evaluation frameworks focus on isolated question-answering tasks that may not capture the essential aspects of strategic reasoning. |
| Approach: | They evaluate 13 large language models across over 800 games in chess . they use a chessian-based framework to test strategic reasoning and pattern recognition . |
| Outcome: | The proposed framework improves performance and basic understanding of large language models. |
Pre-trained Models Perform the Best When Token Distributions Follow Zipf’s Law (2025.emnlp-main)
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| Challenge: | Existing large language models typically fix a vocabulary size in advance, then use Byte Pair Encoding (BPE) to construct the tokenizer. |
| Approach: | They propose a method for determining the vocabulary size by analyzing token frequency distributions through Zipf’s law and propose to use it to optimize model performance. |
| Outcome: | The proposed method improves model efficiency and effectiveness across NLP, genomics, and chemistry. |
LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation (2022.tacl-1)
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| Challenge: | Existing benchmarks for natural language processing focus on understanding or generating short texts . lack of standardized benchmarks makes it difficult to assess and compare models . |
| Approach: | They propose a story-centric benchmark for Chinese long text modeling that aggregates two understanding tasks and two generation tasks. |
| Outcome: | The proposed model outperforms similar-sized models on understanding and generation tasks. |
DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models (2024.findings-acl)
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Taolin Zhang, Qizhou Chen, Dongyang Li, Chengyu Wang, Xiaofeng He, Longtao Huang, Hui Xue’, Jun Huang
| Challenge: | Large language models (LLMs) have shown impressive results, but still suffer from hallucination, i.e., the generation of false information. |
| Approach: | They propose a task of sequential model editing that aims to rectify mistakes continuously. |
| Outcome: | The proposed method significantly outperforms baselines in single-turn and sequential editing. |
LEGO: A Multi-agent Collaborative Framework with Role-playing and Iterative Feedback for Causality Explanation Generation (2023.findings-emnlp)
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| Challenge: | Causality explanation generation is a generative task that aims to explain why a given cause-effect pair is true using natural language. |
| Approach: | They propose a multi-agent framework with role-playing and iterative feedback for causality explanation generation. |
| Outcome: | The proposed framework is superior to existing frameworks on WIKIWHY and e-CARE datasets. |
Don’t Half-listen: Capturing Key-part Information in Continual Instruction Tuning (2025.acl-long)
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Yongquan He, Wenyuan Zhang, Xuancheng Huang, Peng Zhang, Lingxun Meng, Xiang Zhou, Ke Zeng, Xunliang Cai
| Challenge: | Existing methods to improve instruction tuning for large language models may cause catastrophic forgetting (CF) CF is a problem where previously learned abilities are degraded . |
| Approach: | They propose a continual instruction tuning method that uses key-part information gain to replay data and refine training objective. |
| Outcome: | The proposed method achieves superior performance on both seen and held-out tasks. |
Controlling the Focus of Pretrained Language Generation Models (2022.findings-acl)
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| Challenge: | Existing mechanisms to control the model's focus are not available for pretrained transformer-based language generation models. |
| Approach: | They propose to augment a pretrained model with trainable "focus vectors" that are directly applied to the model's embeddings while the model itself is kept fixed. |
| Outcome: | The proposed model is able to generate relevant outputs from user-selected highlights while keeping the model fixed. |
Red-Teaming LLM Multi-Agent Systems via Communication Attacks (2025.findings-acl)
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| Challenge: | Large Language Model-based Multi-Agent Systems (LLM-MAS) have revolutionized complex problem-solving capability by enabling agent collaboration through message-based communications. |
| Approach: | They propose an attack that exploits communication mechanisms in Large Language Model-based Multi-Agent Systems (LLM-MAS) by intercepting and manipulating inter-agent messages. |
| Outcome: | The proposed attack exploits communication mechanisms in large language model-based multi-agent systems by intercepting and manipulating inter-agencies. |
ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback (2023.findings-emnlp)
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Wenxiang Jiao, Jen-tse Huang, Wenxuan Wang, Zhiwei He, Tian Liang, Xing Wang, Shuming Shi, Zhaopeng Tu
| Challenge: | Large language models (LLMs) like ChatGPT are only accessible through restricted APIs, which creates barriers to new research and advancements in the field. |
| Approach: | They propose a framework to enhance and regulate the translation abilities during chat . they reformulate translation data into the instruction-following style and introduce a "Hint" field . |
| Outcome: | The proposed framework enhances and regulates the translation abilities during chat . it reformulates translation data into the instruction-following style and introduces a "Hint" field . |
Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation (2025.findings-acl)
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| Challenge: | Existing systems rely on black-box neural networks, which lack interpretability, which is crucial in mental health contexts. |
| Approach: | They propose a Retrieval-augmented generation framework for Explainable depression detection that retrieves evidence from clinical interview transcripts, providing explanations for predictions. |
| Outcome: | The proposed framework retrieves evidence from clinical interview transcripts, providing explanations for predictions. |
WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models (2025.acl-long)
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| Challenge: | Existing RAG frameworks rely on Automatic Speech Recognition to process speech input, which discards crucial audio information and increases computational overhead. |
| Approach: | They propose a retrieval augmented generation framework with native, end-to-end audio support that integrates audio and text into a unified knowledge representation. |
| Outcome: | The proposed framework can perform 10x faster than current pipelines while delivering 10x acceleration. |
Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference (P19-1)
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| Challenge: | Existing models for SLU use explicit memory representations, but the context memory is under-exploited. |
| Approach: | They propose a dialogue logistic inference task to consolidate the context memory with SLU in a multi-task framework. |
| Outcome: | The proposed model improves slot filling and domain classification performance in a multi-task framework. |
Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems (2022.emnlp-main)
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| Challenge: | Numerical Question Answering is the task of answering questions that require numerical capabilities. |
| Approach: | They propose to conduct numerical capability diagnosis on a series of Numerical Question Answering systems and datasets. |
| Outcome: | The proposed approach relieves existing systems’ lack of robust numerical capabilities. |
HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction (2022.findings-acl)
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| Challenge: | Existing approaches of distantly supervised relation extraction (DSRE) focus on sentence-level or bag-level de-noising, neglecting the explicit interaction with cross levels. |
| Approach: | They propose a hierarchical contrastive learning framework for distantly supervised relation extraction to reduce noisy sentences. |
| Outcome: | The proposed framework outperforms baselines in various mainstream DSRE datasets. |
On the Sentence Embeddings from Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Pre-trained contextual representations like BERT have been widely used for NLP tasks. |
| Approach: | They propose to transform anisotropic sentence embedding distribution to smooth and isotropic Gaussian distribution by normalizing flows that are learned with an unsupervised objective. |
| Outcome: | The proposed method achieves significant performance gains over state-of-the-art embeddings on a variety of semantic textual similarity tasks. |
GCML: Gradient Coherence Guided Meta-Learning for Cross-Domain Emerging Topic Rumor Detection (2025.emnlp-main)
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| Challenge: | Existing domain adaptation rumor detection methods ignore the data generalization differences and rely on a large amount of unlabeled target domain samples to achieve domain adaptation. |
| Approach: | They propose a Gradient Coherence guided Meta-Learning approach for emerging topics rumor detection that selectively learns more "generalizable" tasks that are more beneficial in adapting to the target domain. |
| Outcome: | The proposed method outperforms baselines on real-world datasets and significantly outperformed traditional methods on the in-domain condition. |
Query Rewriting in Retrieval-Augmented Large Language Models (2023.emnlp-main)
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| Challenge: | Existing studies focus on adapting either the retriever or the reader, but this approach is more focused on adaptation of the query itself. |
| Approach: | They propose a new framework for retrieval-augmented Large Language Models . they propose rewrite-retrieve-read instead of retrieve-then-read . |
| Outcome: | The proposed framework improves performance on downstream tasks, open-domain QA and multiple-choice QA. |
Rethinking Smoothness for Fast and Adaptable Entity Alignment Decoding (2025.findings-naacl)
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| Challenge: | Existing methods for integrating knowledge graphs rely on entity and relation embeddings . Fig. 1 shows how to decode knowledge graph in under 6 seconds . |
| Approach: | They propose a framework that only utilizes entity embeddings to decode knowledge graphs. |
| Outcome: | The proposed framework reconstructs KG representation by maximizing smoothness of entity embeddings. |
Reason from Fallacy: Enhancing Large Language Models’ Logical Reasoning through Logical Fallacy Understanding (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but struggle with some more complex reasoning tasks including logical reasoning. |
| Approach: | They propose five concrete tasks from three cognitive dimensions of WHAT, WHY, and HOW to evaluate LLMs’ capability of logical fallacy understanding. |
| Outcome: | The proposed dataset can be used to evaluate LLMs’ LFU capability and to fine-tune LLM models to obtain significantly enhanced performance on logical reasoning. |
ReCon: Active Defense against Large Vision-Language Model Jailbreaks via Reverse Safety Concept Injection (2026.findings-acl)
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| Challenge: | Existing defense strategies neglect visual threats and lack of fine-grained specificity regarding specific attack semantics. |
| Approach: | They propose a black-box defense framework that maps unsafe concepts to fine-grained, constructive Safe Concepts. |
| Outcome: | a new black-box defense framework enhances robustness against jailbreak attacks . it maps detected unsafe concepts to fine-grained, constructive Safe Concepts . the proposed framework is available for free at http://www.epa.org/recon/ . |
On Learning to Summarize with Large Language Models as References (2024.naacl-long)
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Yixin Liu, Kejian Shi, Katherine He, Longtian Ye, Alexander Fabbri, Pengfei Liu, Dragomir Radev, Arman Cohan
| Challenge: | Recent studies have found that summaries generated by large language models (LLMs) are favored by human annotators when compared to reference summary from widely used summarization datasets. |
| Approach: | They propose to use large language models (LLMs) as reference learning settings for smaller text summarization models to investigate whether their performance can be substantially improved. |
| Outcome: | The proposed model outperforms standard supervised fine-tuning and human evaluations while retaining human-level performance. |
Draft Model Knows When to Stop: Self-Verification Speculative Decoding for Long-Form Generation (2025.emnlp-main)
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| Challenge: | Conventional speculative decoding methods use a predefined length policy for proposing drafts, but the reality deviates from this assumption. |
| Approach: | They propose a self-verification length policy that adaptively determines the lengths of draft sequences by referring to the draft entropy. |
| Outcome: | The proposed method achieves 17% speedup on MT-Bench and 22% speedup in long-form reasoning. |
Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models (2025.acl-long)
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JianXing Liao, Junyan Xu, Yatao Sun, Maowen Tang, Sicheng He, Jingxian Liao, Shui Yu, Yun Li, Xiaohong Guan
| Challenge: | Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency. |
| Approach: | They propose a language-guided framework that integrates large language models with computer-automated design to address these challenges. |
| Outcome: | The proposed framework outperforms traditional methods in accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts. |
Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning (2025.coling-main)
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| Challenge: | Large language models generate convincing, fluent explanations, but they often generate inconsistent explanations on different inputs. |
| Approach: | They propose a method that adapts large language models to generate more consistent explanations on related examples. |
| Outcome: | The proposed method yields a 10.0% relative explanation consistency improvement across a variety of question-answering datasets and generalizes to 7 out-of-distribution datasets not seen during finetuning (+4.5%) |
Interpretable Safety Alignment via SAE-Constructed Low-Rank Subspace Adaptation (2026.acl-long)
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| Challenge: | Prior work has shown that safety behaviors are governed by low-rank structures . Low-Rank Adaptation (LoRA) consistently underperforms full fine-tuning and reinforcement learning on safety benchmarks . |
| Approach: | They propose a safety alignment system that disentangles safety-relevant directions into monosemantic features and constructs an interpretable safety subspace from SAE directions. |
| Outcome: | Empirically, the proposed model achieves 99.6% safety rates across multiple model families and scales . low-rank Adaptation consistently underperforms full fine-tuning and reinforcement learning on safety benchmarks compared with previous methods . |
Beyond Literal Descriptions: Understanding and Locating Open-World Objects Aligned with Human Intentions (2024.findings-acl)
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| Challenge: | Existing methods for visual grounding rely on the assumption that the given expression must be literal . this impedes the practical deployment of agents in real-world scenarios. |
| Approach: | They propose a visual grounding task that uses intention expressions to locate foreground entities . they build a large-scale IVG dataset with free-form intention expression to promote VG . |
| Outcome: | The proposed method is based on a large-scale intention-driven visual-language (V-L) dataset with free-form intention expressions. |
Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing approaches to online claim verification rely on prompt engineering or pre-designed reasoning workflows. |
| Approach: | They propose an online reinforcement learning framework that enables an LLM to interact with a search engine and receive reward signals that explicitly shape its planning, retrieval, and reasoning behaviors. |
| Outcome: | Empirical results show that Veri-R1 improves joint accuracy by 30% and doubles evidence score, often surpassing larger-scale model counterparts. |
WaveDetect: Robust Framework for Machine-Generated Text Detection via Wavelet Transform (2026.findings-acl)
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| Challenge: | Existing methods for detecting LLM-generated texts falter when faced with adversarial perturbations, cross-domain shifts, and the rapid temporal evolution of the foundation model. |
| Approach: | They propose a framework that reformulates text detection as a signal processing task within the time-frequency domain. |
| Outcome: | The proposed framework achieves superior accuracy and robustness against sophisticated attacks and generalization across out-of-distribution topics. |
Unsupervised Sentence Representation Learning with Syntactically Aligned Negative Samples (2025.findings-naacl)
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| Challenge: | Existing approaches to sentence representation learning often encounter semantic inconsistencies and feature suppression. |
| Approach: | They propose a method for generating syntactically aligned negative (SAN) samples using a semantic importance-aware Masked Language Model (MLM) approach. |
| Outcome: | The proposed method produces negative samples with substantial textual overlap with the original sentences while conveying different meanings. |
Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents (2026.findings-acl)
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Ying He, Zhouhong Gu, Zhecheng Hu, Yubo Zhou, Hao Shen, Jiaqing Liang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao, Zhixu Li
| Challenge: | Existing LLMs struggle to identify errors in financial documents, a study shows . 18% of financial practitioners make errors daily, one-third make errors several times weekly, and 59% make errors multiple times monthly. |
| Approach: | They introduce FinED-Bench, a publicly available Benchmark for financial error detection . it covers nine real-world financial scenarios and includes over 900 documents in 2025 . supervised fine-tuning can significantly improve the performance of weaker LLMs, they show . |
| Outcome: | The proposed benchmark covers nine real-world financial scenarios and includes over 900 documents reported in 2025 that are unseen by existing language models. |
FlowRAG: Synergizing Explicit Reasoning via Frequency-Aware Multi-Granularity Graph Flow (2026.findings-acl)
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| Challenge: | Existing methods for Graph-based retrieval-augmented generation rely on implicit semantic relevance propagation. |
| Approach: | They propose a semantic-aware retrieval framework that improves both semantic recall and explicit reasoning. |
| Outcome: | Extensive experiments show that FlowRAG improves both semantic recall and explicit reasoning. |
Reflection on Knowledge Graph for Large Language Models Reasoning (2025.findings-acl)
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Yigeng Zhou, Wu Li, Yifan Lu, Jing Li, Fangming Liu, Meishan Zhang, Yequan Wang, Daojing He, Honghai Liu, Min Zhang
| Challenge: | Existing methods for supplementing Large Language Models (LLMs) with knowledge graphs often introduce noise in the retrieval and reasoning pipeline, hindering their ability to integrate external knowledge for complex multi-hop question answering. |
| Approach: | They propose a framework to enhance LLMs' reasoning capabilities through reflective engagement with knowledge graphs by Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction. |
| Outcome: | The proposed framework integrates external knowledge into LLMs and trains them to leverage this knowledge for answering questions. |
RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models (2025.findings-emnlp)
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Jingjing Liu, Zeming Liu, Zihao Cheng, Mengliang He, Xiaoming Shi, Yuhang Guo, Xiangrong Zhu, Yuanfang Guo, Yunhong Wang, Haifeng Wang
| Challenge: | Large Language Models (LLMs) have exhibited significant proficiency in code debugging, especially in automatic program repair. |
| Approach: | They propose a repository-level code debugging dataset with 22 subtypes of errors that supports 8 commonly used programming languages and 3 debug tasks. |
| Outcome: | The proposed dataset supports 8 commonly used programming languages and 3 debugging tasks. |
JiraiBench: A Bilingual Benchmark for Evaluating Large Language Models’ Detection of Human risky health behavior Content in Jirai Community (2026.eacl-long)
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Yunze Xiao, Tingyu He, Lionel Z. Wang, Yiming Ma, Xingyu Song, Xiaohang Xu, Mona T. Diab, Irene Li, Ka Chung Ng
| Challenge: | a cross-lingual dataset captures a transnational cultural phenomenon . risky health behaviors (RHB) are often linked to complex mental health conditions . |
| Approach: | They present the first cross-lingual dataset that captures a transnational cultural phenomenon . their dataset of more than 15,000 annotated social media posts forms the core of JiraiBench . |
| Outcome: | The study shows that cultural context can be more influential than linguistic similarity . the study also shows that the Japanese prompts better handle Chinese content . |
TensorOpera Router: A Multi-Model Router for Efficient LLM Inference (2024.emnlp-industry)
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Dimitris Stripelis, Zhaozhuo Xu, Zijian Hu, Alay Shah, Han Jin, Yuhang Yao, Jipeng Zhang, Tong Zhang, Salman Avestimehr, Chaoyang He
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable performance across a diverse set of domain-specific tasks. |
| Approach: | They propose a non-monolithic LLM querying system that seamlessly integrates various LLM experts into a single query interface and dynamically routes incoming queries to the most high-performant expert based on query’s requirements. |
| Outcome: | The proposed model improves query efficiency by 40% and costs by 30% while maintaining or enhancing model performance by 10%. |
Taming "Zombie" Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution (2026.acl-long)
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Taolin Zhang, Pukun Zhao, Qizhou Chen, Jiuheng Wan, Chen Chen, Xiaofeng He, Chengyu Wang, Richang Hong
| Challenge: | Existing approaches to improve efficiency of multi-agent systems rely on aggressive graph topology evolution . however, such hard pruning overlooks the potential for "zombie" agents to recover and contribute in subsequent discussion rounds. |
| Approach: | They propose a Markov state-aware framework for resilient multi-agent evolution that manages agent collaboration through soft state transitions. |
| Outcome: | The proposed framework outperforms baselines and significantly reduces token consumption through state-aware agent scheduling. |
Generate-on-Graph: Treat LLM as both Agent and KG for Incomplete Knowledge Graph Question Answering (2024.emnlp-main)
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Yao Xu, Shizhu He, Jiabei Chen, Zihao Wang, Yangqiu Song, Hanghang Tong, Guang Liu, Jun Zhao, Kang Liu
| Challenge: | Existing methods to integrate LLMs with Knowledge Graphs (KGs) however, these methods are often incomplete to cover all the knowledge required to answer questions. |
| Approach: | They propose to integrate LLMs with Knowledge Graphs (KGs) to address insufficient knowledge and hallucination issues in Large Language Models. |
| Outcome: | The proposed method outperforms existing methods on two datasets. |
Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation (2025.findings-acl)
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| Challenge: | High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity. |
| Approach: | They propose a framework that compresses instructions into a compact tag space and enhances complexity through RL-guided tag expansion. |
| Outcome: | The proposed framework outperforms existing methods in the evaluation of instruction complexity augmentation and semantic compression of text into a compact tag space. |
HermEs: Interactive Spreadsheet Formula Prediction via Hierarchical Formulet Expansion (2023.acl-long)
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Wanrong He, Haoyu Dong, Yihuai Gao, Zhichao Fan, Xingzhuo Guo, Zhitao Hou, Xiao Lv, Ran Jia, Shi Han, Dongmei Zhang
| Challenge: | HermEs is a spreadsheet formula prediction language that is difficult for Excel users without programming experience to master. |
| Approach: | They propose a hierarchical approach to formula prediction via HiEraRchical forMulet ExpanSion . they propose generating formulas in a fixed order using hierarchically generated formulas . |
| Outcome: | The proposed approach improves formula prediction accuracy by guaranteeing correct grammar and streamlining token-level decoding with high-level Formulet. |
Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults (2026.acl-long)
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| Challenge: | Existing LLM agents struggle with identifying bugs in the Linux kernel . bugs can affect billions of users, affecting the Linux Foundation's research on the topic . |
| Approach: | They propose a LinuxFLBench benchmark to measure the accuracy of LLM agents on the Linux kernel. |
| Outcome: | The proposed framework improves FL accuracy with minimal costs. |
GL-GAN: Perceiving and Integrating Global and Local Styles for Handwritten Text Generation with Mamba (2025.coling-main)
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| Challenge: | Existing models lack the ability to perceive and integrate handwriting styles, which affects the realism of the synthesized samples. |
| Approach: | They propose a Hybrid Style Encoder that captures global and local styles and integrates them into a Dynamic Feature Enhancement Module (DFEM). |
| Outcome: | The proposed model outperforms state-of-the-art models on two widely used handwriting datasets and can provide training data for handwritten text recognition and signature verification. |
FC-Attack: Jailbreaking Multimodal Large Language Models via Auto-Generated Flowcharts (2025.findings-emnlp)
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| Challenge: | Recent research shows that multimodal large language models are vulnerable to jailbreak attacks . |
| Approach: | They propose a jailbreak attack method based on auto-generated flowcharts . the flowchartings are then combined with a benign textual prompt to execute the attack . |
| Outcome: | The proposed method achieves an attack success rate of up to 96% via images and 78% via videos across multiple MLLMs. |
SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine (2025.findings-naacl)
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Xiaochen Wang, Junqing He, Liang Chen, Gholamreza Haffari, Yiru Wang, Zhe Yang, Xiangdi Meng, Kunhao Pan, Zhifang Sui
| Challenge: | Multi-hop Question Answering (MHQA) is a challenging task that requires models to answer multiple questions with multiple passages. |
| Approach: | They propose a self-guided prompting finite state machine to improve multi-hop reasoning abilities by iterating over multiple questions and correcting itself to improve accuracy. |
| Outcome: | The proposed approach outperforms baselines on Musique and other datasets. |
Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive Pretraining (2023.eacl-main)
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| Challenge: | Existing studies on robustness of pretrained multilingual models are limited to the English language. |
| Approach: | They propose to use data augmentation and contrastive loss term to boost robustness of multilingual models in cross-lingual settings. |
| Outcome: | The proposed model outperforms existing models on clean and noisy data in the cross-lingual setting. |
UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation (2024.acl-long)
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Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Yezhaohui Wang, Dawei He, Cheng Peng, Zhonghao Wang, Haiying Deng
| Challenge: | Large language models (LLMs) produce hallucinated text, compromising their practical utility in professional contexts. |
| Approach: | They have developed an unconstrained hallucination generation evaluation benchmark that contains hallucines generated by large language models with minimal restrictions. |
| Outcome: | The proposed benchmarks are based on a Chinese-language dataset that is lacking in the field. |
Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization (2022.acl-long)
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| Challenge: | Abstractive summarization systems still suffer from faithfulness errors, authors say . prior work has proposed models that improve faithfulness, but it is unclear whether this improvement comes from an increased level of extractiveness of the outputs. |
| Approach: | They propose a faithfulness-abstractiveness trade-off curve that serves as a control . they also learn a selector to identify the most faithful and abstractive summary for a given document . |
| Outcome: | The proposed model achieves higher faithfulness scores while being abstractive than the baseline system on two datasets. |
Order Doesn’t Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation (2025.emnlp-main)
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| Challenge: | Logical reasoning is essential for large language models (LLMs) to ensure accurate and coherent inferences. |
| Approach: | They propose an order-centric data augmentation framework based on commutativity in logical reasoning that randomly shuffles independent premises to introduce condition order augmentation. |
| Outcome: | The proposed framework improves LLMs’ reasoning performance and adaptability to diverse logical structures. |
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis (2020.acl-main)
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| Challenge: | sentiment knowledge is ignored in sentiment analysis, despite its use in pretraining. |
| Approach: | They propose to use sentiment knowledge to learn a unified sentiment representation for multiple sentiment analysis tasks. |
| Outcome: | The proposed method outperforms strong pre-training baseline on three kinds of sentiment tasks. |
Contrastive Preference Learning for Neural Machine Translation (2024.findings-naacl)
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| Challenge: | Existing discrepancies between token-level objective and overall sequence-level quality of a model are causing exposure bias and other issues in NMT. |
| Approach: | They propose a contrastive preference model that integrates an indicator function to fine-tune a pre-trained model in Neural Machine Translation. |
| Outcome: | The proposed model outperforms the traditional Plackett-Luce model on three language pairs and also outperFORMs token-level and sequence-level baseline models. |
Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL (2026.findings-acl)
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Zhewei Yao, Guoheng Sun, Łukasz Borchmann, Zheyu Shen, Minghang Deng, Bohan Zhai, Hao Zhang, Ang Li, Yuxiong He
| Challenge: | Translating natural language questions into SQL is a core challenge in natural language understanding and human-computer interaction. |
| Approach: | They propose a reinforcement learning framework and model family to generate accurate, executable SQL using a lightweight reward signal based solely on execution correctness. |
| Outcome: | The proposed framework outperforms previous versions of 70B-class systems and achieves state-of-the-art execution accuracy across six diverse Text2SQL benchmarks. |
Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring (2024.findings-emnlp)
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| Challenge: | Existing methods for generating rationales that justify scoring decisions are not accurate and often contain hallucinated information. |
| Approach: | They propose a framework capable of generating more faithful rationales and matching performance with classifier-based scoring systems. |
| Outcome: | The proposed framework achieves 38% improvement in QWK score compared to prior work . it can be used to match performance with classifier-based scoring systems . |
Q-TOD: A Query-driven Task-oriented Dialogue System (2022.emnlp-main)
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| Challenge: | Existing pipelined task-oriented dialogue systems have difficulties adapting to unseen domains . end-to-end systems are plagued by large-scale knowledge bases in practice . |
| Approach: | They propose a query-driven task-oriented dialogue system that extracts dialogue context information into a natural language query. |
| Outcome: | The proposed system outperforms strong baselines and establishes a new state-of-the-art performance on three publicly available task-oriented dialogue datasets. |
OpenHuEval: Evaluating Large Language Model on Hungarian Specifics (2025.findings-acl)
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Haote Yang, Xingjian Wei, Jiang Wu, Noémi Ligeti-Nagy, Jiaxing Sun, Yinfan Wang, Győző Zijian Yang, Junyuan Gao, Jingchao Wang, Bowen Jiang, Shasha Wang, Nanjun Yu, Zihao Zhang, Shixin Hong, Hongwei Liu, Wei Li, Songyang Zhang, Dahua Lin, Lijun Wu, Gábor Prószéky, Conghui He
| Challenge: | Recent advances in Large Language Models (LLMs) represent significant strides toward artificial general intelligence (AGI). |
| Approach: | They introduce OpenHuEval, the first benchmark for LLMs focusing on the Hungarian language and specifics. |
| Outcome: | The framework reveals intrinsic patterns and mechanisms of LLMs in non-English languages, with Hungarian serving as an example. |
MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs (2025.findings-acl)
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| Challenge: | Existing multi-agent systems lack agent coordination and rely on predefined procedures . existing systems lack adaptive task coordination when task is big and complex . |
| Approach: | They propose a large-scale autonomous LLM-based multi-agent system that generates agents based on task complexity and enables dynamic task decomposition, parallel execution, efficient communication and comprehensive system monitoring. |
| Outcome: | The proposed system outperforms existing systems in task completion efficiency and scalability. |
Automated Chinese Essay Scoring from Multiple Traits (2022.coling-1)
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| Challenge: | Current research on AES focuses on scoring the overall quality or single trait of prompt-specific essays. |
| Approach: | They propose a hierarchical multi-task trait scorer to evaluate quality of writing . they propose an inter-sequence attention mechanism to enhance information interaction . |
| Outcome: | The proposed model outperforms several strong models on ACEA and outperformed other models. |
Noisy Pair Corrector for Dense Retrieval (2023.findings-emnlp)
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| Challenge: | Existing dense retrieval models assume that query-document pairs are exactly matched, resulting in mismatched-pair noise. |
| Approach: | They propose a novel approach to train an effective model with mismatched-pair noise. |
| Outcome: | The proposed model performs well on natural question and triviaQA, code-search benchmarks and SO-DS. |
Distributional Clarity: The Hidden Driver of RL-Friendliness in Large Language Models (2026.acl-long)
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| Challenge: | RL-friendly models exhibit intra-class compactness and inter-class separation in probability assignments . under identical training, Qwen models achieve substantial gains, while others like Llama yield limited improvements. |
| Approach: | They propose a method to quantify distributional clarity in probability space . they show distributional clearness is a trainable property underlying RL-Friendliness . |
| Outcome: | The proposed model families achieve substantial gains under identical training, while others like Llama yield limited improvements. |
To Answer or Not to Answer (TAONA): A Robust Textual Graph Understanding and Question Answering Approach (2025.findings-emnlp)
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Yuchen Yan, Aakash Kolekar, Sahika Genc, Wenju Xu, Edward W Huang, Anirudh Srinivasan, Mukesh Jain, Qi He, Hanghang Tong
| Challenge: | Existing studies assume that generated answers integrate all relevant information from the textual graph. |
| Approach: | They propose a novel GraphRAG model that integrates all relevant information from the textual graph into the generated answer. |
| Outcome: | Extensive experiments validate TAONA’s superior performance for both A-side and B-side tasks. |
Navigating the Shadows: Unveiling Effective Disturbances for Modern AI Content Detectors (2024.acl-long)
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| Challenge: | Recent research indicates that AI-text detection systems lack robustness and struggle to effectively differentiate perturbed texts. |
| Approach: | They propose to evaluate the robustness of current detection systems by using black-box text perturbation methods and adversarial learning experiments. |
| Outcome: | The proposed methods assess the robustness of current detection models across perturbation granularities and the impact of perturbation data augmentation on the robustity of AI-text detectors. |
ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch (2026.acl-long)
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Zheng Liu, Honglin Lin, Xiaoyang Wang, Xin Gao, Yu Li, Mengzhang Cai, Yun Zhu, Zhanping Zhong, Qizhi Pei, Zhuoshi Pan, Xiaoran Shang, Conghui He, Bin Cui, Wentao Zhang, Lijun Wu
| Challenge: | Existing open-source vision language models lack high-quality training data for chart reasoning . current models are simplistic and repetitive, while associated QA pairs are prone to hallucinations . |
| Approach: | They propose a framework to synthesize complex charts and reliable reasoning data from scratch. |
| Outcome: | Experimental results show that ChartVerse-8B surpasses existing models in QA and difficulty . lack of high-quality training data hampers development of open-source models . |
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning (2022.acl-short)
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| Challenge: | Existing methods to find out out-of-domain (OOD) intents do not take prior knowledge of in-domain data into account. |
| Approach: | They propose a disentangled knowledge transfer method to bridge the gap between IND pre-training and OOD clustering by using a unified multi-head contrastive learning framework. |
| Outcome: | The proposed method is able to group new unknown intents into different clusters, enabling future development of the system. |
VALU: A Benchmark for Video Anomaly Temporal Localization and Understanding at Multiple Semantic Levels (2026.acl-long)
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Yixiao He, Menghao Zhang, Haifeng Sun, Jing Wang, Kangheng Lin, Jinghan Wang, Chenye Xu, Pengfei Ren, Qi Qi, Jingyu Wang
| Challenge: | Recent advances in Video Large Language Models (Video-LLMs) enhance the ability of VAU models to describe and interpret anomalies. |
| Approach: | They propose a benchmark that explicitly defines anomalies across five semantic levels and provides detailed temporal boundaries and detailed textual descriptions for each. |
| Outcome: | The proposed benchmark defines anomalies across five semantic levels and provides detailed descriptions for each. |
VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-Tuning (2025.naacl-long)
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Yifan Peng, Krishna C Puvvada, Zhehuai Chen, Piotr Zelasko, He Huang, Kunal Dhawan, Ke Hu, Shinji Watanabe, Jagadeesh Balam, Boris Ginsburg
| Challenge: | Recent studies have augmented large language models (LLMs) with speech capabilities, leading to the development of speech language models. |
| Approach: | They propose a single-stage joint speech-text SFT approach for training SpeechLMs . their model combines text-only SFT data with three types of speech-related data . |
| Outcome: | The proposed model outperforms previous SpeechLMs on speech-based QA tasks while maintaining original speech-only capabilities. |
Code Representation Pre-training with Complements from Program Executions (2024.emnlp-industry)
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| Challenge: | Existing languages have syntactic representations of code to improve code intelligence, but they are difficult to learn from code. |
| Approach: | They propose to embed dynamic information of programs revealed by their test cases into feature representations of code as complements. |
| Outcome: | The proposed method yields 6%/19% mAP improvements over its masked language modeling counterparts. |
TempTool-R1: Tool-Augmented Reinforcement Learning for Temporal Knowledge Graph Question Answering (2026.findings-acl)
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| Challenge: | Existing approaches to temporal knowledge graph question answering struggle with multi-hop reasoning and implicit temporal constraints. |
| Approach: | They propose a temporal tool-based API capable of transforming implicit temporal cues into executable operations and supervised fine-tuning teaches the model to interweave chain-of-thought reasoning with think-then-tool usage. |
| Outcome: | The proposed framework outperforms existing methods on three challenging questions. |
AutoMedEval: Harnessing Language Models for Automatic Medical Capability Evaluation (2025.acl-long)
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Xiechi Zhang, Zetian Ouyang, Linlin Wang, Gerard De Melo, Zhu Cao, Xiaoling Wang, Ya Zhang, Yanfeng Wang, Liang He
| Challenge: | Existing evaluation methods based on large language models (LLMs) are expensive and lack expertise due to limitations in human expertise. |
| Approach: | They propose an open-source automatic evaluation model with 13B parameters specifically engineered to measure the question-answering proficiency of medical LLMs. |
| Outcome: | The proposed model surpasses baselines in terms of correlation with human judgments. |
Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering (2025.acl-long)
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| Challenge: | Existing retrieval-augmented generation methods are insufficient for multi-hop question answering . however, they tend to generate hallucinations due to semantic mismatching . |
| Approach: | They propose to optimize question semantic space for dynamic retrieval-augmented multi-hop question answering by optimizing the semantic embeddings. |
| Outcome: | The proposed method outperforms existing RAG methods in both in- and out-of-domain settings. |
Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering (2026.acl-long)
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| Challenge: | Existing studies on neurons focus on emotion and rhetoric, neglecting their intrinsic connections. |
| Approach: | They propose a framework for fine-grained steering of emotion and rhetoric in large language models . they propose 'neuro-based' masking method that integrates multi-dimensional screening . |
| Outcome: | The proposed method achieves directed induction of non-target sentences and enhancement of emotion tasks via rhetoric neurons. |
Beyond Code: Evaluate Thought Steps for Complex Code Generation (2024.lrec-main)
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| Challenge: | Existing efforts to generate code in C++ rely on relatively simple programming problems . large language models (LLMs) pre-trained on numerous code data have opened up new opportunities for code generation. |
| Approach: | They propose a task that evaluates the quality of thought steps and code implementation . they construct a dataset of complex programming problems in C++ . |
| Outcome: | The proposed task evaluates the quality of thought steps and code implementation in a C++ programming language. |
Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models (2025.findings-emnlp)
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Haonan He, Yuchen Ren, Yining Tang, Ziyang Xu, Junxian Li, Minghao Yang, Di Zhang, Yuan Dong, Tao Chen, Shufei Zhang, Yuqiang Li, Nanqing Dong, Wanli Ouyang, Dongzhan Zhou, Peng Ye
| Challenge: | Biology-Instructions is the first large-scale instruction-tuning dataset for multi-omics biological sequences. |
| Approach: | They propose a large-scale instruction-tuning dataset for multi-omics biological sequences . they propose 'chatMultiOmics' to overcome limitations of current LLMs on multi-ome tasks . |
| Outcome: | The proposed dataset bridges LLMs and complex biological sequence-related tasks while maintaining conversational fluency. |
Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning (2026.eacl-long)
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| Challenge: | Chain-of-Thought prompting is a powerful technique for enhancing language model’s reasoning capabilities, but generating long and correct CoT trajectories is challenging. |
| Approach: | They propose to align the steps of Chain-of-Thought reasoning with loop iterations and apply intermediate supervision during the training of Looped Transformers. |
| Outcome: | The proposed method generates accurate reasoning chains for complex problems exceeding training length, and improves performance of the auto-regressive model. |
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization (2023.findings-eacl)
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| Challenge: | a recent study shows that accessing medical literature is difficult for laypeople because it is written for specialists and contains medical jargon. |
| Approach: | They propose a two-stage strategy to identify relevant content to be simplified . they first generate reference summaries via sentence matching between the original and simplified abstracts . |
| Outcome: | The proposed approach improves on a seq2seq-based test set on an English medical corpus . it also improves the SARI score by 1.1% . |
MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue Generation (2025.naacl-long)
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| Challenge: | Existing evaluation metrics for memory-augmented dialogue systems lack practical value . current evaluation methods only consider passive memory retrieval while ignoring diverse memory recall with rich triggering factors. |
| Approach: | They propose to use long-term memory to create human-like dialogues using chatbots. |
| Outcome: | The proposed benchmark covers memory retrieval and memory recognition tasks with both passive and proactive memory recall data. |
QANom: Question-Answer driven SRL for Nominalizations (2020.coling-main)
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Ayal Klein, Jonathan Mamou, Valentina Pyatkin, Daniela Stepanov, Hangfeng He, Dan Roth, Luke Zettlemoyer, Ido Dagan
| Challenge: | Traditionally, SRL annotations focus on verbal predicates, but other types of predicate are frequent in natural language. |
| Approach: | They propose a semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom, using crowdsourcing and QA-driven annotations. |
| Outcome: | The proposed scheme outperforms existing annotations and is useful for downstream tasks. |
LlmLink: Dual LLMs for Dynamic Entity Linking on Long Narratives with Collaborative Memorisation and Prompt Optimisation (2025.coling-main)
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| Challenge: | Existing methods focus on supervised fine-tuning or limited to one-off prediction, which poses a challenge where the context is long. |
| Approach: | They propose a dynamic approach to CoREFerence resolution in chunked long narratives by deploying dual Large Language Models. |
| Outcome: | The proposed model achieves performance gains over existing models and fine-tuning approaches on long narrative datasets, significantly reducing the resources required for inference and training. |
DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)
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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xin Liu, Zhengyang Wang, Xianfeng Tang, Shiyang Li, Xiang He, Ruijie Wang, Bing Yin, Xiao Gu, Lei Clifton, David A. Clifton
| Challenge: | commercial LLMs can be difficult to use in real-world clinical decision-making . a lightweight LLM can be used to collaborate with diverse clinical tools . |
| Approach: | They propose a lightweight LLM that can be used to build medical LLMs as agents . they use recursive curriculum learning to optimize the LLM in an easy-to-hard progression . |
| Outcome: | The proposed approach outperforms human experts in medical examinations on diverse datasets. |
SingaKids: A Multilingual Multimodal Dialogic Tutor for Language Learning (2025.acl-industry)
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Zhengyuan Liu, Geyu Lin, Hui Li Tan, Huayun Zhang, Yanfeng Lu, Xiaoxue Gao, Stella Xin Yin, Sun He, Hock Huan Goh, Lung Hsiang Wong, Nancy F. Chen
| Challenge: | Empirical studies show that SingaKids provides effective dialogic teaching, benefiting learners at different performance levels. |
| Approach: | They propose a dialogic tutor designed to facilitate language learning through picture description tasks. |
| Outcome: | Empirical studies show that SingaKids provides effective dialogic teaching, benefiting learners at different performance levels. |
CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation (2025.emnlp-main)
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| Challenge: | Prior implicit CoT methods have underperformed in terms of efficiency and robustness by relying on natural language tokens for reasoning. |
| Approach: | They propose a training framework that compresses natural language CoT into continuous space by aligning hidden states of a designated token. |
| Outcome: | The proposed framework outperforms the existing state-of-the-art in 3.1x compression rate and 28.2% accuracy on GSM8k scale. |
Multimodal Graph Transformer for Multimodal Question Answering (2023.eacl-main)
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| Challenge: | a myriad of complex tasks require both prior knowledge and reasoning intelligence. |
| Approach: | They propose a plug-and-play quasi-attention mechanism to integrate multimodal graph information to vanilla self-attention as effective prior. |
| Outcome: | The proposed model is able to perform reasoning across multiple modalities. |
A Unified One-Step Solution for Aspect Sentiment Quad Prediction (2023.findings-acl)
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| Challenge: | Existing ASQP datasets are small and low-density, hindering technical advancement . et al. (2017): aspect sentiment quad prediction provides a complete aspect-level sentiment structure. |
| Approach: | They propose a one-step solution for Aspect sentiment quad prediction that can detect aspect categories and identify aspectopinion-sentiment triplets simultaneously. |
| Outcome: | The proposed solution can detect aspect categories and identify aspectopinion-sentiment triplets simultaneously. |
PolyNorm: Few-Shot LLM-Based Text Normalization for Text-to-Speech (2025.emnlp-industry)
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| Challenge: | Text Normalization (TN) is a key preprocessing step in Text-to-Speech systems. |
| Approach: | They propose a prompt-based approach to TN using Large Language Models (LLMs) they propose scalable experimentation across languages to reduce the reliance on manual rules . |
| Outcome: | The proposed approach reduces the reliance on manual rules and enables broader linguistic applicability with minimal human intervention across eight languages. |
CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenge (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities, but their capabilities in cryptographic decryption tasks remain underexplored. |
| Approach: | They propose a benchmark to evaluate the reasoning capabilities of large language models in cryptographic decryption tasks. |
| Outcome: | The proposed benchmark examines the reasoning capabilities of large language models in cryptographic decryption tasks. |
Cheater’s Bowl: Human vs. Computer Search Strategies for Open-Domain QA (2022.findings-emnlp)
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| Challenge: | Open-domain and multi-hop QA is an important problem for both humans and computers. |
| Approach: | They propose a gamified interface where a human answers complex questions with access to traditional and modern search tools. |
| Outcome: | The proposed interface compares human queries to state-of-the-art QA models . human queries can improve the accuracy of existing systems, the authors argue . |
T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts (2025.acl-long)
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Ziwei Huang, Wanggui He, Quanyu Long, Yandi Wang, Haoyuan Li, Zhelun Yu, Fangxun Shu, Weilong Dai, Hao Jiang, Fei Wu, Leilei Gan
| Challenge: | Existing studies on text-to-image (T2I) models focus on text alignment, image quality, and object composition capabilities. |
| Approach: | They propose a T2I-FactualBench benchmark to evaluate the factuality of knowledge-intensive concept generation. |
| Outcome: | The proposed framework evaluates the factuality of knowledge-intensive concept generation tasks. |
Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training (2025.acl-long)
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Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Jiahao Xu, Tian Liang, Pinjia He, Zhaopeng Tu
| Challenge: | Large Language Models exhibit a level of intelligence that is both impressive and everevolving, but their ability to refuse generating unsafe content is a double-edged sword. |
| Approach: | They propose a method to tackle a refusal position bias within safety tuning data that compromises the models’ ability to appropriately refuse generating unsafe content. |
| Outcome: | The proposed method significantly improves model safety without compromising performance and surpasses baseline methods in defending against attacks. |
Data Whisperer: Efficient Data Selection for Task-Specific LLM Fine-Tuning via Few-Shot In-Context Learning (2025.acl-long)
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Shaobo Wang, Xiangqi Jin, Ziming Wang, Jize Wang, Jiajun Zhang, Kaixin Li, Zichen Wen, Zhong Li, Conghui He, Xuming Hu, Linfeng Zhang
| Challenge: | Using fine-tuning on task-specific data is essential for large language models to be effective in specialized tasks. |
| Approach: | They propose a method that leverages few-shot in-context learning with the model to be fine-tuned. |
| Outcome: | The proposed method outperforms existing methods with a 3.1-point improvement and a 7.4 speedup on the Llama-3-8B-Instruct model using just 10% of the dataset. |
Progressive Re-ranking for Multimodal Retrieval-Augmented Generation via Curriculum Learning (2026.findings-acl)
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| Challenge: | Existing approaches to improve retrieval performance of large language models are limited by static knowledge. |
| Approach: | They propose a multimodal re-ranking framework that combines curriculum learning with fine-grained reranking and multimodal section reassessment to improve CLIP-based visual coarse-grain retrieval. |
| Outcome: | The proposed framework achieves state-of-the-art answer accuracy and competitive retrieval performance on InfoSeek and Enc-VQA. |
FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm (D19-55)
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| Challenge: | Existing spell checkers for Chinese are based on denoising autoencoder and decoder paradigms that require a small amount of data to be effective. |
| Approach: | They propose a Chinese spell checker based on a new paradigm which consists of a denoising autoencoder and a decoder. |
| Outcome: | The proposed spell checker is faster, more Adaptable to simplified and traditional Chinese texts and has a much simpler structure to be as much Powerful in error detection and correction. |
SGG: Learning to Select, Guide, and Generate for Keyphrase Generation (2021.naacl-main)
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| Challenge: | Existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. |
| Approach: | They propose to deal with present and absent keyphrases separately with different mechanisms by using a hierarchical neural network with a pointing-based selector and a selection-guided generator. |
| Outcome: | The proposed model outperforms baselines on four keyphrase generation tasks and shows extensibility in natural language generation tasks. |
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning (2024.lrec-main)
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| Challenge: | General pre-trained language models (PLMs) leverage relation triples from knowledge graphs (KGs) and integrate external data sources into language models via self-supervised learning. |
| Approach: | They propose to learn Knowledge-Enhanced language representations with Hierarchical Reinforcement Learning (KEHRL) to detect positions for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge. |
| Outcome: | The proposed model can detect essential positions in texts for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge. |
SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis (2025.coling-main)
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Senbin Zhu, ChenYuan He, Hongde Liu, Pengcheng Dong, Hanjie Zhao, Yuchen Yan, Yuxiang Jia, Hongying Zan, Min Peng
| Challenge: | Currently, most sentiment analysis corpora use sequence-level annotation. |
| Approach: | They propose a two-stage approach to financial entity-level sentiment analysis called Self-aware In-context Learning Correction. |
| Outcome: | The proposed approach achieves state-of-the-art on the largest English and Chinese financial entity-level sentiment analysis datasets to date. |
Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations (D18-1)
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| Challenge: | Existing learning approaches for parsing from denotations (SpFD) do not provide access to correct representations, so there are two steps for every training example. |
| Approach: | They propose a framework for parsing from denotations that generalizes three different learning algorithms. |
| Outcome: | The proposed framework outperforms previous work by 5.0% absolute on exact match accuracy on a question answering dataset. |
HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation (2023.acl-long)
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| Challenge: | Similes are a crucial part of creative writing, but there is still a lack of evaluation metrics for simile generation. |
| Approach: | They propose to use similes as a tool to evaluate simile generation metrics . they propose to combine five criteria and automatic metrics for each criterion . |
| Outcome: | The proposed metrics are significantly more correlated with human ratings from each perspective compared with prior automatic metrics. |
Knowledge-Guided Dynamic Modality Attention Fusion Framework for Multimodal Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing methods focus on equally treating the contribution of each modality or statically using text as the dominant modality to conduct interaction, which neglects the situation where each modal may become dominant. |
| Approach: | They propose a Knowledge-Guided Dynamic Modality Attention Fusion Framework (KuDA) that uses sentiment knowledge to guide the model dynamically selecting the dominant modality and adjusting the contributions of each modality. |
| Outcome: | The proposed model can be used to highlight the contribution of dominant modality through the correlation evaluation loss. |
Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs (2023.findings-emnlp)
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| Challenge: | Complex Query Answering (CQA) is a challenge task of Knowledge Graphs due to incompleteness of KGs. |
| Approach: | They propose a query embedding approach that decouples the training for simple and complex queries. |
| Outcome: | The proposed approach decouples training for simple and complex queries and achieves state-of-the-art performance over three public benchmarks. |
Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training (2022.emnlp-industry)
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Taolin Zhang, Junwei Dong, Jianing Wang, Chengyu Wang, Ang Wang, Yinghui Liu, Jun Huang, Yong Li, Xiaofeng He
| Challenge: | Existing knowledge-enhanced pre-trained language models (KEPLMs) can capture internal knowledge, but can't understand external background knowledge. |
| Approach: | They propose to use Chinese knowledge-enhanced pre-trained language models to improve context-aware representations via learning from structured relations in knowledge bases. |
| Outcome: | Experiments show that Chinese knowledge-enhanced pre-trained language models outperform strong baselines over various benchmark NLP tasks and in different model sizes. |
Cross-layer Attention Sharing for Pre-trained Large Language Models (2026.tacl-1)
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Yongyu Mu, Yuzhang Wu, Yuchun Fan, Chenglong Wang, Hengyu Li, Jiali Zeng, Qiaozhi He, Murun Yang, Fandong Meng, Jie Zhou, Tong Xiao, Jingbo Zhu
| Challenge: | Existing studies focus on compressing the Key-Value cache or grouping attention heads, while overlooking redundancy between layers. |
| Approach: | They propose a lightweight substitute for self-attention in well-trained LLMs that uses feed-forward networks to align attention heads between adjacent layers and low-rank matrices to approximate differences in layer-wise attention weights. |
| Outcome: | The proposed model reduces redundancy by sharing weights across layers while maintaining high response quality while reducing redundant calculations within 53% 84% of the total layers. |
Attack Prompt Generation for Red Teaming and Defending Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing studies construct attack prompts via manual or automatic methods, but these methods have limitations on cost and quality. |
| Approach: | They propose an attack framework to instruct LLMs to mimic human-generated prompts through in-context learning and a defense framework that fine-tunes victim LLM's through iterative interactions with the attack framework. |
| Outcome: | The proposed approach is based on experiments on different LLMs to evaluate their effectiveness against red teaming attacks. |
Generalized Intent Discovery: Learning from Open World Dialogue System (2022.coling-1)
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| Challenge: | Existing intent classification models rely on a pre-defined intent set and supervised labels, which is limited in some practical scenarios. |
| Approach: | They propose to extend an IND intent classifier to an open-world intent set including IND and OOD intents. |
| Outcome: | The proposed task can classify IND and OOD intents while discovering new unlabeled OOD types incrementally. |
Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels (2022.findings-naacl)
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| Challenge: | Existing methods to train pre-trained language models for zero-shot cross-lingual tasks are noisy and lack confidence. |
| Approach: | They propose an uncertainty-aware cross-lingual transfer framework with pseudo-partial-label to maximize the utilization of unlabeled data by reducing noise. |
| Outcome: | The proposed framework outperforms baselines on named entity recognition and natural language inference tasks on 40 languages. |
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation (2023.acl-long)
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| Challenge: | Existing methods for fewshot text classification depend on inter-class variance . Existing approaches suffer from MLADA, which performs poorly on tasks with high inter- class variance whereas it fails to distinguish samples from tasks with low inter-group variance. |
| Approach: | They propose a task-adaptive reference transformation network to transform class prototypes to per-class fixed reference points in task-adapted metric spaces. |
| Outcome: | The proposed method surpasses state-of-the-art methods in 1-shot and 5-shot classifications on the 20 Newsgroups dataset. |
Correctable-DST: Mitigating Historical Context Mismatch between Training and Inference for Improved Dialogue State Tracking (2022.emnlp-main)
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Hongyan Xie, Haoxiang Su, Shuangyong Song, Hao Huang, Bo Zou, Kun Deng, Jianghua Lin, Zhihui Zhang, Xiaodong He
| Challenge: | Existing dialogue state tracking approaches predict the dialogue state of a target turn sequentially based on the ground-truth previous dialogue state. |
| Approach: | They propose a method that predicts dialogue state sequentially based on previous dialogue state . they propose generating a previously “predicted” dialogue state using ground-truth previous dialogue states . |
| Outcome: | The proposed method achieves 67.51%, 68.24%, 70.30%, 71.38%, and 81.27% joint goal accuracy on MultiWOZ 2.0-2.4 datasets. |
The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation (2023.findings-emnlp)
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| Challenge: | Modern artificial intelligence is characterized by large pretrained language models with strong language capabilities to be adapted to various downstream tasks. |
| Approach: | They propose to use the task of speech translation (ST) to pretrain speech models for end-to-end SLU on intra- and cross-lingual scenarios. |
| Outcome: | The proposed model achieves higher performance over baselines on monolingual and multilingual intent classification as well as spoken question answering using SLURP, MINDS-14, and NMSQA benchmarks. |
UTBoost: Rigorous Evaluation of Coding Agents on SWE-Bench (2025.acl-long)
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| Challenge: | Large language models have enabled the development of coding agents for real-world code generation. |
| Approach: | They propose a novel LLM-driven test case generator that analyzes codebases and dependencies to generate test cases for real-world Python projects. |
| Outcome: | The proposed framework improves the performance of SWE-Bench by analyzing codebases and dependencies. |
Can’t See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs (2025.acl-long)
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Wenxuan Wang, Xiaoyuan Liu, Kuiyi Gao, Jen-tse Huang, Youliang Yuan, Pinjia He, Shuai Wang, Zhaopeng Tu
| Challenge: | Multimodal Large Language Models (MLLMs) have expanded the capabilities of traditional language models by enabling interaction through both text and images. |
| Approach: | They propose a multimodal safety awareness benchmark to evaluate MLLMs across 29 safety scenarios with 1,500 carefully curated image-prompt pairs. |
| Outcome: | The proposed model is able to identify unsafe content and avoid over-sensitivity that can hinder helpfulness. |
ZSEE: A Dataset based on Zeolite Synthesis Event Extraction for Automated Synthesis Platform (2024.findings-naacl)
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| Challenge: | Automated synthesis of zeolite holds great significance for attaining economic and environmental benefits. |
| Approach: | They propose an event extraction task to mine structural synthesis actions from experimental narratives for modular automated synthesis. |
| Outcome: | The proposed method can significantly expedite automated synthesis of zeolites owing to its machine readability. |
Unveiling Divergent Inductive Biases of LLMs on Temporal Data (2024.naacl-short)
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| Challenge: | Temporal relations play a crucial role across diverse applications, including event summarization, predicting future events and medical information processing. |
| Approach: | They evaluate the performance of large language models in the analysis of temporal data using two prompt types, Question Answering and Textual Entailment. |
| Outcome: | The proposed models show that they are biased towards specific temporal relationships, while GPT-3.5 prefers “AFTER” for implicit and explicit events, while TE models lean towards “BEFORE”. |
Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking (2020.findings-emnlp)
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| Challenge: | Recent work on sentence prediction tasks uses shallow neural networks to learn essay representations and constrain calculated scores with regression loss or ranking loss. |
| Approach: | They propose to use a pre-trained language model to learn text representations first and then to constrain the scores with regression loss or ranking loss. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Automated Student Assessment Prize dataset. |
Parallel Refinements for Lexically Constrained Text Generation with BART (2021.emnlp-main)
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| Challenge: | Existing work injects lexical constraints into the output, which generates generic or ungrammatical sentences and has high computational complexity. |
| Approach: | They propose a model that incorporates pre-specified keywords into the output to control the generated text. |
| Outcome: | The proposed model decomposes the generated text into two sub-tasks and improves the sentence quality. |
TurnaboutLLM: A Deductive Reasoning Benchmark from Detective Games (2025.emnlp-main)
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| Challenge: | evaluating large language models' reasoning abilities via detective stories is often infeasible due to the large answer space and diverse reasoning types presented by its questions. |
| Approach: | They propose a framework and dataset for evaluating the deductive reasoning abilities of Large Language Models (LLMs) by leveraging the interactive gameplay of detective games Ace Attorney and Danganronpa. |
| Outcome: | The proposed framework and dataset are based on the detective games Ace Attorney and Danganronpa and show that they are more efficient than current strategies for enhancing deductive reasoning. |
UFO: A UI-Focused Agent for Windows OS Interaction (2025.naacl-long)
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Chaoyun Zhang, Liqun Li, Shilin He, Xu Zhang, Bo Qiao, Si Qin, Minghua Ma, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | UFO is a UI-Fcused agent designed to fulfill user requests tailored to Windows OS applications . it decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications. |
| Approach: | They propose a UI-Fcused Windows OS agent that decomposes user requests using a divide-and-conquer approach and incorporates a control interaction module tailored for Windows OS. |
| Outcome: | The proposed agent decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications. |
Zero-Shot Conversational Stance Detection: Dataset and Approaches (2025.findings-acl)
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| Challenge: | Existing stance detection datasets are limited to a limited set of specific targets . current models are limited in their ability to detect large numbers of unseen targets based on a large number of unidentified targets. |
| Approach: | They propose a speaker interaction and target-aware prototypical contrastive learning model that can detect public opinion towards specific targets using social media data. |
| Outcome: | The proposed model achieves state-of-the-art in zero-shot conversational stance detection with only an F1-macro score of 43.81%. |
Select-Then-Decompose: From Empirical Analysis to Adaptive Selection Strategy for Task Decomposition in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing task decomposition methods focus on memory, tool usage, and feedback mechanisms, but they often overlook the trade-off between performance and cost. |
| Approach: | They propose a strategy that selects the most suitable decomposition approach based on task characteristics and enhances the reliability of the results through a verification module. |
| Outcome: | The proposed strategy is based on categories of approaches, characteristics of tasks, and configuration of decomposition and execution models. |
Towards Efficient NLP: A Standard Evaluation and A Strong Baseline (2022.naacl-main)
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Xiangyang Liu, Tianxiang Sun, Junliang He, Jiawen Wu, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, Xipeng Qiu
| Challenge: | Rather than pursuing the reachless SOTA accuracy, researchers are focusing on model efficiency and usability. |
| Approach: | They propose an evaluation and a public leaderboard for efficient NLP models that depicts the Pareto Frontier for various language understanding tasks. |
| Outcome: | The proposed model outperforms or performs on par with SOTA compressed and early exiting models. |
DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale (2025.findings-acl)
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Linghao Zhang, Junhao Wang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Jiaheng Wen, Chengxing Xie, Maoquan Wang, Yufan Huang, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Existing studies highlight that dependency-related issues cause over 40% of observed runtime errors on the generated repository. |
| Approach: | They propose a large-scale benchmark and evaluation framework specifically designed to assess LLMs’ capability on dependency inference. |
| Outcome: | The proposed model achieves only a 48% execution pass rate on Python, indicating room for improvement. |
Towards Automatic Evaluation for Image Transcreation (2025.naacl-long)
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| Challenge: | Existing methods for evaluating image transcreation have relied on human evaluation. |
| Approach: | They propose a suite of automatic evaluation metrics inspired by machine translation metrics . they identify cultural relevance, semantic equivalence and visual similarity as critical dimensions of image transcreation . |
| Outcome: | The proposed evaluation metrics agree with human ratings across 7 countries. |
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing (P18-1)
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| Challenge: | Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. |
| Approach: | They propose a variational auto-encoding model for semi-supervised semantic parsing which learns from limited amounts of parallel data and readily-available unlabeled NL utterances. |
| Outcome: | Experiments on ATIS domain and Python show that with extra unlabeled data, StructVAE outperforms strong supervised models. |
DKME: Rethinking Coupled Knowledge Memory for Lifelong Model Editing of Large Language Models (2026.findings-acl)
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| Challenge: | Existing memory-based editors suffer from catastrophic forgetting as edits accumulate. |
| Approach: | They propose a method which injects factual updates into large language models without retraining or finetuning into existing memory-based editors. |
| Outcome: | Experiments on HalluEditBench, CKnowEdit, and WikiDatacounterfact show that the proposed model achieves a more favorable trade-off between editing success and locality compared to baselines while maintaining more stable performance as the edit scale increases. |
TRACE: Traversal Retrieval-Augmented Chain of Evidence for Document Understanding (2026.acl-long)
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| Challenge: | Long-context Document Visual Question Answering (DocVQA) methods struggle with visual semantics or handling finite context windows. |
| Approach: | They propose a new approach to longcontext document visual question answering that transforms retrieval into adaptive evidence chain construction using a Bi-Layered Graph. |
| Outcome: | The proposed approach achieves an average accuracy improvement of 14.07% on M5BookVQA and exhibits robust generalization with a 13.38% gain across four established benchmarks. |
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources (2021.findings-acl)
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| Challenge: | MRC is a popular task in NLP, aiming to understand a passage and answer the relevant questions. |
| Approach: | They propose a multi-target machine learning task for the medical domain that predicts answers to medical questions and corresponding support sentences from medical information sources simultaneously. |
| Outcome: | The proposed model outperforms baselines by fusing context-aware and knowledge-awful token representations. |
Detect and Perturb: Neutral Rewriting of Biased and Sensitive Text via Gradient-based Decoding (2021.findings-emnlp)
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| Challenge: | Written language carries explicit and implicit biases that can distract from meaningful signals; at worst they can lead to unfair outcomes. |
| Approach: | They propose a gradient-based rewriting framework that detects and perturbs sensitive components and regenerates fluent alternatives that are neutral in the sensitive attribute while maintaining the semantics of other attributes. |
| Outcome: | The proposed framework regenerates fluent alternatives that are neutral in the sensitive attribute while maintaining the semantics of other attributes. |
Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning (C18-1)
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| Challenge: | Existing approaches to named entity recognition (NER) in Chinese are limited by the lack of annotated data. |
| Approach: | They propose a method which can automatically populate annotated training data without humancost by using distant supervision. |
| Outcome: | The proposed method performs better than comparison systems on two datasets. |
TableFormer: Robust Transformer Modeling for Table-Text Encoding (2022.acl-long)
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| Challenge: | Existing tables models require linearization of the table structure, where row or column order is encoded as an unwanted bias. |
| Approach: | They propose a robust and structurally aware table-text encoding architecture TableFormer where tabular structural biases are incorporated completely through learnable attention biase. |
| Outcome: | The proposed architecture outperforms strong baselines on SQA, WTQ and TabFact table reasoning datasets and achieves state-of-the-art performance on SQ. |
PiCSAR: Probabilistic Confidence Selection and Ranking for Reasoning Chains (2026.findings-acl)
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Joshua Ong Jun Leang, Zheng Zhao, Aryo Pradipta Gema, Sohee Yang, Wai-Chung Kwan, Xuanli He, Wenda Li, Pasquale Minervini, Eleonora Giunchiglia, Shay B Cohen
| Challenge: | Recent studies show that large reasoning models (LLMs) achieve strong performance on complex reasoning tasks. |
| Approach: | They propose a method that scores each candidate generation using the joint log-likelihood of the reasoning and final answer. |
| Outcome: | The proposed method outperforms baselines with 2x fewer samples in 20 out of 25 comparisons. |
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization (2023.acl-long)
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Pengcheng He, Baolin Peng, Song Wang, Yang Liu, Ruochen Xu, Hany Hassan, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang
| Challenge: | Z-Code++ is a pre-trained language model optimized for abstractive text summarization. |
| Approach: | They propose a pre-trained language model optimized for abstractive text summarization that uses a two-phase pre-training technique to improve model's performance. |
| Outcome: | The proposed model outperforms the competing models on low-resource summarization tasks in zero-shot and few-shot settings. |
Granular Entity Mapper: Advancing Fine-grained Multimodal Named Entity Recognition and Grounding (2024.findings-emnlp)
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| Challenge: | Existing methods for fine-grained content extraction are limited by long-tailed distribution of textual entity categories and performance of object detectors. |
| Approach: | They propose a multi-granularity entity recognition module and a reranking module to integrate hierarchical information of entity categories, visual cues, and external textual resources collectively. |
| Outcome: | The proposed framework achieves state-of-the-art on the fine-grained content extraction task. |
The Role of Model Confidence on Bias Effects in Measured Uncertainties for Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Quantifying epistemic uncertainty in open-ended tasks is challenging due to the presence of aleatoric uncertainty, which arises from multiple valid answers. |
| Approach: | They conduct experiments on visual question answering tasks and find that mitigating prompt-introduced bias improves uncertainty quantification. |
| Outcome: | The proposed approach reduces uncertainty quantification in visual question answering tasks by mitigating prompt-introduced biases. |
MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibration (2025.acl-long)
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| Challenge: | Existing methods calibrate model confidence on entire response, which leads to incorrect answers with high confidence. |
| Approach: | They propose a framework that advances the knowledge boundary awareness of multimodal large language models through reasoning step confidence calibration. |
| Outcome: | Empirical results show that the proposed framework outperforms existing methods across domains and metrics. |
AgentFactory: A Self-Evolving Framework Through Executable Subagent Accumulation and Reuse (2026.acl-demo)
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| Challenge: | Existing frameworks for building LLM-based agents treat agent behavior as static-knowledge gained during execution is not preserved for future use. |
| Approach: | They propose a new paradigm that preserves successful task solutions as executable subagent code rather than textual experience. |
| Outcome: | The proposed agent-based agent-driven paradigm preserves successful tasks as executable subagent code rather than textual experience. |
TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition (2025.acl-long)
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Tianwei Lin, Jiang Liu, Wenqiao Zhang, Yang Dai, Haoyuan Li, Zhelun Yu, Wanggui He, Juncheng Li, Jiannan Guo, Hao Jiang, Siliang Tang, Yueting Zhuang
| Challenge: | Existing methods for fine-tuning are resource-efficient, but performance often falls short . a new approach, TeamLoRA, integrates collaborative and competitive modules to improve performance. |
| Approach: | They propose to introduce task-specific LoRA as domain experts to improve learning efficiency . teamLoRA integrates collaborative and competition modules to improve model learning . |
| Outcome: | Experiments show that TeamLoRA improves performance in multi-task learning . teamLorea integrates collaborative and competitive modules to improve performance . |
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach (2021.emnlp-main)
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Simiao Zuo, Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Jianfeng Gao, Weizhu Chen, Tuo Zhao
| Challenge: | Existing approaches to adversarial regularization treat adversarials and defending players equally, which is undesirable because only the defending player contributes to the generalization performance. |
| Approach: | They propose a method which formulates adversarial regularization as a Stackelberg game and induces a competition between a leader and a follower. |
| Outcome: | The proposed method outperforms existing adversarial regularization baselines on a set of machine translation and natural language understanding tasks. |
ALCAP: Alignment-Augmented Music Captioner (2023.emnlp-main)
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| Challenge: | Traditional approaches to music captioning ignore the intricate interplay between the two . however, a comprehensive understanding of music necessitates the integration of both these elements. |
| Approach: | They propose a method to learn multimodal alignment between audio and lyrics through contrastive learning. |
| Outcome: | The proposed method achieves new state-of-the-art on two music captioning datasets. |
A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text (D19-1)
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| Challenge: | Variational Autoencoders are powerful language models and effective representation learning frameworks. |
| Approach: | They propose a fix for posterior collapse which improves held-out likelihood, reconstruction and latent representation learning . |
| Outcome: | The proposed fix significantly improves held-out likelihood, reconstruction, and latent representation learning compared with previous state-of-the-art methods. |
LEMMA: Learning from Errors for MatheMatical Advancement in LLMs (2025.findings-acl)
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Zhuoshi Pan, Yu Li, Honglin Lin, Qizhi Pei, Zinan Tang, Wei Wu, Chenlin Ming, H. Vicky Zhao, Conghui He, Lijun Wu
| Challenge: | Existing approaches focus on improving the quality of correct training data, neglecting the value contained in error data, thereby hindering the model’s reflective ability. |
| Approach: | They propose to improve LLM's reasoning ability by learning from error data and a grounded mistake augmentation method to collect representative errors. |
| Outcome: | The proposed model achieves significant performance improvements over other strong models with less than 90k data. |
DRAMA: Dynamic Multi-Granularity Graph Estimate Retrieval over Tabular and Textual Question Answering (2024.lrec-main)
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| Challenge: | TableTextQA tasks require tabular and textual data, gaining increasing attention . however, row-based approaches suffer from limitations such as lack of interaction between rows . |
| Approach: | They propose a method that incorporates an interaction mechanism among multiple rows . Empirical results demonstrate that the proposed method is effective . |
| Outcome: | Empirical results show that the proposed model is effective on tabFact and HybridQA datasets. |
TWEETSUM: Event oriented Social Summarization Dataset (2020.coling-main)
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| Challenge: | Developing social summarization systems is becoming more and more critical . but, the publicly available and high-quality large scale social summaries are rare . |
| Approach: | They propose to build a social summarization dataset using twitter's hot events . they collect user relations, hashtags and user profiles to evaluate their summarizing methods . |
| Outcome: | The proposed dataset is based on a dataset from twitter with 12 real world hot events with 44,034 tweets and 11,240 users. |
Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection (2022.coling-1)
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| Challenge: | Experimental results show that cross-language data expansion results in performance degradation. |
| Approach: | They leverage cross-language data expansion and retraining to enhance neural Event Detection on English ACE corpus. |
| Outcome: | The proposed method improves ED performance by 1.6% over the straight data combination. |
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding (2020.acl-demos)
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Xiaodong Liu, Yu Wang, Jianshu Ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon, Guihong Cao, Jianfeng Gao
| Challenge: | MT-DNN is an open-source natural language understanding toolkit . it allows researchers and developers to train customized deep learning models . |
| Approach: | They present MT-DNN, an open-source natural language understanding toolkit . it is designed to facilitate rapid customization for a broad spectrum of NLU tasks . MT supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. |
| Outcome: | The proposed model can significantly compress a large model without significant performance drop. |
Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection (2021.acl-long)
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| Challenge: | Emotion detection in dialogues requires the identification of thematic topics underlying a conversation, commonsense knowledge, and the intricate transition patterns between affective states. |
| Approach: | They propose a Topic-Driven Knowledge-Aware Transformer model that integrates topic representation and commonsense knowledge from ATOMIC for dialogue emotion detection. |
| Outcome: | The proposed model outperforms state-of-the-art models on four dialogue datasets . it can detect topics which help distinguish emotion categories, the authors show . |
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents (2024.acl-long)
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Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Zhong Zhang, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Current language model-driven agents lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. |
| Approach: | They propose a benchmark to inspect users’ implicit intentions through explicit queries and a model expert as the upstream in agent design to enhance user-agent interaction. |
| Outcome: | The proposed approach excels at identifying vague user tasks, recovering and summarizing critical missing information, setting precise and necessary agent execution goals, and minimizing redundant tool usage, thus boosting overall efficiency. |
PodAgent: A Comprehensive Framework for Podcast Generation (2025.findings-acl)
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| Challenge: | Existing automatic audio generation methods struggle to generate podcast-like audio programs effectively. |
| Approach: | They propose a framework for creating podcast-like audio programs that generates informative topic-discussion content by designing a multi-agent collaboration system, builds a voice pool and uses LLM-enhanced speech synthesis to generate expressive conversational speech. |
| Outcome: | The proposed framework surpasses direct GPT-4 generation in topic-discussion dialogue content, and produces more expressive conversational speech. |
Learning Adaptive Segmentation Policy for Simultaneous Translation (2020.emnlp-main)
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| Challenge: | Experimental results show that adaptive segmentation policies for simultaneous translation are more accurate than current methods . if translation starts before adequate source content is delivered, the quality of translation degrades . waiting for too much source text increases latency, which would hurt accuracy . |
| Approach: | They propose a new adaptive segmentation policy for simultaneous translation based on human interpreters . it learns to segment the source text by considering possible translations produced by the translation model . |
| Outcome: | Experimental results show that the proposed method achieves better accuracy-latency trade-off over state-of-the-art methods. |
MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation Alignment (2025.acl-long)
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| Challenge: | Existing personalized product search methods assume that users’ query fully captures their real motivation, but in practice, user's queries do not always articulate the requirements. |
| Approach: | They propose a Motivation-Aware Personalized Search method that embeds queries and consultations into a unified semantic space via LLMs and utilizes a Mixture of Attention Experts (MoAE) to prioritize critical semantics. |
| Outcome: | Extensive experiments on real and synthetic data show that the proposed method outperforms existing methods in retrieval and ranking tasks. |
Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation (2023.emnlp-main)
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| Challenge: | Modern NLP models are often trained over large untrustworthy datasets, raising the potential for a malicious adversary to compromise model behaviour. |
| Approach: | They propose to mitigate spurious correlations between textual triggers and classification labels by combining them with insertion-based attacks. |
| Outcome: | The proposed defence significantly reduces attack success rates across backdoor attacks and provides a near-perfect defence against insertion-based attacks. |
Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search (2026.acl-long)
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| Challenge: | Large Language Model (LLM) based multi-agent systems (MAS) have high potential for tackling complex tasks through collaborative intelligence. |
| Approach: | They propose a framework that incorporates influence scores to guide tree search and data selection in data synthesis. |
| Outcome: | The proposed framework incorporates influence scores to guide tree search and data selection in data synthesis. |
DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning (2025.findings-acl)
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| Challenge: | Existing multimodal sentence representation learning methods focus on aligning images and text at a coarse level, resulting in cross-modal misalignment bias and intra-modal semantic divergence. |
| Approach: | They propose a dual-level alignment learning framework for multimodal sentence representation learning that promotes cross-modal and intra-modal alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on semantic textual similarity and transfer tasks on semantic similarity, ranking distillation and global intra-modal alignment learning. |
UniArk: Improving Generalisation and Consistency for Factual Knowledge Extraction through Debiasing (2024.naacl-long)
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| Challenge: | Existing studies have investigated the potential of language models as knowledge bases and the existence of severe biases when extracting factual knowledge. |
| Approach: | They propose an adapter-based framework for generalised factual knowledge extraction using simple methods without introducing extra parameters. |
| Outcome: | The proposed framework improves the model’s out-of-domain generalisation and consistency under various prompts. |
GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigation (2025.findings-naacl)
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Runchuan Zhu, Xinke Jiang, Jiang Wu, Zhipeng Ma, Jiahe Song, Fengshuo Bai, Dahua Lin, Lijun Wu, Conghui He
| Challenge: | Experimental evaluations on open-ended and multiple-choice questions demonstrate GRAIT significantly outperforms existing RAIT methods in the overall performance. |
| Approach: | They propose a framework to reduce the risk of over-refusal and reduce hallucinations by rejecting unknown questions to minimize hallucinism and ensuring correct answers are not rejected. |
| Outcome: | The proposed framework outperforms existing methods on open-ended and multiple-choice questions. |
Exploring the Capacity of Pretrained Language Models for Reasoning about Actions and Change (2023.acl-long)
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| Challenge: | Recent transformer-based language models (LMs) provide reasoning over textual benchmarks . RAC is essential to understand and interact with the ever-changing environment . |
| Approach: | They propose to use a transformer-based language model to learn to reason over textual benchmarks. |
| Outcome: | The proposed model minimizes the influence of other linguistic requirements to focus on RAC. |
Exploring the Semantic Space of Second Language Learners (2026.eacl-srw)
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| Challenge: | Using machine learning models, we compared the semantic space of university-level students learning French with native speakers' (L1) . |
| Approach: | They extracted semantic features from narrative text and used interpretability techniques to identify the most informative features per model. |
| Outcome: | The results show that the second language learners had higher semantic similarity scores than the native speakers at the token level, whereas the similarity decreased over time but did not reach native-level values. |
C3LPGCN:Integrating Contrastive Learning and Cooperative Learning with Prompt into Graph Convolutional Network for Aspect-based Sentiment Analysis (2024.findings-naacl)
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| Challenge: | Recent studies have shown that graph convolutional networks (GCNs) can model syntactic information but incorrect syntaktic structure may introduce additional noise. |
| Approach: | They propose a graph convolutional network which integrates Contrastive Learning and Cooperative Learning with Prompt into GCN to alleviate the noise when modeling syntactic information. |
| Outcome: | The proposed model outperforms state-of-the-art models on three datasets and significantly outperformed existing models. |
GRAD: Generalizing RAG Adaptation with Decoding (2026.acl-long)
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| Challenge: | Using GRAD, we can steer Retrieval-augmented generation objectives without retraining large language models. |
| Approach: | They propose an adaptive decoding-time framework that keeps the base generator fixed and composes small, objective-specific guidance at inference. |
| Outcome: | The proposed framework improves accuracy with favorable latency across public benchmarks and private settings with no in-domain labels while reliably activating helpful objectives and suppressing harmful ones, adaptively to tasks. |
Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning (2022.coling-1)
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| Challenge: | Existing approaches to cross-domain text classification focus on one-to-one domain adaptation. |
| Approach: | They propose a framework for domain generalization that uses contrastive learning with a memory-saving queue. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on Amazon review sentiment datasets and rumour detection datasets. |
CPL: Counterfactual Prompt Learning for Vision and Language Models (2022.emnlp-main)
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Xuehai He, Diji Yang, Weixi Feng, Tsu-Jui Fu, Arjun Akula, Varun Jampani, Pradyumna Narayana, Sugato Basu, William Yang Wang, Xin Wang
| Challenge: | Existing prompt tuning methods tend to learn spurious or entangled representations, leading to poor generalization to unseen concepts. |
| Approach: | They propose a prompt tuning technique that tunes the learnable prompt for pre-trained vision and language models. |
| Outcome: | The proposed method improves few-shot performance on vision and language tasks over existing prompt tuning methods. |
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue (2023.acl-long)
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| Challenge: | Existing pre-trained language models rely on a contrastive framework and are difficult to use in practice. |
| Approach: | They propose a dialogue pre-training model which distills future knowledge to the representation of the previous dialogue context using a self-training framework. |
| Outcome: | The proposed model can be applied to various downstream dialogue tasks. |
Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention (D19-1)
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| Challenge: | Existing approaches to detect suicidal ideation on social media are limited to a small group of people. |
| Approach: | They propose to use tree holes to embed words into microblogs to strengthen the sensibility of suicide-related lexicons and to use a two-layered attention mechanism to grasp intermittently changing points from individual's open blog streams. |
| Outcome: | The proposed approach can achieve over 91% accuracy with the use of suicide-oriented word embeddings and attention on a large-scale well-labelled suicide data set. |
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation (P19-3)
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Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Sachan, Eric Xing
| Challenge: | Texar is an open-source text generation toolkit that supports a broad set of text generation tasks. |
| Approach: | They introduce Texar, an open-source text generation toolkit that supports text generation tasks. |
| Outcome: | Texar supports machine translation, summarization, dialog, content manipulation, and more. |
Let’s Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought (2023.emnlp-main)
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Vaishnavi Himakunthala, Andy Ouyang, Daniel Rose, Ryan He, Alex Mei, Yujie Lu, Chinmay Sonar, Michael Saxon, William Wang
| Challenge: | Existing studies show vision-language systems can reason about images using natural language, but their capacity for video reasoning remains underexplored. |
| Approach: | They propose to frame video reasoning as the sequential understanding of a small number of keyframes, thereby leveraging the power and robustness of vision-language systems' capacity to reason about images using natural language. |
| Outcome: | The proposed models can generate multiple intermediate keyframes and predict future keyframe, and they perform poorly on GPT-4, GPT-3, and VICUNA. |
NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning (2025.emnlp-main)
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| Challenge: | Recent advances in reinforcement learning, such as DeepSeek R1-Zero, highlight the effectiveness of incentive training, but these methods rely on external verifiers, which limits their applicability to domains like mathematics and coding, where such verifier is readily available. |
| Approach: | They propose a general reinforcement learning framework that requires only standard supervised fine-tuning data with no need for an external verifier. |
| Outcome: | The proposed framework outperforms the model of the same size distilled from large reasoning models such as DeepSeek R1 671B by 7.7%. |
Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning (2022.acl-long)
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| Challenge: | Recent studies show that tabular reasoning models use spurious correlations and focus on false evidence or ignore it altogether. |
| Approach: | They propose a task where models need to extract evidence and then inference labels . they crowdsource evidence row labels and develop unsupervised evidence extraction strategies . |
| Outcome: | The proposed approach outperforms baseline models on the inference task using only the automatically extracted evidence as the premise. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
Turning Failures into Value: Negative Experience Replay for RLVR via Confidence Gating and Boundary Failure Sampling (2026.acl-long)
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| Challenge: | Existing experience replay methods for RLVR ignore sample inefficiency . expensive reasoning trajectories are discarded immediately after a single gradient update . |
| Approach: | They propose a method to replay failure trajectories to improve model refinement . they propose 'nexGRPO' which employs mid-confidence gating to filter invalid noise and saturated errors. |
| Outcome: | The proposed model outperforms strong baaselines and achieves improved out-of-distribution generalization. |
Robustness via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction (2026.findings-acl)
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Yulin Chen, Haoran Li, Yuan Sui, Yue Liu, Yufei He, Xiaoling Bai, Chi Fei, Li Yabo, Haozhe Ma, Yangqiu Song, Bryan Hooi
| Challenge: | Prompt injection attacks manipulate large language models (LLMs) by misleading them to deviate from the original input instructions and execute maliciously injected instructions. |
| Approach: | They propose a prompt injection defense method that suppresses the model's instruction-following tendencies rather than suppressing them. |
| Outcome: | The proposed method outperforms prompt-engineering-based approaches and fine-tuning methods and reduces the ASR to nearly 0% in some scenarios. |
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation (2025.acl-long)
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Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, Yutong Xie, Imran Razzak, Zongyuan Ge, Jionglong Su, Junjun He, Yu Qiao
| Challenge: | Existing multimodal large language models lack the ability to memorize, recall, and reason in sustained interactions. |
| Approach: | They propose a multimodal real-world conversation benchmark for evaluating open-ended abilities of multimodal large language models. |
| Outcome: | The proposed benchmarks show that the models perform better in open-ended conversations. |
Representation Degeneration Problem in Prompt-based Models for Natural Language Understanding (2024.lrec-main)
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| Challenge: | Prompt-based fine-tuning (PF) models have shown improved performance on few-shot natural language understanding benchmarks. |
| Approach: | They propose a framework to alleviate anisotropy in the embedding space by aligning with pre-trained language models' training objective. |
| Outcome: | The proposed method outperforms mainstream methods on many NLU benchmarks. |
SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition (2025.acl-long)
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Shuangrui Ding, Zihan Liu, Xiaoyi Dong, Pan Zhang, Rui Qian, Junhao Huang, Conghui He, Dahua Lin, Jiaqi Wang
| Challenge: | Creating lyrics and melodies in symbolic format requires expert knowledge of melody and an advanced understanding of lyrics. |
| Approach: | They introduce SongComposer, a music-specialized large language model that can create symbolic lyrics and melodies following instructions. |
| Outcome: | The proposed model outperforms existing models in symbolic song composition tasks. |
BrailleLLM: Braille Instruction Tuning with Large Language Models for Braille Domain Tasks (2025.emnlp-main)
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Tianyuan Huang, Zepeng Zhu, Hangdi Xing, Zirui Shao, Zhi Yu, Chaoxiong Yang, Jiaxian He, Xiaozhong Liu, Jiajun Bu
| Challenge: | Existing Braille research focuses on isolated tasks while mixed-content Braille tasks face data scarcity and ambiguities. |
| Approach: | They propose a syntax tree-based augmentation method tailored for Braille data. |
| Outcome: | The proposed method improves Braille translation, formula-to-Braille conversion, and mixed-text translation. |
PRBench: Large-Scale Expert Rubrics for Evaluating High-Stakes Professional Reasoning (2026.acl-long)
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Afra Feyza Akyürek, Advait Gosai, Chen Bo Calvin Zhang, Vipul Gupta, Jaehwan Jeong, Anisha Gunjal, Tahseen Rabbani, Maria Mazzone, David Randolph IV, Mohammad Mahmoudi Meymand, Gurshaan Chattha, Paula Rodriguez, Diego A. Mares Buendia, Pavit Singh, Michael Liu, Subodh Chawla, Peter Cline, Lucy Ogaz, Ernesto Gabriel Hernández Montoya, Zihao Wang, Pavi Bhatter, Marcos Ayestaran, Bing Liu, Yunzhong He
| Challenge: | Frontier models often lack a view of performance on open-ended, economically consequential tasks in high-stakes professional domains where practical returns matter most. |
| Approach: | They introduce a professional reasoning benchmark that recruits 182 qualified professionals to contribute questions inspired by their workflows. |
| Outcome: | The proposed model outperforms other models in 114 countries and 47 US jurisdictions on hard subsets. |
Efficient Shapley Values Estimation by Amortization for Text Classification (2023.acl-long)
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| Challenge: | Shapley Values are often estimated with a small number of stochastic model evaluations, but this can only be mitigated by aggregating thousands of model evaluation. |
| Approach: | They propose to combine a model with thousands of model evaluations to estimate Shapley Values without additional model evaluation. |
| Outcome: | The proposed model estimates Shapley Values accurately with up to 60 times speedup compared to traditional methods and does not suffer from stability issues as inference is deterministic. |
SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs (2026.acl-long)
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| Challenge: | Existing educational LLMs are vulnerable to pedagogical jailbreaks where students use answer-inducing prompts to elicit solutions rather than scaffolded instructions. |
| Approach: | They propose a graph-augmented tutoring pipeline that infers prerequisite concepts from queries and identifies mastery gaps. |
| Outcome: | The proposed method improves safety under two pedagogical jailbreak scenarios while maintaining near-ceiling helpfulness under the same evaluation protocol. |
Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models Using Minimal Pairs (2024.lrec-main)
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| Challenge: | a new method is being developed to probe internal linguistic characteristics in neural language models layer by layer . |
| Approach: | They propose a method that uses minimal pairs benchmark to probe internal linguistic characteristics in neural language models layer by layer. |
| Outcome: | The proposed method captures grammaticality labels in language models layer by layer . it is based on the cognitive neurosciences of the brain and its representations as "neural activations". |
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification (2024.naacl-long)
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| Challenge: | Existing self-supervised methods in natural language processing rely on augmentation rules to generate contrastive samples. |
| Approach: | They propose a hierarchy-aware information lossless contrastive learning scheme that uses syntactic information reserved in the input sample and fused during the learning process. |
| Outcome: | The proposed learning scheme is superior to existing methods in hierarchical text classification . the proposed learning system is based on a structure encoder and a text encoder . |
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation (D19-1)
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| Challenge: | Currently, Chinese characters share glyph and phonetic variations to escape detection algorithms due to their complexity and complexity. |
| Approach: | They propose a Chinese variation-enhanced Graph Embedding algorithm that can learn Chinese character embeddings and latent variation families. |
| Outcome: | The proposed model outperforms state-of-the-art models on Chinese spam detection datasets and review datasets. |
Neural Attention-Aware Hierarchical Topic Model (2021.emnlp-main)
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| Challenge: | Neural topic models (NTMs) use deep neural networks to learn topic information. |
| Approach: | They propose a variational autoencoder model that reconstructs sentence and document word counts using bag-of-words embeddings and pre-trained semantic embedders. |
| Outcome: | The proposed model lowers reconstruction errors at sentence and document levels and finds more coherent topics from real-world datasets. |
TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition (2020.acl-main)
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| Challenge: | Existing approaches to implicit discourse relation recognition lack connectives as strong linguistic clues. |
| Approach: | They propose a transS-driven joint learning architecture to translate discourse relations in low-dimensional embedding space and exploit the semantic features of arguments to assist discourse understanding. |
| Outcome: | The proposed model outperforms existing systems on the Penn Discourse TreeBank. |
Worse than Random? An Embarrassingly Simple Probing Evaluation of Large Multimodal Models in Medical VQA (2025.findings-acl)
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| Challenge: | Large Multimodal Models (LMMs) have demonstrated impressive performance on existing medical visual question answering benchmarks. |
| Approach: | They evaluate large multimodal models that perform worse than random guessing on medical questions . authors suggest more robust evaluation methods to ensure reliability of LMMs . |
| Outcome: | a new study shows that large multimodal models perform worse than random guessing on medical visual question answering benchmarks. |
E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service (2022.lrec-1)
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Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang Song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, Xiaodong He
| Challenge: | Recent research has focused on developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations. |
| Approach: | They construct an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders. |
| Outcome: | The proposed dataset contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders. |
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention (C18-1)
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| Challenge: | Existing topic models ignore that one discusses diverse topics when dynamically interacting with different people. |
| Approach: | They propose an Interaction-Aware Topic Model (IATM) for microblog conversations by integrating network embedding and user attention. |
| Outcome: | The proposed model is based on three real-world microblog datasets. |
TMFN: A Target-oriented Multi-grained Fusion Network for End-to-end Aspect-based Multimodal Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing methods for multimodal aspect-based sentiment analysis focus on fusing image regional information and textual words. |
| Approach: | They propose a multimodal aspect-based sentiment analysis method that integrates regional and global image information with global image data. |
| Outcome: | Experiments show that the proposed method outperforms state-of-the-art methods on two benchmark datasets. |
Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation (2021.naacl-main)
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| Challenge: | Existing non-autoregressive machine translation models have shown significant inference speedup but suffer from inferior translation accuracy. |
| Approach: | They propose to use AT as an auxiliary task to transfer AT knowledge to NAT models by knowledge distillation. |
| Outcome: | The proposed method achieves significant improvements over baseline non-Autoregressive machine translation models on WMT14 En-De and WMT16 En-Ro datasets. |
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness (2020.findings-emnlp)
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| Challenge: | Existing approaches to learn text representations can encode private information of the input, thus can be exploited to recover such information with reasonable accuracy. |
| Approach: | They propose a novel approach to preserve privacy of the extracted representation from text by combining differential privacy with dropout. |
| Outcome: | The proposed approach preserves privacy of the extracted representation from text while masking words via dropout can enhance privacy. |
DrugWatch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information (2024.acl-demos)
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| Challenge: | Drug safety research is crucial for maintaining public health, but resources available to the public are limited. |
| Approach: | They propose an easy-to-use and interactive multi-source information visualisation platform for drug safety study. |
| Outcome: | The proposed platform provides a one-stop information analysis, retrieval, and annotation service. |
Using Natural Language Explanations to Improve Robustness of In-context Learning (2024.acl-long)
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| Challenge: | Recent studies show that large language models excel in many tasks via in-context learning (ICL). However, ICL struggles to execute complex tasks such as arithmetic, commonsense, and symbolic reasoning. |
| Approach: | They propose to augment ICL with natural language explanations (NLEs) to produce further NLEs on adversarial datasets. |
| Outcome: | The proposed approach yields more accurate results than zero-shot-ICL and using only human-generated NLEs on eight adversarial datasets. |
InstructCoder: Instruction Tuning Large Language Models for Code Editing (2024.acl-srw)
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| Challenge: | InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing. |
| Approach: | They propose to use Large Language Models to edit code based on user instructions . they use a dataset to adapt LLMs to general-purpose code editing . |
| Outcome: | The proposed model can significantly improve code editing performance compared to proprietary models . the proposed model is based on a human-written execution-based benchmark . |
DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models (2025.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have significantly enhanced their knowledge and generative capabilities, leading to a surge of interest in leveraging LLMs for high-quality data synthesis. |
| Approach: | They propose a controllable data synthesis framework based on variational autoencoder which leverages diffusion models to reserve more information of original distribution and format structure in the learned latent distribution. |
| Outcome: | The proposed framework generates high-quality data with performance exceeding that of real data by 2%–7% on seven real-world datasets. |
The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis (2024.emnlp-main)
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| Challenge: | In-context learning (ICL) is a capability that enables large language models to excel in proficiency through demonstration examples. |
| Approach: | They present a survey on the interpretation and analysis of in-context learning . they focus on theoretical and empirical perspectives on the concept . |
| Outcome: | The proposed model can perform tasks with minimal examples without re-training and has demonstrated proficiency across various tasks with a minimal set of task-oriented examples. |
Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models (2024.findings-emnlp)
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| Challenge: | Prompt-based learning is susceptible to intrinsic bias present in pre-trained language models (LMs), leading to sub-optimal performance in prompt-based zero/few-shot settings. |
| Approach: | They propose a null-input prompting method to calibrate intrinsic bias encoded in pre-trained language models (LMs) they leverage a diverse set of auto-selected null meaning inputs generated from GPT-4 to probe intrinsic bias. |
| Outcome: | The proposed method significantly improves zero/few-shot learning performance of LMs for both in-context learning and prompt-based fine-tuning (on average 9% and 2%, respectively). |
ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training (2024.acl-long)
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Le Zhuo, Zewen Chi, Minghao Xu, Heyan Huang, Jianan Zhao, Heqi Zheng, Conghui He, Xian-Ling Mao, Wentao Zhang
| Challenge: | Experimental results demonstrate that ProtLLM achieves superior performance against protein-specialized baselines on protein-centric tasks and induces zero-shot and in-context learning capabilities on protein language tasks. |
| Approach: | They propose a cross-modal large language model (LLM) that can handle protein-centric and protein-language tasks by using a dynamic protein mounting mechanism. |
| Outcome: | The proposed model can predict proteins from a vast pool of candidates and can also predict natural language and biological papers. |
From Pseudo-Balancing to True Specialization: Memory-Aware Routing for Mixture-of-Experts (2026.findings-acl)
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| Challenge: | Existing methods to optimize expert-centered load balancing fail to account for pseudo-balance phenomenon . severe knowledge overlap among experts leads to redundant representations and inefficient parameter utilization . |
| Approach: | They propose a method that prioritizes expert utilization over semantic alignment . they use memory-aware routing to ensure expert load balancing is consistent . |
| Outcome: | Experimental results show that MAR improves expert specialization by 35% and accuracy by 2%-25% . MAR matches baseline performance with only half the experts . |
ChunQiuTR: Time-Keyed Temporal Retrieval in Classical Chinese Annals (2026.findings-acl)
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| Challenge: | Historical research often focuses on finding exact record for a specific regnal month . classical Chinese sources are a canonical example of evidence-centric retrieval . |
| Approach: | They propose a time-keyed retrieval benchmark that organizes records by month-level reign keys . they propose 'CTD', a dual-encoder that combines absolute context with offset biasing . |
| Outcome: | The proposed benchmark organizes records by month-level reign keys and includes chrono-near confounders that mimic real retrieval failures. |
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling (2021.emnlp-main)
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| Challenge: | Existing methods for zero-shot cross-domain slot filling do not achieve effective knowledge transfer to the target domain. |
| Approach: | They propose a novel approach based on prototypical contrastive learning and a dynamic label confusion strategy for zero-shot slot filling. |
| Outcome: | The proposed model improves on unseen slots while setting new state-of-the-arts on slot filling task. |
Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification (2021.acl-long)
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| Challenge: | Existing methods for fact verification focus on analyzing semantic interaction between claim and evidence but fail to capture their topical consistency . Existing models focus on the aggregation of multiple pieces of evidence without considering their implicit stances to the claim, thereby introducing spurious information. |
| Approach: | They propose a topic-aware evidence reasoning and stance-again aggregation model that checks topical consistency between claims and evidence. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |
Stability Implies Redundancy: Delta Attention Selective Halting for Efficient Long-Context Prefilling (2026.acl-long)
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| Challenge: | Existing methods to reduce sequence length rely on heuristics that break compatibility with hardware-efficient kernels like FlashAttention. |
| Approach: | They propose a method that selectively halts stabilized tokens by monitoring layer-wise update dynamics of the self-attention mechanism. |
| Outcome: | The proposed method can reduce prefill complexity while preserving model accuracy and hardware efficiency. |
Wav2Gloss: Generating Interlinear Glossed Text from Speech (2024.acl-long)
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Taiqi He, Kwanghee Choi, Lindia Tjuatja, Nathaniel Robinson, Jiatong Shi, Shinji Watanabe, Graham Neubig, David Mortensen, Lori Levin
| Challenge: | Interlinear Glossed Text (IGT) is a form of linguistic annotation that can support documentation and resource creation for endangered languages. |
| Approach: | They propose a task in which these four annotation components are extracted automatically from speech and introduce a dataset to lay the groundwork for future research on IGT generation from speech. |
| Outcome: | The proposed dataset provides the first dataset to lay the groundwork for future research on IGT generation from speech, including end-to-end versus cascaded, monolingual versus multilingual, and single-task versus multiple-task approaches. |
Extracted BERT Model Leaks More Information than You Think! (2022.emnlp-main)
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| Challenge: | Existing pre-trained language models are vulnerable to model extraction attacks . model extraction can cause severe privacy leakage even when victim models are facilitated with state-of-the-art defensive strategies. |
| Approach: | They propose to launch an attribute-inference attack against an extracted BERT model to prevent privacy leakage. |
| Outcome: | The proposed attack can cause severe privacy leakage even when victim models are facilitated with state-of-the-art defensive strategies. |
Sparse Activation Editing for Reliable Instruction Following in Narratives (2025.emnlp-main)
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| Challenge: | Existing benchmarks fail to capture the challenges of instruction following in complex narrative contexts. |
| Approach: | They propose a training-free framework that identifies and edits instruction-relevant neurons using only natural language instructions without requiring labelled data. |
| Outcome: | The proposed framework improves instruction following by identifying and editing instruction-relevant neurons using only natural language instructions, without requiring labelled data. |
Here’s a Free Lunch: Sanitizing Backdoored Models with Model Merge (2024.findings-acl)
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| Challenge: | democratization of pre-trained language models brings significant security risks, including backdoor attacks. |
| Approach: | They propose to merge a backdoored model with other homogeneous models to remediate backdoor vulnerabilities. |
| Outcome: | The proposed model merging approach outperforms other models on classification tasks without additional resources or specific knowledge. |
Schema Generation for Large Knowledge Graphs Using Large Language Models (2025.findings-emnlp)
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| Challenge: | Schemas are a vital part of ontology engineering and require substantial knowledge engineers and domain experts to create them. |
| Approach: | They propose to use large language models to generate schemas in Shape Expressions (ShEx) to bridge the resource gap between knowledge engineers and domain experts. |
| Outcome: | The proposed pipelines use local and global information from knowledge graphs (KGs) to generate high-quality schemas in Shape Expressions (ShEx). |
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck (2022.coling-1)
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| Challenge: | Event argument extraction (EAE) aims to extract arguments with given roles from texts. |
| Approach: | They propose a multi-format transfer learning model with variational information bottleneck to learn from existing datasets. |
| Outcome: | The proposed model improves on three benchmark datasets and obtains state-of-the-art performance on EAE. |
Diagnosing Memorization in Chain-of-Thought Reasoning, One Token at a Time (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) perform well on reasoning benchmarks but often fail when inputs alter slightly, raising concerns about overreliance on memorization. |
| Approach: | They propose a framework for Source-aware Token-level Identification of Memorization which attributes each token in a reasoning chain to one of multiple memorization sources based on their statistical co-occurrence with the token in the pretraining corpus. |
| Outcome: | The proposed framework attributes each token in a reasoning chain to one of multiple memorization sources based on their statistical co-occurrence with the token in the pretraining corpus. |
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving (2025.acl-long)
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Teng Wang, Wing Yin Yu, Zhenqi He, Zehua Liu, HaileiGong HaileiGong, Han Wu, Xiongwei Han, Wei Shi, Ruifeng She, Fangzhou Zhu, Tao Zhong
| Challenge: | Existing datasets in operations research domain lack detailed annotations of the modeling process, focusing only on objective values. |
| Approach: | They propose an annotation-based tree-of-thought tree-based reasoning algorithm that integrates reinforcement learning into a tree- of-though. |
| Outcome: | The proposed algorithm outperforms state-of-the-art methods on StructuredOR, NL4OPT, and MAMO-ComplexLP datasets. |
JDDC 2.1: A Multimodal Chinese Dialogue Dataset with Joint Tasks of Query Rewriting, Response Generation, Discourse Parsing, and Summarization (2022.emnlp-main)
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| Challenge: | e-commerce users express their needs using text, images, or videos . but detailed information provided by images is limited, and customer service systems cannot understand the intent of users without the input text. |
| Approach: | They construct a large-scale multimodal multi-turn dialogue dataset from a mainstream Chinese E-commerce platform . the dataset contains about 246K dialogue sessions, 3M utterances, and 507K images . |
| Outcome: | The proposed dataset contains 246K dialogue sessions, 3M utterances, 507K images . it also includes product knowledge bases and image category annotations . |
Zero-shot Cross-lingual Automated Essay Scoring (2024.lrec-main)
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| Challenge: | Existing approaches to automate essay scoring (AES) use pre-trained multilingual representations and writing quality alignment to score essays in unseen languages. |
| Approach: | They propose a novel cross-lingual scoring method using pretrained multilingual representation and writing quality alignment to represent multilingual essays. |
| Outcome: | The proposed method achieves state-of-the-art cross-lingual scoring performance. |
ChatEdit: Towards Multi-turn Interactive Facial Image Editing via Dialogue (2023.emnlp-main)
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| Challenge: | Existing approaches to interactive facial image editing treat multi-turn editing as a sequence of successive single-turn edits, leading to attribute forgetting and error accumulation. |
| Approach: | They propose a framework for interactive facial image editing through dialogues based on the CelebA-HQ dataset and a benchmark dataset to evaluate this. |
| Outcome: | The proposed framework outperforms existing methods and improves existing ones. |
AgentV-RL: Scaling Reward Modeling with Agentic Verifier (2026.findings-acl)
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Jiazheng Zhang, Ziche Fu, Zhiheng Xi, Wenqing Jing, Mingxu Chai, Wei He, Guoqiang Zhang, Chenghao Fan, Chenxin An, Wenxiang Chen, Zhicheng Liu, Haojie Pan, Dingwei Zhu, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to improve LLM reasoning are limited in complex domains and lack external grounding makes verifiers unreliable on computation-intensive tasks. |
| Approach: | They propose a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process. |
| Outcome: | The proposed framework surpasses state-of-the-art ORMs by 25.2% under parallel and sequential TTS. |
Extracting Event Temporal Relations via Hyperbolic Geometry (2021.emnlp-main)
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| Challenge: | Recent neural approaches to event temporal relation extraction map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. |
| Approach: | They propose to embed events into hyperbolic spaces to model hierarchical structures . they propose to use hyperbolical embeddings to directly infer event relations . |
| Outcome: | The proposed architecture is based on two approaches to encode events and their temporal relations in hyperbolic spaces. |
Bi-Chainer: Automated Large Language Models Reasoning with Bidirectional Chaining (2024.findings-acl)
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| Challenge: | Existing unidirectional chaining methods suffer from low prediction accuracy and efficiency. |
| Approach: | They propose a bidirectional chaining method which dynamically switches to depth-first reasoning in the opposite reasoning direction when it encounters multiple branching options within the current direction. |
| Outcome: | The proposed method achieves sizable accuracy boots over unidirectional chaining frameworks on four challenging logical reasoning datasets. |
Mitigating Shortcuts in Language Models with Soft Label Encoding (2024.lrec-main)
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| Challenge: | Recent studies have shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks. |
| Approach: | They propose a framework for debiasing shortcuts and a dummy class to encode shortcuts into a model and use it to generate soft labels. |
| Outcome: | The proposed framework significantly improves out-of-distribution generalization while maintaining satisfactory in-district accuracy. |
Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural Language Understanding (2025.coling-main)
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| Challenge: | Current approaches for fine-tuning datasets rely on expensive sample ranking processes . data set pruning aims to select a subset of a dataset for efficient model training . |
| Approach: | They propose a method that uses TF-IDF embeddings with geometric median to rapidly evaluate sample importance. |
| Outcome: | The proposed method significantly reduces training and storage costs while maintaining model effectiveness. |
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs (2022.coling-1)
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| Challenge: | Existing MLaaS models are vulnerable to imitation attacks, but none of the stolen models can outperform the original black-box APIs. |
| Approach: | They conduct unsupervised domain adaptation and multi-victim ensemble to show attackers could surpass victims. |
| Outcome: | The proposed model outperforms the original black-box models on transferred domains. |
Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation (2022.findings-acl)
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| Challenge: | Document-level relation extraction (DocRE) is a more challenging task than sentence-level one. |
| Approach: | They propose a semi-supervised framework for document-level relation extraction with three components . they use an axial attention module for learning the interdependency among entity-pairs . |
| Outcome: | The proposed model outperforms baseline models on two DocRE datasets and outperformed previous models on human annotated data and distantly supervised data. |
Federated LoRA Fine-Tuning with Pipelined Error-Mitigated Aggregation and Matrix-Wise Freezing (2026.findings-acl)
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| Challenge: | Existing methods for fine-tuning large language models often suffer from biased model aggregation and are hindered by significant communication and computation burden. |
| Approach: | They propose a Federated low-rank adaptation system for large language models that leverages pipelined error-mitigated model aggregation and adaptive matrix-wise parameter freezing to mitigate aggregations. |
| Outcome: | The proposed system improves time-to-target by 2.17-8.48 on real-world datasets. |
SCVQ: Sparse-Compensated Vector Quantization for Large Language Models (2026.acl-long)
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| Challenge: | Existing vector quantization methods incur inference overhead due to massive codebook storage and intensive index lookups. |
| Approach: | They propose a framework for vector quantization that incorporates a salience-aware weighted K-means clustering scheme with symmetry constraints to reduce codebook size and indexing costs. |
| Outcome: | The proposed framework achieves a perplexity of 5.78 on WikiText-2 for LLaMA-2-7B at 2-bit quantization while delivering a 1.4 speedup over existing baselines. |
Heterogeneous Adaptive Policy Optimization: Tailoring Optimization to Every Token’s Nature (2026.acl-long)
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| Challenge: | Existing methods that use entropy as a discrete filter or post-hoc regulator are limited in their ability to optimize for reasoning tasks. |
| Approach: | They propose a token-aware algorithm that continuously adapts optimization dynamics based on token-level entropy throughout the entire training process. |
| Outcome: | Extensive experiments on mathematical reasoning, code, and logic tasks across multiple models demonstrate HAPO’s consistent superiority over DAPO. |
Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models (2026.acl-long)
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Runxuan Liu, Xianhao Ou, Xinyan Ma, Jiyuan Wang, Jiafeng Liang, Jiaqi Li, Tao He, Zheng Chu, Rongchuan Mu, Zekun Wang, Baoxin Wang, Dayong Wu, Ming Liu, Shijin Wang, Guoping Hu, Bing Qin
| Challenge: | Existing methods for long chain-of-thought (LCoT) are coarse-grained, reward hacking, and poor generalization. |
| Approach: | They propose a Long Chain-of-Thought (LCoT) model that integrates reinforcement learning with verifiable rewards with a process-aware verification approach. |
| Outcome: | The proposed model improves reasoning and code generation tasks while reducing the cost of training and performance bottlenecks. |
Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation (2020.acl-main)
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| Challenge: | Empirical results on machine translation suggest that DPE is effective for segmenting output sentences. |
| Approach: | They propose a new algorithm for tokenizing sentences into subword units . they propose enabling exact log marginal likelihood estimation and exact MAP inference . |
| Outcome: | The proposed algorithm improves on machine translation datasets and on a large dataset. |
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks (2024.acl-long)
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| Challenge: | Existing studies on this topic focus on the robustness of specific detectors or particular attack methods. |
| Approach: | They stress test the detectors’ robustness to malicious attacks under realistic scenarios using LLMs and metric-based detectors. |
| Outcome: | The proposed methods are based on a set of LLM-based models and their performance is compared under different budget levels. |
Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation (2023.acl-long)
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| Challenge: | Existing controllable dialogue generation models focus on single attribute and lack generalization capability to out-of-distribution multiple attribute combinations. |
| Approach: | They propose a compositional generalization model that learns from seen attributes and generalizes to unseen combinations. |
| Outcome: | The proposed model can learn from seen attribute values and generalize to unseen combinations. |
Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews (2021.eacl-main)
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| Challenge: | Existing models for sentiment-topic extraction assume topics are grouped under discrete sentiment categories such as ‘positive’, ‘negative’ and ‘neural’. |
| Approach: | They propose a Brand-Topic Model which aims to detect brand-associated polarity-bearing topics from product reviews. |
| Outcome: | The proposed model outperforms existing models on Amazon reviews and shows that it is more coherent and unique than existing models. |
CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling (2022.coling-1)
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| Challenge: | Existing methods to determine a goal item by sequentially tracking users’ interests ignore the rich goal-aware implicit interest sequence patterns in a dialog. |
| Approach: | They propose to model goal-aware implicit user interest sequence patterns in a dialog and a hierarchical Star Transformer to guide multi-turn utterances generation. |
| Outcome: | The proposed framework achieves more accurate recommendations with more fluent and coherent dialog utterances. |
SetGNER: General Named Entity Recognition as Entity Set Generation (2022.emnlp-main)
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| Challenge: | Named entity recognition (NER) is a fundamental task in the field of information extraction and has played an important role in the development of natural language processing. |
| Approach: | They propose a method that treats each entity as a sequence and is capable of recognizing discontinuous mentions. |
| Outcome: | The proposed model outperforms state-of-the-art generative NER models on two discontinuous NER datasets, two nested NER and one flat NER. |
Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training (2020.emnlp-main)
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| Challenge: | Existing approaches to task-oriented dialogue systems require a large number of handcrafted features and labels. |
| Approach: | They propose a "Two-Teacher One-Student" learning framework for task-oriented dialogue . the framework amalgamates knowledge from two teacher networks and provides guidance . |
| Outcome: | The proposed framework outperforms baseline methods on two benchmark datasets . it can retrieve accurate KB entities and generate human-like responses simultaneously . |
A general framework for information extraction using dynamic span graphs (N19-1)
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| Challenge: | Existing frameworks for information extraction use a pipeline approach to identify entities and then use the detected entity spans for relation extraction and coreference resolution. |
| Approach: | They propose a framework for several information extraction tasks that share span representations using dynamically constructed span graphs. |
| Outcome: | The proposed framework significantly outperforms state-of-the-art on multiple information extraction tasks across multiple datasets reflecting different domains. |
Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation (2024.findings-emnlp)
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| Challenge: | Recent Retrieval Augmented Generation (RAG) aims to enhance Large Language Models . however, such approach can generate inconsistent answer with external references . |
| Approach: | They propose to integrate the verification module into the RAG to improve external retrieval correctness and internal generation consistency. |
| Outcome: | The proposed model can significantly surpass the state-of-the-art baselines using different LLM backbones. |
ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis (2025.findings-naacl)
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| Challenge: | Existing methods for enhancing large language models lack clear metrics for evaluating data characteristics. |
| Approach: | They propose a method that integrates models, data, and tasks to refine datasets. |
| Outcome: | The proposed method achieves comparable results to full-scale fine-tuning using only half the data in mathematical tasks and exhibits strong generalization across different models and domains. |
ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5 (2025.acl-long)
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Jiaming Zhou, Shiyao Wang, Shiwan Zhao, Jiabei He, Haoqin Sun, Hui Wang, Cheng Liu, Aobo Kong, Yujie Guo, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin
| Challenge: | Automatic speech recognition systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0. |
| Approach: | They propose to use Mandarin speech datasets to analyze pronunciation and tone of children aged 3 to 5 and evaluate their models on speaker verification (SV) They find that the datasets are more robust than those used by adult speech recognition systems and are open-source and available for all academic purposes. |
| Outcome: | The proposed dataset includes 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation. |
Exploring Content Predictability in Turn-Taking Through Different Computer-Mediated Communications (2025.coling-main)
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Wanqing He, Calen C. MacDonald, Yejoon Yoo, Marcos Eizayaga, Ryun Shim, Lev D. Katreczko, Susan R. Fussell
| Challenge: | Existing studies on face-to-face (f2f) communication have suggested that speakers rely heavily on a variety of multi-modal cues to make real-time predictions about upcoming words. |
| Approach: | They assessed how loss of multi-modal cues would affect word predictability in turn-taking by watching videos, listening to audio, or reading a transcript of f2f conversations. |
| Outcome: | The results confirm the key role of prediction in language processing and conversation smoothness, especially in computer-mediated conversation (CMC). |
A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature (D19-1)
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| Challenge: | Existing academic search engines cannot detect relevant papers where a resource is mentioned. |
| Approach: | They propose a framework to model the role and function of on-line resource citations . they construct a dataset SciRes, which includes 3,088 manually annotated resource contexts based on a multi-task framework . |
| Outcome: | The proposed model achieves the best results on both the classification task and recommendation task. |
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack (2020.coling-main)
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| Challenge: | Existing approaches to zero-shot slot filling ignore constraints in the latent space and lack robustness. |
| Approach: | They propose a Contrastive Zero-Shot Learning with Adversarial Attack method for slot filling . they propose to map slot value contextual representations to slot description representations . |
| Outcome: | The proposed method outperforms state-of-the-art models under zero-shot and few-shot settings. |
Harmonizing the Past, Present, and Future: A Null-Space Constrained Region-Specific Method for Continual Learning in LLMs (2026.acl-long)
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Jinhui Chen, Shizhu He, Xingchang Yang, Huanxuan Liao, Yequan Wang, Xiangwen Liao, Wenhao Teng, Kang Liu, Jun Zhao
| Challenge: | Existing continual learning paradigms prioritize instant performance through dense updates, leading to catastrophic forgetting and rapid exhaustion of model capacity. |
| Approach: | They propose a method that preserves previously acquired knowledge and acquires new task-specific skills while preserving sufficient parameter capacity for subsequent adaptation. |
| Outcome: | The proposed method is based on the brain's functional partitioning and can be used to map tasks between specialized and generalist neurons. |
A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extraction (2026.findings-acl)
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Chengguang Gan, Sunbowen Lee, Qingyu Yin, Yunhao Liang, Xinyang He, Hanjun Wei, Younghun Lim, Shijian Wang, Hexiang Huang, QingHao Zhang, Shiwen Ni, Tatsunori Mori
| Challenge: | Existing work on the Mutual Reinforcement Effect in information extraction has not been empirically validated . 76 percent of the 21 sub-datasets exhibit the Mutual Reforcement effect across languages . |
| Approach: | They propose a multilingual MRE mix dataset that integrates 21 sub-datasets covering English, Japanese, and Chinese. |
| Outcome: | The proposed framework reduces manual annotation effort while preserving structural requirements of MRE tasks. |
Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference (2026.acl-long)
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| Challenge: | Speculative decoding (SD) is a powerful and efficient way to accelerate autoregressive generation. |
| Approach: | They propose a training-free framework that recovers valid tokens discarded by standard verification . they use online correction memory and Semantic Consistency Gating to analyze rejections . |
| Outcome: | The proposed framework outperforms existing methods and achieves peak throughput speedup of 2.33x. |
FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation (2025.findings-emnlp)
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| Challenge: | Using large language models to generate meaningful tests is expensive and time-consuming . |
| Approach: | They propose a data augmentation technique that incorporates valid testing semantics and diverse coverage-guided inputs into large language models. |
| Outcome: | The proposed technique improves performance over the baselines by incorporating valid testing semantics and providing diverse coverage-guided inputs. |
HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts (2024.acl-long)
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| Challenge: | Existing methods for enhancing performance through increased use of expert knowledge often result in diminishing sparsity during expert selection. |
| Approach: | They propose a framework that integrates the computational processes of MoE with the concept of knowledge transferring in multi-task learning. |
| Outcome: | The proposed framework outperforms existing methods under identical conditions concerning the number of experts. |
Recurrent Alignment with Hard Attention for Hierarchical Text Rating (2024.emnlp-main)
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| Challenge: | Large language models excel at understanding and generating plain text, but they are not tailored to handle hierarchical text structures or directly predict task-specific properties such as text rating. |
| Approach: | They propose a framework that integrates Recurrent Alignment with Hard Attention to analyze hierarchically structured text. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods on three hierarchical text rating datasets. |
Navigating the Infinite Dynamic Web Space: Effective In-Context Exploration via Cognitive Multi-Agent Collaboration (2026.eacl-long)
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Guozhao Mo, Yanjiang Liu, Yafei Shi, Jiawei Chen, Yang Li, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Bo Zheng, Xianpei Han
| Challenge: | Existing methods for dynamic web navigation rely on greedy strategies or value estimation, struggle to achieve effective backtracking and are heavily dependent on proprietary models. |
| Approach: | They propose a cognitive multi-agent collaboration framework that enhances cyberspace exploration capability through In-Context Exploration. |
| Outcome: | The proposed framework surpasses the proprietary model Claude-3.5 Sonnet on the WebArena benchmark. |
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)
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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu
| Challenge: | Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. |
| Approach: | They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety. |
| Outcome: | The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system. |
Trustworthiness and Self-awareness in Large Language Models: An Exploration through the Think-Solve-Verify Framework (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) are becoming increasingly influential in reasoning tasks, but they lack trustworthiness and introspective self-awareness when subjected to complex reasoning tasks. |
| Approach: | They propose a framework to explore LLMs’ trustworthiness, introspective self-awareness, and collaborative reasoning by using the Think-Solve-Verify framework. |
| Outcome: | The proposed approach improves from 67.3% to 72.8% on the AQuA dataset and demonstrates the model’s ability to explain the given answers. |
Benchmarking Large Language Models Under Data Contamination: A Survey from Static to Dynamic Evaluation (2025.emnlp-main)
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Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, Baishakhi Ray
| Challenge: | In the era of evaluating large language models, data contamination is an increasingly prominent concern . static benchmarking has been used for evaluation, but there are limitations of *dynamic* benchmarks . |
| Approach: | They propose a series of optimal design principles for *dynamic* benchmarking and analyze the limitations of existing *static* benchmarks. |
| Outcome: | The proposed benchmarks highlight a critical gap in the evaluation of LLMs. |
LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated impressive zero-shot capabilities in conversational recommender systems (CRS). |
| Approach: | They propose LLM-based CRS-based LLMs with Collaborative Verbalized Experience to enhance historical conversations by sampling trajectories of LLM agents on historical queries and establishing verbalized experience banks . |
| Outcome: | The proposed system improves on existing approaches to enhancing historical conversations by leveraging trajectories and verbalized experiences from LLMs on historical queries and user feedback. |
Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives (2024.findings-acl)
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| Challenge: | Existing datasets for narrative understanding fail to represent complexity and uncertainty of relationships in real-life social scenarios. |
| Approach: | They propose a benchmark for extracting and analysing intricate character relation graphs from detective narratives using large-scale large-language models. |
| Outcome: | The proposed dataset extracts and analyses character relation graphs from detective narratives using advanced Large Language Models like GPT-3.5, GPT-4, and Llama2 . |
HomoGraphAdapter: A Homogeneous Graph Neural Network as an Effective Adapter for Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Existing adaptation methods overlook structural knowledge between text and image modalities or create overly complex graphs containing redundant information for alignment. |
| Approach: | They propose a method to adapt visual models to downstream tasks using text and image modalities. |
| Outcome: | The proposed method improves classification accuracy by 1.51% for 1-shot and 0.74% for 16-shot on 11 datasets. |
Synthetic Pre-Training Tasks for Neural Machine Translation (2023.findings-acl)
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| Challenge: | toxicity and bias can be addressed by pre-training with synthetic resources . BLEU scores are used to compare methods with real-world data . |
| Approach: | They propose several ways to generate obfuscated data from large parallel corpus and concatenating phrase pairs from small word-aligned corpus with synthetic parallel data without real human language corpora. |
| Outcome: | The proposed methods can be used to generate obfuscated data or synthetic parallel data without real human language corpora even with high levels of oblication. |
Generative Models for Automatic Medical Decision Rule Extraction from Text (2024.emnlp-main)
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| Challenge: | Medical decision rules are traditionally constructed by medical experts, which is expensive and hard to scale up. |
| Approach: | They propose to extract medical decision rules from text using generative models . their code will be open-source upon acceptance . |
| Outcome: | The proposed model outperforms state-of-the-art models on a Chinese benchmark and achieves 67% tree accuracy. |
ExpNote: Black-box Large Language Models are better Task Solvers with Experience Notebook (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown great power in solving various tasks but fail in many specific tasks. |
| Approach: | They propose a framework to help black-box LLMs better adapt to unfamiliar tasks by reflecting and noting experiences from training data and retrieving them from external memory during testing. |
| Outcome: | The proposed framework improves the performance of black-box Large Language Models on multiple tasks and demonstrates that it is a good choice for the future. |
Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders (2026.acl-long)
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| Challenge: | Recent advances in large language models have enabled their use as semantic encoders for recommendation, but their roles and behaviors in this setting are still not well understood. |
| Approach: | They propose a benchmark to evaluate large language models as semantic encoders in recommendation scenarios. |
| Outcome: | The proposed benchmark shows that ranking of 11 leading LLMs is low compared to MTEB, highlighting the unique challenges of semantic encoding in recommendation. |
Advancing Process Verification for Large Language Models via Tree-Based Preference Learning (2024.emnlp-main)
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| Challenge: | Existing methods for generating step-by-step rationales fail to fully utilize the relative merits of intermediate steps, limiting the effectiveness of feedback provided. |
| Approach: | They propose a tree-based preference learning verifier that constructs reasoning trees via a best-first search algorithm and collects step-level paired data for preference training. |
| Outcome: | The proposed approach outperforms existing benchmarks on arithmetic and commonsense reasoning tasks. |
DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations (2024.findings-naacl)
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| Challenge: | Existing language models pre-trained on general text overlook the one-to-many property of task-oriented dialogues, where multiple responses can be appropriate given the same context. |
| Approach: | They propose a model that pre-trains LLMs to learn diverse task-oriented dialogue representations by removing domain knowledge that contradicts TODs. |
| Outcome: | The proposed model outperforms strong TOD baselines on various downstream dialogue tasks and learns the intrinsic diversity of task-oriented dialogues. |
CaMML: Context-Aware Multimodal Learner for Large Models (2024.acl-long)
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| Challenge: | a lightweight module for tuning large multimodal models is introduced . CaMML integrates contextual samples into large models, enabling them to make inferences . |
| Approach: | They introduce a lightweight module for tuning large multimodal models . they have developed two models that have shown exceptional performance . |
| Outcome: | The proposed model outperforms LLaVA-1.5 on ten widely recognized datasets with a noticeable margin. |
Principles from Clinical Research for NLP Model Generalization (2024.naacl-long)
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| Challenge: | In clinical research, generalizability depends on (a) internal validity of experiments and (b) external validity or transportability of the results to the wider population. |
| Approach: | They propose to ensure internal validity when building machine learning models in NLP by incorporating learning spurious correlations into their models. |
| Outcome: | The proposed model can perform well on data unseen during training, but drawn from the same distribution or population. |
RESF: Regularized-Entropy-Sensitive Fingerprinting for Black-Box Tamper Detection of Large Language Models (2025.emnlp-main)
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| Challenge: | Existing methods for tamper detection rely on model stability, not inherently stochastic models. |
| Approach: | They propose a hypothesis-testing method for black-box tamper detection for LLMs . they propose regularized entropy-sensitive fingerprinting to enable efficient fingerprinting . |
| Outcome: | The proposed method achieves 98.80% detection accuracy under challenging conditions . it is based on a first-order surrogate for KL divergence to identify prompts most responsive to parameter perturbations. |
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing benchmarks for hallucination detection are intentionally generated by large language models (LLMs) however, many focus on factuality while ignoring faithfulness. |
| Approach: | They propose a dialogue-level hallucination evaluation benchmark for large language models . they integrate the topic into prompts and facilitate a dialog between two LLMs . |
| Outcome: | The proposed benchmark covers four common multi-turn dialogue domains and five hallucination subtypes, extended from factuality and faithfulness hallucines. |
Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model (2024.naacl-long)
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| Challenge: | Existing methods to improve translation quality using human feedback have not been validated. |
| Approach: | They propose to use quality estimation to predict human preferences for feedback training . they propose to detect incorrect translations and assign a penalty term to the reward scores . |
| Outcome: | The proposed method outperforms systems using larger parallel corpora by a small amount of monolingual data. |
RMSSinger: Realistic-Music-Score based Singing Voice Synthesis (2023.findings-acl)
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| Challenge: | Existing methods for singing voice synthesis are limited to fine-grained music scores . manual adjustment destroys regularity of note durations, making fine-grain music scores "crushed" |
| Approach: | They propose a method to synthesize singing voices given realistic music scores . they use real-music-score-based Singing Voice Synthesis to generate high-quality voices . |
| Outcome: | The proposed method eliminates manual annotation and simplifies phoneme-level mel-note alignment. |
Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning (2025.acl-long)
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Ruoxi Xu, Yunjie Ji, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Ben He, Yingfei Sun, Xiangang Li, Le Sun
| Challenge: | Existing knowledge injection frameworks focus on knowledge memorization and retrieval, but static nature of large language models leads to outdated information as the real world evolves or when adapting to domain-specific knowledge. |
| Approach: | They propose a four-tier knowledge injection framework that defines the levels of knowledge injection: memorization, retrieval, reasoning, and association. |
| Outcome: | The proposed framework defines the levels of knowledge injection: memorization, retrieval, reasoning, and association. |
Can Multiple Responses from an LLM Reveal the Sources of Its Uncertainty? (2025.findings-emnlp)
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| Challenge: | Large language models can produce unreliable or misleading outputs, posing challenges for real-world applications. |
| Approach: | They employ an auxiliary LLM to analyze the patterns of disagreement among LLMs . they validate their framework on AmbigQA, OpenBookQA, and MMLU-Pro . |
| Outcome: | The proposed model can be used to diagnose uncertainty sources in a model with an auxiliary model. |
Token-wise Curriculum Learning for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing curriculum learning approaches to Neural Machine Translation (NMT) require sampling sufficient amounts of “easy” samples from training data at the early stage of training. |
| Approach: | They propose a token-wise curriculum learning approach that creates sufficient amounts of easy samples from training data. |
| Outcome: | The proposed approach outperforms baselines on five language pairs on low-resource languages. |
MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning (2026.acl-long)
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| Challenge: | Existing defenses against forgery are inadequate for healthcare. |
| Approach: | They propose a large-scale benchmark for pre-hoc, evidence-grounded medical forgery detection using a doctor inspection guideline and gold edit locations. |
| Outcome: | Experiments show that the proposed solution can detect and explain medical scans with high fidelity and accuracy. |
HeteroCache: A Dynamic Retrieval Approach to Heterogeneous KV Cache Compression for Long-Context LLM Inference (2026.acl-long)
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| Challenge: | Existing static compression methods suffer from coarse-grained caching and high I/O overhead. |
| Approach: | They propose a training-free dynamic compression framework that uses a sparse attention mechanism to categorize attention heads based on stability and similarity. |
| Outcome: | The proposed framework achieves state-of-the-art performance on long-context benchmarks and accelerates decoding by up to 3 compared to the original model with a 224K context. |
Sparse Black-Box Multimodal Attack for Vision-Language Adversary Generation (2023.findings-emnlp)
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| Challenge: | Existing adversarial attacks using imperceptible perturbations are challenging to simulate . e-commerce product restrictions and hate speech monitoring are examples of such attacks . |
| Approach: | They propose a black-box adversarial attack that leverages sparse perturbations to simulate adversarials exhibited by illegal merchants in the black- box scenario. |
| Outcome: | The proposed method outperforms existing attacks and unimodal attacks by treating images and text in discrete space and outperforming existing models. |
Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling (2025.naacl-long)
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Yiwen Ding, Zhiheng Xi, Wei He, Lizhuoyuan Lizhuoyuan, Yitao Zhai, Shi Xiaowei, Xunliang Cai, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Large language models (LLMs) generate solutions themselves and iteratively train on filtered, high-quality rationales, but performance reaches a ceiling after a few iterations. |
| Approach: | They propose a strategy to improve the efficiency of sampling heavy-tailed data by using Socratic-style guidance signals to help LLMs reasoning with complex queries. |
| Outcome: | The proposed approach is effective on difficult queries and on held-out tasks, while requiring human supervision. |
D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents (2026.findings-acl)
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Hongze Mi, Yibo Feng, WenJie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Jun Fang, Hua Chai, Naiqiang Tan, Gang Pan
| Challenge: | Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. |
| Approach: | They propose a deliberative framework that leverages a fine-grained tip retrieval mechanism to inform its decision-making process. |
| Outcome: | The proposed framework achieves SOTA among open-source general models on AndroidWorld and ScreenSpot-V2 . it leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process . |
Self-Guided Function Calling in Large Language Models via Stepwise Experience Recall (2025.findings-emnlp)
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| Challenge: | Existing methods for function calling require expert effort and prompt engineering becomes inefficient. |
| Approach: | They propose a method that performs fine-grained, stepwise retrieval from a continually updated experience pool. |
| Outcome: | The proposed method achieves an average improvement of 6.1% on easy and 4.7% on hard questions. |
Let’s Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models (2024.lrec-main)
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| Challenge: | Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
| Approach: | They propose a diffusion model which extracts aspects step by step and learns a denoising process that progressively restores them in a reverse manner. |
| Outcome: | Empirical evaluations on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
Locket: Robust Feature-Locking Technique for Language Models (2026.acl-long)
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| Challenge: | Existing FLoTEs (e.g., password-locked models) fail to meet these criteria. |
| Approach: | They propose a feature-locking technique that is effective in refusing locked features . they propose scalable FLoTEs that can selectively disable specific features of a model . |
| Outcome: | The proposed solution is effective (100% refusal rate), utility-preserving ( 7% utility degradation), robust (5% attack success rate), and scalable to multiple features and clients. |
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning (2022.emnlp-main)
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| Challenge: | Existing methods to detect out-of-domain (OOD) intents ignore alignment between representation learning and scoring function, limiting performance. |
| Approach: | They propose a unified neighborhood learning framework to detect OOD intents . they propose to align representation learning with scoring function . |
| Outcome: | The proposed method is able to detect out-of-domain (OOD) intents from user queries. |
Leveraging Web-Crawled Data for High-Quality Fine-Tuning (2024.findings-emnlp)
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| Challenge: | Currently, large language models are fine-tuned using expensive human-annotated data or GPT-4 generated data. |
| Approach: | They propose to use web-crawled data to train a language model on a smaller set of data . their results show that the model can convert web data with irregular formats into high-quality ones . |
| Outcome: | The proposed model outperforms open-source models larger than 32B and outperformed open-sourced models such as GPT-3.5. |
AdaFuse: Adaptive Ensemble Decoding for Large Language Models (2026.acl-long)
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Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng, Zhining Liu, Xuying Ning, Duo Zhou, Jingrui He
| Challenge: | Existing ensemble approaches to large language models lack flexibility for mid-generation adaptation. |
| Approach: | They propose an adaptive ensemble decoding framework that dynamically selects semantically appropriate fusion units during generation. |
| Outcome: | The proposed framework outperforms existing ensemble frameworks on open-domain QA, arithmetic reasoning, and machine translation tasks. |
Speaker Turn Modeling for Dialogue Act Classification (2021.findings-emnlp)
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| Challenge: | Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout the dialogue, thus treating it no different than non-interactive written text. |
| Approach: | They propose to integrate the turn changes in conversations among speakers when modeling DAs by learning conversation-invariant speaker turn embeddings to represent speaker turns in a conversation. |
| Outcome: | The proposed model captures semantics from the dialogue content while accounting for different speaker turns in a conversation. |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
CogKGE: A Knowledge Graph Embedding Toolkit and Benchmark for Representing Multi-source and Heterogeneous Knowledge (2022.acl-demo)
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Zhuoran Jin, Tianyi Men, Hongbang Yuan, Zhitao He, Dianbo Sui, Chenhao Wang, Zhipeng Xue, Yubo Chen, Jun Zhao
| Challenge: | Existing methods focus on entity-centric knowledge, but CogKGE supports heterogeneous knowledge. |
| Approach: | They propose a knowledge graph embedding toolkit to represent multi-source and heterogeneous knowledge. |
| Outcome: | The proposed toolkit provides a unified programming framework for KGE tasks and a series of knowledge representations for downstream tasks. |
Can Indirect Prompt Injection Attacks Be Detected and Removed? (2025.acl-long)
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| Challenge: | Recent studies have developed various detection mechanisms to protect against prompt injection attacks. |
| Approach: | They investigate the feasibility of detecting and removing indirect prompt injection attacks . they use two methods to evaluate their performance and train detection models . |
| Outcome: | The proposed method is based on a benchmark dataset and is available on github . it evaluates the performance of existing models and open-source detection models . |
UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation (2022.emnlp-main)
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| Challenge: | Existing methods for question answering using knowledge resources are mixed-of-experts and semantic parsing-based. |
| Approach: | They propose a semantic-parsing-based approach to perform Unified discrete Reasoning over heterogeneous knowledge resources as Program Generation. |
| Outcome: | The proposed approach improves interpretability and scalability over table and text . it achieves promising performance on the TAT-QA dataset without annotation . |
Efficient OpAmp Adaptation for Zoom Attention to Golden Contexts (2025.acl-long)
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| Challenge: | Large language models have shown significant promise in question-answering tasks . noisy reference documents hinder performance of LLMs, causing disproportionate attention to irrelevant content . |
| Approach: | They propose an adaptive large language model that allocates disproportionate attention to irrelevant documents . they use transformers to train the model and integrate it into pre-trained Transformer blocks . |
| Outcome: | The proposed model outperforms state-of-the-art models on noisy-context benchmarks. |
Cross-Lingual Training of Neural Models for Document Ranking (2020.findings-emnlp)
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| Challenge: | a recent study shows that multi-lingual BERT models can be used for document ranking in non-English languages . a blog post by Google suggests that the company is exploring this approach to improve web search across a number of languages. |
| Approach: | They propose to leverage relevance judgments in English to train neural document ranking models for mono-lingual retrieval in multiple target languages. |
| Outcome: | The proposed approach improves search quality in non-English languages while requiring low resources. |
ConsistentChat: Building Skeleton-Guided Consistent Multi-Turn Dialogues for Large Language Models from Scratch (2025.emnlp-main)
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Jiawei Chen, Xinyan Guan, Qianhao Yuan, Mo Guozhao, Weixiang Zhou, Yaojie Lu, Hongyu Lin, Ben He, Le Sun, Xianpei Han
| Challenge: | Existing instruction data synthesis methods focus on single-turn instructions and neglect cross-turn coherence, resulting in context drift and reduced task completion rates. |
| Approach: | They propose a framework that constrains multi-turn instruction synthesis by explicitly modeling human conversational intent. |
| Outcome: | The proposed framework outperforms existing models trained on single-turn and multi-turn instruction datasets. |
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization (2021.acl-long)
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Chen Liang, Simiao Zuo, Minshuo Chen, Haoming Jiang, Xiaodong Liu, Pengcheng He, Tuo Zhao, Weizhu Chen
| Challenge: | 'lottery tickets' can be trained to match the performance of a full model . subnetwork training can also outperform random sampled subnetworks of the same size . |
| Approach: | They propose to train a subnetwork of 'lottery tickets' to match the full model's performance. |
| Outcome: | The proposed model outperforms subnetworks of the same size in a phase transition phenomenon . the proposed model improves single task fine-tuning by 0.9 points on BERT-base and 1.0 points on GLUE large . |
Improving Faithfulness by Augmenting Negative Summaries from Fake Documents (2022.emnlp-main)
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| Challenge: | Current abstractive summarization systems tend to hallucinate unfaithful content . however, the most common method does not disentangle factual errors from other errors. |
| Approach: | They propose a back-translation-style approach to augment negative samples that mimic factual errors made by the model. |
| Outcome: | The proposed method improves faithfulness without sacrificing informativeness . it incorporates negative samples into training, and produces faithful/unfaithful summaries . |
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets (2023.findings-acl)
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| Challenge: | Existing work on conversational recommendation systems lacks high-quality data . existing datasets lack large-scale and high-level data based on human annotators . |
| Approach: | They propose an automatic dataset synthesis approach that generates large-scale recommendation dialogues using structured graphs based on user-item information from the real world. |
| Outcome: | The proposed approach can generate large-scale and high-quality recommendation dialogues . it exploits user preferences, knowledge graphs, and conversation ability from existing datasets based on real-world data . |
Deep Reinforcement Learning for NLP (P18-5)
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| Challenge: | Many natural language processing tasks can be formulated as deep reinforcement learning (DRL) problems. |
| Approach: | This tutorial provides an introduction to the foundations of deep reinforcement learning . it describes recent advances in designing deep reinforcement for NLP . |
| Outcome: | This tutorial provides an introduction to the foundations of deep reinforcement learning and some practical solutions for NLP tasks. |
ConRPG: Paraphrase Generation using Contexts as Regularizer (2021.emnlp-main)
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| Challenge: | Existing methods for paraphrase generation lack reliable supervision signals. |
| Approach: | They propose an unsupervised paradigm for paraphrase generation based on contextual language models, candidate filtering and paraphrase model training based upon the selected candidates. |
| Outcome: | The proposed paradigm outperforms existing paraphrase generation methods in supervised and unsupervised setups. |
ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis (2026.acl-long)
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| Challenge: | High-Level Synthesis (HLS) is a hardware design tool that can be used to design hardware from C-like languages, but its widespread adoption is limited by strict coding constraints and design-specific optimizations. |
| Approach: | They propose a multi-agent HLS design framework that leverages specialized LLMs for automated debugging and directive tuning. |
| Outcome: | The proposed framework outperforms Gemini-3-pro in debugging and speedups across various HLS kernels and neural network accelerators. |
A Semi-Supervised Stable Variational Network for Promoting Replier-Consistency in Dialogue Generation (D19-1)
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| Challenge: | Existing methods favor uninformative and non replier-specific responses due to lack of relevant information guidance. |
| Approach: | They propose to use a semi-supervised variable network to generate replier-specific responses . they use vMF as latent space to obtain stable KL performance . |
| Outcome: | The proposed model outperforms baseline models on two large conversation datasets and generates diverse and replier-specific responses. |
Translation or Recitation? Calibrating Evaluation Scores for Machine Translation of Extremely Low-Resource Languages (2026.acl-short)
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| Challenge: | Existing studies show that performance across low-resource settings is variable, resulting in a significant barrier for the MT community. |
| Approach: | They propose to use FRED Difficulty Metrics to contextualize reported performance across different language pairs to determine whether breakthroughs reported in other contexts are artifacts of benchmark collection. |
| Outcome: | The proposed metrics explain a significant portion of result variability rather than model capability. |
CLEAN–EVAL: Clean Evaluation on Contaminated Large Language Models (2024.findings-naacl)
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Wenhong Zhu, Hongkun Hao, Zhiwei He, Yun-Ze Song, Jiao Yueyang, Yumeng Zhang, Hanxu Hu, Yiran Wei, Rui Wang, Hongyuan Lu
| Challenge: | Existing methods to evaluate large language models are prone to data contamination. |
| Approach: | They propose a method which parses contaminated data and back-translates it into a candidate set. |
| Outcome: | The proposed method reduces data contamination and evaluates the LLMs more cleanly. |
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models (2024.lrec-main)
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| Challenge: | Existing methods for incorporating external knowledge into language models do not prioritize learning embeddings for entity-related tokens. |
| Approach: | They propose a framework for incorporating external knowledge into pre-training models that utilize entity-related tokens. |
| Outcome: | The proposed framework reduces pre-training time by 50% and outperforms other KEPLMs in knowledge probing tasks and multiple knowledge-aware language understanding tasks. |
Anchor-based Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) require massive GPU memory due to their size and parameter count. |
| Approach: | They propose to use anchor-based self-attention network and anchor-basic inference strategy to compress sequence information into an anchor token, reducing the keys/values cache and enhancing inference efficiency. |
| Outcome: | The proposed model reduces the key/value cache and improves inference efficiency by 99% while maintaining similar accuracy levels. |
In-Context Former: Lightning-fast Compressing Context for Large Language Model (2024.findings-emnlp)
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| Challenge: | Existing methods to reduce inference costs of transformer-based large language models entail quadratic complexity . et al., 2017): transformer-derived large language model performance is a major challenge. |
| Approach: | They propose a method that compresses long contexts into short soft prompts . they use the self-attention mechanism of the large model to extract and condense information . |
| Outcome: | The proposed method reduces compression costs by 68 to 112 times while achieving 90% of baseline performance. |
Beyond Human Labels: A Multi-Linguistic Auto-Generated Benchmark for Evaluating Large Language Models on Resume Parsing (2025.emnlp-main)
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| Challenge: | Efficient resume parsing is critical for global hiring, yet the lack of dedicated benchmarks for evaluating large language models (LLMs) on multilingual, structure-rich resumes hinders progress. |
| Approach: | They propose to use a human-in-the-loop pipeline to generate 2,500 synthetic resumes spanning 50 templates, 30 career fields, and 5 languages to evaluate large language models. |
| Outcome: | The proposed benchmarks show that the models perform poorly on multilingual resumes and lack of standardized templates. |
TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation (2023.acl-short)
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| Challenge: | tense inconsistency is a common problem in machine translation systems. |
| Approach: | They propose a parallel tense test set, containing French-English 552 utterances, and introduce a benchmark, tence prediction accuracy. |
| Outcome: | The proposed model can measure the tense consistency performance of machine translation systems for the first time. |
FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging (2025.acl-long)
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Zichen Tang, Haihong E, Ziyan Ma, Haoyang He, Jiacheng Liu, Zhongjun Yang, Zihua Rong, Rongjin Li, Kun Ji, Qing Huang, Xinyang Hu, Yang Liu, Qianhe Zheng
| Challenge: | Compared to existing benchmarks, FinanceReasoning provides three key advancements: (1) credibility; (2) comprehensiveness; (3) numerical precision; (4) complexity; (5) complexity; and (6) complexity. |
| Approach: | They propose a benchmark to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems. |
| Outcome: | The proposed benchmark exceeds existing benchmarks in 67.8% of financial concepts and formulas and is credible, comprehensive, and challenging. |
Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning (2025.acl-long)
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Zhu Xu, Zhiqiang Zhao, Zihan Zhang, Yuchi Liu, Quanwei Shen, Fei Liu, Yu Kuang, Jian He, Conglin Liu
| Challenge: | Large language models (LLMs) use tokenization methods but often obscure internal character structures within tokens. |
| Approach: | They propose a method that improves models’ ability to capture character positions within tokens by training them on reverse character prediction tasks using the tokenizer’s vocabulary. |
| Outcome: | Experiments show that the proposed method improves position prediction accuracy in large language models, enabling more precise identification of target characters in original text. |
A Novel Matching Paradigm: Unified Generative and Discriminative LLM with Prompt Compression for Relevance Learning (2026.acl-industry)
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Guoliang Zhao, Zixin Cui, Chao Ye, Dengwu He, Fei Huang, Yubo Liu, Shuanglong Li, Tzungren Kuo, Bin Ding, Shuang Zhang, null KunhongZhu, Zhi Guo, Liu Lin
| Challenge: | Existing approaches to matching use Large Language Models as feature extractors, underutilizing their full modeling capabilities. |
| Approach: | They propose a matching paradigm that integrates two-tower, single-towing, and generative tasks within a unified LLM framework via attention-mask partitioning. |
| Outcome: | The proposed model achieves superior performance and strong practical value in an industrial search engine. |
Traffic Light or Light Traffic? Investigating Phrasal Semantics in Large Language Models (2024.findings-emnlp)
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| Challenge: | Phrases are fundamental linguistic units through which humans convey semantics. |
| Approach: | They assess the capacity of API-based large language models to comprehend phrase semantics . they use three human-annotated datasets to analyze their results . |
| Outcome: | The proposed model outperforms embedding-based methods in phrase semantic reasoning tasks . the proposed model does not show significant advantage over fine-tuned methods . |
Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph (D19-1)
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| Challenge: | Existing studies on symptom diagnosis based on EHRs focus on the standard electronic medical records, but the dialogues between doctors and patients that contain more rich information are not well studied. |
| Approach: | They propose to build a global attention mechanism to capture more symptom related information and build symptom graphs to model the associations between symptoms rather than treating each symptom independently. |
| Outcome: | The proposed model achieves the state-of-the-art on the constructed dataset. |
DialMed: A Dataset for Dialogue-based Medication Recommendation (2022.coling-1)
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| Challenge: | Existing studies on medication recommendation mainly rely on EHRs, but some details of interactions between doctors and patients may be ignored or omitted in EHR. |
| Approach: | They propose to use medical dialogues to recommend medications with medical dialogue data . they propose to model dialogue structure and disease knowledge aware network . |
| Outcome: | The proposed method is a promising solution to recommend medications with medical dialogues. |
OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization (2025.acl-long)
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Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Hongming Zhang, Tianqing Fang, Zhenzhong Lan, Dong Yu
| Challenge: | Existing studies focus on building text-only agents in synthetic environments where the reward signals are clearly defined. |
| Approach: | They propose a multimodal web agent that can autonomously conduct real-world exploration and improve itself after each iteration. |
| Outcome: | The proposed agent improves itself after each iteration, demonstrating strong performance across multiple test sets. |
MaskTab: Scalable Masked Tabular Pretraining with Scaling Laws and Distillation for Industrial Classification (2026.findings-acl)
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| Challenge: | Tabular data is high-dimensional, riddled with missing entries, and rarely labeled at scale. |
| Approach: | They propose a unified pre-training framework for industrial-scale tabular data . MaskTab encodes missing values via dedicated learnable tokens . |
| Outcome: | The proposed framework outperforms XGBoost and MaskTab-L on industrial-scale . it achieves +5.04% AUC and +8.28% KS over prior art under rigorous scaling . |
Cognitive Scaffold: From Fluid Context to Crystallized Memory for Long-Horizon DeepResearch Agents (2026.acl-long)
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| Challenge: | Scaling LLM-based agents to long-horizon deep research is constrained by context-noise trade-off . solving a single query may require hundreds of interactions with noisy environments . |
| Approach: | They propose a factorized memory architecture that decouples the cognitive state into a Fluid Working Context for immediate reasoning and a persistent Knowledge Graph for long-term retention. |
| Outcome: | The Cognitive Scaffold outperforms baselines on Xbench-DeepSearch, BrowseComp-ZH, and GAIA . it achieves 74.7% Avg@3 and 87.0% Pass@3 on xbench, browseComp, and 88.3% Pass@3. |
Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence (2025.findings-acl)
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| Challenge: | Existing evaluations of Large Language Models (LLMs) reflect statistical rules that may not accurately represent LLMs’ true linguistic competence. |
| Approach: | They propose a method that combines minimal pair and diagnostic probing to analyze activation patterns across model layers. |
| Outcome: | The proposed method combines minimal pair and diagnostic probing to analyze activation patterns across model layers. |
Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup (2022.findings-emnlp)
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| Challenge: | Experimental results show that Rex can benefit from cross-lingual training and improve the effectiveness of semantic parsers. |
| Approach: | They propose a Representation Mixup Framework for effectively exploiting translations in the cross-lingual Text-to-SQL task. |
| Outcome: | The proposed framework can benefit from cross-lingual training and improve the effectiveness of semantic parsers, achieving state-of-the-art performance. |
PyraMathBench: Evaluating and Improving Mathematical Capability in Large Language Models (2026.findings-acl)
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| Challenge: | Numerical reasoning is ubiquitous in scientific research and financial analysis, but few benchmarks evaluate them by integrating numerical processing and mathematical reasoning. |
| Approach: | They propose a numerically-integrated hierarchical benchmark with 27,215 questions derived from 7,404 math word problems that spans 4 key cognitive aspects, 14 subcategories, and 2 modalities. |
| Outcome: | The proposed model improves Qwen-2.5 score with SOLVE and IRPO training. |
SEAD: A Surrogate-free Label-only Membership Inference Attack against Pre-trained LLMs with Semantic-Aware Density (2026.findings-acl)
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| Challenge: | Existing membership inference attacks require access to complete logits, but such access is often unavailable in real-world deployments where only the generated text is exposed. |
| Approach: | They propose a surrogate-free label-only MIA approach that directly estimates token probabilities through Monte Carlo sampling of the target model. |
| Outcome: | The proposed approach outperforms existing label-only attacks and serves as a foundational density estimator in the label-exclusive setting. |
GottBERT: a pure German Language Model (2024.emnlp-main)
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Raphael Scheible, Johann Frei, Fabian Thomczyk, Henry He, Patric Tippmann, Jochen Knaus, Victor Jaravine, Frank Kramer, Martin Boeker
| Challenge: | Pre-trained language models have advanced natural language processing (NLP) despite the introduction of BERT, single-language models are still relevant. |
| Approach: | They present a German singlelanguage RoBERT model pre-trained exclusively on the German portion of the OSCAR dataset. |
| Outcome: | The GottBERT model outperforms the existing models on Named Entity Recognition and text classification tasks. |
BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering (2023.acl-short)
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| Challenge: | Existing methods for commonsense reasoning use knowledge graphs to train models . however, it is not always possible to have relevant training data available . |
| Approach: | They propose to transform a question-answer task into a binary classification task by ranking all candidate answers according to their reasonableness. |
| Outcome: | The proposed approach is less data hungry than existing methods using KGs. |
Modularized Interaction Network for Named Entity Recognition (2021.acl-long)
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| Challenge: | Named Entity Recognition (NER) models focus on word-level information, while segment-based models focus only on word level information. |
| Approach: | They propose a Modularized Interaction Network (MIN) model which utilizes both word-level information and segment-level dependencies. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on three NER benchmark datasets. |
Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing (2025.emnlp-main)
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| Challenge: | Existing studies on large language models (LLMs) fail to detect character knowledge errors, leading to low-quality automatic corpus construction. |
| Approach: | They propose to use a large language model to detect known knowledge errors and an agent-based reasoning method to improve error detection. |
| Outcome: | The proposed method improves the ability of LLMs to detect errors in known knowledge errors and unknown knowledge errors while playing roles. |
ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models (2024.lrec-main)
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| Challenge: | acquiring and representing commonsense in machines has posed a long-standing challenge (Li et al., 2021; Zhang e t al, 2022; Zhou e al. 2023) . |
| Approach: | They use a commonsense-based LLM to evaluate ChatGPT's commonsensing abilities by analyzing 11 datasets and generating knowledge descriptions. |
| Outcome: | The proposed model can achieve good QA accuracies while still struggling with certain domains of datasets. |
A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation (2020.aacl-main)
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| Challenge: | Existing sampling algorithms for auto-regressive language models have similar performance . entropy reduction, order preservation and slope preservation are common properties of existing methods . |
| Approach: | They investigate the quality-diversity trade-off between ancestral sampling algorithms for auto-regressive language models. |
| Outcome: | The proposed methods have similar performance to existing methods for open-ended language generation. |
Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning (2026.findings-acl)
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| Challenge: | Recent advances have adapted this paradigm to Multimodal Foundation Models (MFMs), unlocking their potential in multimodal reasoning and generation. |
| Approach: | They propose a taxonomy framework that categorizes existing methodologies into three distinct strategies: sampling-based, feedback-based and search-based approaches. |
| Outcome: | The proposed framework categorizes existing methodologies into three distinct strategies: sampling-based, feedback-based and search-based approaches. |
Token Preference Optimization with Self-Calibrated Visual-Anchored Rewards for Hallucination Mitigation (2025.findings-emnlp)
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| Challenge: | Existing methods for predicting hallucinations suffer from two drawbacks: Lack of scalable token-level rewards and Neglect of visual-anchored tokens. |
| Approach: | They propose a Token Preference Optimization model with self-calibrated rewards . they propose based on visual-anchored tokens and visual-aware training objective . |
| Outcome: | The proposed model improves hallucination performance by focusing on visual-anchored tokens without fine-grained annotations. |
Active Generalized Category Discovery with Diverse LLM Feedback (2026.eacl-long)
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| Challenge: | Generalized Category Discovery (GCD) is a practical and challenging open-world task that aims to recognize both known and novel categories in unlabeled data using limited labeled data from known categories. |
| Approach: | They propose a framework for generalized category discovery that actively learns from diverse and collaborative feedback. |
| Outcome: | The proposed framework improves instance-level contrastive features, generates category descriptions, and aligns uncertain instances with LLM-selected category descriptions. |
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference (2021.findings-acl)
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| Challenge: | Multilingual transformers have been shown to have remarkable transfer skills in zero-shot settings. |
| Approach: | They investigate cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference using a large scale Chinese dataset. |
| Outcome: | The proposed model trains on Chinese and English natural language inference datasets. |
End-to-End Conversational Search for Online Shopping with Utterance Transfer (2021.emnlp-main)
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Liqiang Xiao, Jun Ma, Xin Luna Dong, Pascual Martínez-Gómez, Nasser Zalmout, Chenwei Zhang, Tong Zhao, Hao He, Yaohui Jin
| Challenge: | a new study proposes a conversational search system that integrates product attributes and dialog with search . but it faces two real world challenges: imperfect product schema/knowledge and lack of training dialog data . |
| Approach: | They propose an end-to-end conversational search system that integrates search with text . they propose an utterance transfer approach that generates dialogue utterations from other domains . |
| Outcome: | The proposed system outperforms the best tested baseline in a conversational search dataset for online shopping. |
Response-G1: Explicit Scene Graph Modeling for Proactive Streaming Video Understanding (2026.acl-long)
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Ke Ma, Jiaqi Tang, Bin Guo, Xueting Han, Ruonan Xu, Qingfeng He, Ziheng Wang, Xu Wang, Qifeng Chen, Zhiwen Yu, Yunhao Liu
| Challenge: | Existing methods for streaming video understanding are query-agnostic and implicitly model video evidence. |
| Approach: | They propose a framework that establishes explicit, structured alignment between the accumulated video evidence and the query’s expected response conditions via scene graphs. |
| Outcome: | The proposed model achieves more interpretable and accurate response timing decisions on both proactive and reactive tasks. |
Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration (2025.acl-long)
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Tianyi Bai, Ling Yang, Zhen Hao Wong, Fupeng Sun, Xinlin Zhuang, Jiahui Peng, Chi Zhang, Lijun Wu, Qiu Jiantao, Wentao Zhang, Binhang Yuan, Conghui He
| Challenge: | Efficient data selection is crucial to accelerate the pretraining of language models . limited research has addressed the inherent conflicts between data selection methods . |
| Approach: | They propose a multi-actor collaborative data selection mechanism that prioritizes data based on its specific criterion and updates prioritization rules using the current state of the model. |
| Outcome: | The proposed model accelerates convergence in LM pretraining and achieves an average relative performance gain of 10.5% across multiple language model benchmarks. |
Making Large Language Models Efficient Dense Retrievers (2026.acl-long)
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| Challenge: | Recent studies have shown that fine-tuning large language models for dense retrieval yields strong performance, but their substantial parameter counts make them computationally inefficient. |
| Approach: | They propose a framework for developing efficient retrievers that performs coarse-to-fine compression through a coarse-grained coarse-tuning strategy. |
| Outcome: | The proposed framework reduces model size and inference cost while preserving performance of full-size models. |
Self-Training Large Language Models for Tool-Use Without Demonstrations (2025.findings-naacl)
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| Challenge: | Recent work augmented LLMs with tools to mitigate factual inaccuracies and computational errors. |
| Approach: | They propose a method to synthesise tool-use traces using the LLM itself. |
| Outcome: | The proposed method improves performance on a long-tail knowledge task, but not on other datasets. |
ComCLIP: Training-Free Compositional Image and Text Matching (2024.naacl-long)
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| Challenge: | erroneous semantics of individual entities are essentially confounders that cause the matching failure. |
| Approach: | They propose a training-free compositional CLIP model which disentangles input images into subjects, objects, and action subimages and composes CLIP’s vision encoder and text encoder to perform evolving matching over compositional text embedding and subimage embeddments. |
| Outcome: | The proposed model mitigates spurious correlations introduced by the pretrained CLIP models and dynamically evaluates the importance of each component. |
Neuro-Symbolic Sentiment Analysis with Dynamic Word Sense Disambiguation (2023.findings-emnlp)
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| Challenge: | Traditional neural network models represent word senses as vectors that are uninterpretable for humans. |
| Approach: | They propose a framework that incorporates word Sense Disambiguation (WSD) by identifying and paraphrasing ambiguous words to improve sentiment predictions. |
| Outcome: | The proposed framework improves sentiment analysis accuracy and interpretability on a downstream task without ground-truth word sense labels. |
Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge (2026.acl-industry)
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| Challenge: | Existing MLLMs are optimized for single-task scenarios and struggle to generalize to diverse contexts. |
| Approach: | They propose a framework that integrates multitask reinforcement learning and generalization capabilities of MLLMs to optimize the judge model across multiple tasks. |
| Outcome: | The proposed framework outperforms baseline models in judgment consistency and correlation with human preferences. |
UMRSpell: Unifying the Detection and Correction Parts of Pre-trained Models towards Chinese Missing, Redundant, and Spelling Correction (2023.acl-long)
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| Challenge: | Chinese Spelling Correction (CSC) is a task of detecting and correcting misspelled charac- ters in Chinese texts. |
| Approach: | They propose a model to learn detection and correction parts together from a multi-task learning perspective. |
| Outcome: | The proposed model can learn detection and correction parts together from a multi-task learning perspective. |
Tracking Brand-Associated Polarity-Bearing Topics in User Reviews (2023.tacl-1)
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| Challenge: | Existing models that infer brand polarity scores from reviews are not able to infer polarities directly. |
| Approach: | They propose a dynamic Brand-Topic Model which detects and tracks brand-associated sentiment scores and polarity-bearing topics from product reviews organized in temporally ordered time intervals. |
| Outcome: | The proposed model outperforms competitive models on a MakeupAlley and hotel review datasets. |
RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following (2025.findings-acl)
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| Challenge: | Existing role-playing datasets mostly contribute to controlling role style and knowledge boundaries, but overlook role-following in instruction-follower scenarios. |
| Approach: | They propose a fine-grained role-playing and instruction-following composite benchmark, named RoleMRC, which includes multi-turn dialogues between ideal roles and humans, including free chats or discussions upon given passages . |
| Outcome: | The proposed model improves instruction-following without compromising general role-playing and reasoning capabilities. |
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog (2023.findings-acl)
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| Challenge: | Empirical results show dialog context representations are more conducive to multi-turn task-oriented dialog. |
| Approach: | They propose an end-to-end task-oriented dialog system with two contrastive learning strategies to model relationship between dialog context and belief/action state representations. |
| Outcome: | Empirical results show that dialog context representations are more conducive to multi-turn task-oriented dialog. |
Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models (2026.findings-acl)
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Teng Wang, Jiang Zhangyi, Zhenqi He, Hailei Gong, Shenyang Tong, Wenhan Yang, Zeyu Li, Yanan Zheng, Zifan He, Zewen Ye, Shengjie Ma, Jianping Zhang
| Challenge: | Existing Process Reward Models (PRMs) are vulnerable to reward hacking and require expensive, large-scale annotation of reasoning steps. |
| Approach: | They propose a reward model approach which evaluates both individual and consecutive reasoning steps from fine-grained and coarse-grounded level. |
| Outcome: | Empirical results show that the proposed model performs better than existing PRMs and is more robust than existing models. |
Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond (2025.acl-industry)
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Liang Wen, Yunke Cai, Fenrui Xiao, Xin He, Qi An, Zhenyu Duan, Yimin Du, Junchen Liu, Tanglifu Tanglifu, Xiaowei Lv, Haosheng Zou, Yongchao Deng, Shousheng Jia, Xiangzheng Zhang
| Challenge: | Experimental results show that opensource curriculum training is more effective when distinct datasets are available for different training stages. |
| Approach: | They propose an opensource suite for training long reasoning models using publicdata and models. |
| Outcome: | The proposed model outperforms DeepSeek-R1-DistillQwen-32B models in math reasoning. |
Hire a Linguist!: Learning Endangered Languages in LLMs with In-Context Linguistic Descriptions (2024.findings-acl)
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| Challenge: | Existing LLMs rarely perform well in unseen, endangered languages . Existing models such as Llama and GPT-4 lack a rich corpus of training data . |
| Approach: | They propose a training-free approach to enable an LLM to process unseen languages that hardly occur in its pre-training. |
| Outcome: | The proposed approach elevates translation capability from GPT-4’s 0 to 10.5 BLEU for 10 language directions. |
Rationales for Answers to Simple Math Word Problems Confuse Large Language Models (2024.findings-acl)
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| Challenge: | Recent studies show that large language models have advanced mathematical problem-solving abilities in grade school math word problems. |
| Approach: | They propose to combine fine-tuning and prompt-based methods to improve performance . they propose to use a hybrid algorithm to fine- tune LLMs on specific tasks . |
| Outcome: | The proposed methods improve performance on the proposed reasoning process evaluation benchmarks. |
Simultaneous Translation (2020.emnlp-tutorials)
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| Challenge: | Simultaneous translation is a problem that has long been considered one of the hardest problems in AI . this tutorial will provide a deep understanding of the history and the recent advances in simultaneous translation. |
| Approach: | This tutorial will examine the design and evaluation of policies for simultaneous translation . it will provide an overview of the history and recent advances in simultaneous translation. |
| Outcome: | This tutorial will examine the design and evaluation of policies for simultaneous translation . |
Controlling Bias Exposure for Fair Interpretable Predictions (2022.findings-emnlp)
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| Challenge: | Existing approaches to reduce bias in NLP tasks focus on protecting or isolating information related to a sensitive attribute, but they lack control over how much bias is required to be removed. |
| Approach: | They propose a favorable debiasing method that uses sensitive information ‘fairly’, rather than blindly eliminating it. |
| Outcome: | The proposed method achieves a trade-off between debiasing and task performance along with producing debiased rationales as evidence. |
DATA-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning (2024.findings-acl)
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| Challenge: | Existing methods to improve sentence representation learning (SRL) ignore the potential interference problems across tasks and instances. |
| Approach: | They propose a multi-task instruction tuning method that arranges the order of multi- task data for training to minimize interference risks. |
| Outcome: | The proposed method can boost the performance of state-of-the-art methods. |
DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection (2022.findings-emnlp)
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Haoran Meng, Zheng Xin, Tianyu Liu, Zizhen Wang, He Feng, Binghuai Lin, Xuemin Zhao, Yunbo Cao, Zhifang Sui
| Challenge: | DialogUSR is a plug-in and domain-agnostic module that empowers multi-intent detection for chatbots . a single user query triggers inquiries on highspeed train ticket price and weather of destination. |
| Approach: | They propose a dialog utterance splitting and reformulation task that splits multi-intent user query into multiple single-intention sub-queries and recovers all coreferred and omitted information in the sub-questions. |
| Outcome: | The proposed model can be used to split multi-intent user queries into multiple sub-queries . it can be trained in two stages and perform in-depth analyses on the proposed models . |
PRP-Graph: Pairwise Ranking Prompting to LLMs with Graph Aggregation for Effective Text Re-ranking (2024.acl-long)
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| Challenge: | Existing methods for pairing ranking prompting only output the same label for comparison results of different confidence intervals without considering the uncertainty of pairwise comparison. |
| Approach: | They propose a pairwise ranking prompting approach that exploits the output probabilities of target labels to capture the degree of certainty of comparison results. |
| Outcome: | The proposed method shows strong robustness and acceptable efficiency on the BEIR benchmark. |
S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Model (2024.naacl-long)
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| Challenge: | Existing benchmarks fail to evaluate extremely long-context LLMs or analyze their limitations. |
| Approach: | They propose a Synthetic, Scalable, Systematic evaluation suite for LLMs using SQL execution. |
| Outcome: | The proposed evaluation suite is able to scale text length and difficulty across scenarios and provides strong correlations with real-world benchmarks. |
TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control (2024.emnlp-main)
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| Challenge: | Existing models fail to generate singing voices rich in stylistic nuances for unseen singers due to multifaceted nature of singing styles. |
| Approach: | They propose a zero-shot SVS model for style transfer across cross-lingual speech and singing styles and multi-level style control. |
| Outcome: | Experimental results show that TCSinger outperforms baseline models in synthesis quality, singer similarity, and style controllability. |
On Safety Risks in Experience-Driven Self-Evolving Agents (2026.findings-acl)
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Weixiang Zhao, Yichen Zhang, Yingshuo Wang, Yang Deng, Yanyan Zhao, Xuda Zhi, Yongbo Huang, Hao He, Wanxiang Che, Bing Qin, Ting Liu
| Challenge: | Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduces underexplored safety risks. |
| Approach: | They investigate how experience accumulation and utilization in self-evolving agents affect safety performance across web-based and embodied environments. |
| Outcome: | The findings expose inherent limitations of current self-evolving agents and call for more principled strategies to ensure safe and reliable adaptation. |
STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework (P19-1)
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Mingbo Ma, Liang Huang, Hao Xiong, Renjie Zheng, Kaibo Liu, Baigong Zheng, Chuanqiang Zhang, Zhongjun He, Hairong Liu, Xing Li, Hua Wu, Haifeng Wang
| Challenge: | Simultaneous translation is notoriously dif- ficult due to word-order differences. |
| Approach: | They propose a prefix-to-prefix framework that implicitly learns to anticipate in a single translation model. |
| Outcome: | The proposed framework achieves low latency and reasonable qual- ity on 4 directions. |
Sentiment Knowledge Enhanced Self-supervised Learning for Multimodal Sentiment Analysis (2023.findings-acl)
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| Challenge: | Existing studies have used general approaches to alleviate the overfitting of supervised models based on video data with sentiment annotations. |
| Approach: | They propose to capture common sentimental patterns in unlabeled videos using sentiment knowledge and non-verbal behavior to embed sentiment information into pre-trained multimodal representations. |
| Outcome: | The proposed model outperforms the baseline and achieves new State-Of-The-Art (SOTA) results. |
MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation (2023.emnlp-main)
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| Challenge: | Existing medical datasets require high quality domain-specific datasets. |
| Approach: | They propose a multi-level, multi-task, and multi-domain medical benchmark to facilitate the development of language models for healthcare. |
| Outcome: | The proposed model provides granular potential usage and supports a wide range of tasks. |
Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining (2020.emnlp-main)
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| Challenge: | Pre-trained neural language models improve learning for various NLP tasks by fine-tuning them on task-specific training sets. |
| Approach: | They propose a meta-learning procedure to fine-tune neural language models on task-specific training sets. |
| Outcome: | The proposed procedure solves a group of similar NLP tasks on a text mining dataset. |
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation (2024.findings-acl)
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Hao Li, Yuping Wu, Viktor Schlegel, Riza Batista-Navarro, Tharindu Madusanka, Iqra Zahid, Jiayan Zeng, Xiaochi Wang, Xinran He, Yizhi Li, Goran Nenadic
| Challenge: | Recent advances in large language models (LLMs) have made it difficult to build an automated debate system that helps people to synthesise persuasive arguments. |
| Approach: | They propose to use an argument mining dataset to capture the end-to-end process of preparing an argumentative essay for a debate. |
| Outcome: | The proposed dataset shows that it performs better on individual tasks than on human-centred evaluations. |
VENUS: A VLLM-driven Video Content Discovery System for Real Application Scenarios (2025.emnlp-industry)
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Minyi Zhao, Yi Liu, Jianfeng Wen, Boshen Zhang, Hailang Chang, Zhiheng Ouyang, Jie Wang, Wensong He, Shuigeng Zhou
| Challenge: | Video Content Discovery (VCD) is to identify specific videos defined by a pre-specified text policy. |
| Approach: | They propose a Vision-Language Large Model-driven video content discovery system called VENUS to solve these problems. |
| Outcome: | The proposed system generates high-quality, VCD-specific data for model training and extends it to support it better. |
LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces (2026.findings-acl)
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Yukang Feng, Jianwen Sun, Zelai Yang, Jiaxin Ai, Chuanhao Li, Zizhen Li, Fanrui Zhang, Kang He, Rui Ma, Jifan Lin, Jie Sun, Yang Xiao, Sizhuo Zhou, Wenxiao Wu, Yiming Liu, Pengfei Liu, Shenglin Zhang, Kaipeng Zhang
| Challenge: | Existing benchmarks for agentic programming in long-horizon command-line interface tasks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics. |
| Approach: | They propose a benchmark to evaluate agentic capabilities across long-horizon command-line interface tasks. |
| Outcome: | The proposed benchmarks cover four engineering categories: from scratch, feature addition, bug fixing, and refactoring. |
Advancing Reasoning with Off-the-Shelf LLMs: A Semantic Structure Perspective (2025.findings-emnlp)
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| Challenge: | Existing reasoning models suffer from hallucinations and unfaithfulness, whereas general LLMs perform suboptimal on complex tasks. |
| Approach: | They propose a structure analysis method that helps LLMs better understand the question structure and guide the problem-solving process. |
| Outcome: | The proposed method improves zero-shot performance on knowledge-intensive and mathematical tasks while demonstrating strong robustness against corrupted reasoning paths. |
PlanningArena: A Modular Benchmark for Multidimensional Evaluation of Planning and Tool Learning (2025.acl-long)
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| Challenge: | Recent studies have shown that LLMs can be significantly improved by integrating external tools. |
| Approach: | They propose a framework that integrates external tools into large language models to evaluate their ability to generate action plans. |
| Outcome: | The proposed framework evaluates the ability of large language models to generate action plans and generate action plan templates. |
Divide-Verify-Refine: Can LLMs Self-align with Complex Instructions? (2025.findings-acl)
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| Challenge: | Existing research shows LLMs struggle with complex instructions involving multiple constraints. |
| Approach: | They propose a framework to divide complex instructions into single constraints and prepare appropriate tools to verify responses. |
| Outcome: | The proposed framework doubles Llama3.1-8B’s constraint adherence and triples Mistral-7B’ s performance. |
BC-Prover: Backward Chaining Prover for Formal Theorem Proving (2024.emnlp-main)
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| Challenge: | Existing methods for interactive theorem proving in formal logic lack robustness and robustness. |
| Approach: | They propose a backward chaining framework guided by pseudo steps for proofstep generation that prioritizes pseudo steps. |
| Outcome: | The proposed framework improves on the miniF2F benchmark. |
Syntax-aware Multilingual Semantic Role Labeling (D19-1)
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| Challenge: | Existing work on semantic role labeling (SRL) on English has focused on syntactic integration and enhanced word representation. |
| Approach: | They propose a method guided by syntactic rule to prune arguments to integrate syntax into multilingual SRL model simply and effectively. |
| Outcome: | The proposed model achieves state-of-the-art results on the CoNLL-2009 benchmarks of all seven languages. |
Are We Using the Right Benchmark: An Evaluation Framework for Visual Token Compression Methods (2026.acl-long)
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Chenfei Liao, Wensong Wang, Zichen Wen, Xu Zheng, Yiyu Wang, Haocong He, Yuanhuiyi Lyu, Lutao Jiang, Xin Zou, Yuqian Fu, Bin Ren, Linfeng Zhang, Xuming Hu
| Challenge: | Recent efforts to accelerate inference in Multimodal Large Language Models have focused on visual token compression. |
| Approach: | They propose a framework that leverages downsampling as a discriminator to denoise existing benchmarks. |
| Outcome: | The proposed evaluation framework leverages downsampling as a discriminator to denoise existing benchmarks. |
Dense Information Flow for Neural Machine Translation (N18-1)
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| Challenge: | Recent advances in deep neural networks have improved learning performance for NMT . Residual connections allow features from previous layers to be accumulated to the next layer easily. |
| Approach: | They propose a densely connected NMT architecture that can train more efficiently for NMT. |
| Outcome: | The proposed architecture improves learning performance and attention quality on multiple datasets. |
Knowledge Graph Embedding with Hierarchical Relation Structure (D18-1)
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| Challenge: | Existing knowledge graph embedding models embed entities and relations into latent vectors without leveraging rich information from relation structure. |
| Approach: | They extend existing KGE models to learn knowledge representations by leveraging relation structure . authors say their approach is capable of extending other KGEs . |
| Outcome: | The proposed approach can extend existing KGE models, and validates against baselines. |
Align Voting Behavior with Public Statements for Legislator Representation Learning (2021.acl-long)
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| Challenge: | Existing studies rely on roll call data to estimate political preference of legislators. |
| Approach: | They propose to integrate voting behavior and public statements on Twitter to jointly model legislators. |
| Outcome: | The proposed model improves on the task of roll call vote prediction . it also shows that the model captures nuances in statements . |
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval (D18-1)
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| Challenge: | Existing neural IR models do not have a mechanism for treating expansion terms differently from the original query terms, making it difficult to combine them with existing PRF approaches. |
| Approach: | They propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks. |
| Outcome: | Extensive experiments on two standard test collections confirm the effectiveness of the proposed framework in improving the performance of two state-of-the-art neural IR models. |
LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models (2024.emnlp-main)
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Yuxuan Wan, Wenxuan Wang, Yiliu Yang, Youliang Yuan, Jen-tse Huang, Pinjia He, Wenxiang Jiao, Michael Lyu
| Challenge: | LogicAsker examines and improves the reasoning abilities of large language models such as ChatGPT and GPT-4. |
| Approach: | They propose a set of atomic reasoning skills grounded in propositional and predicate logic to examine and improve the reasoning abilities of large language models such as ChatGPT and GPT-4. |
| Outcome: | The proposed approach improves reasoning abilities in large language models such as ChatGPT and GPT-4 by up to 5%. |
Fine- and Coarse-Granularity Hybrid Self-Attention for Efficient BERT (2022.acl-long)
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| Challenge: | Transformer-based pre-trained models achieve state-of-the-art results, but they can be prohibitively costly. |
| Approach: | They propose a fine- and coarse-granularity hybrid self-attention that shortens the computational sequence length in self- attention by progressively shortening the computational time. |
| Outcome: | The proposed model reduces computation cost by shortening the computational sequence length in self-attention. |
TAGQuant: Token-Aware Clustering for Group-Wise Quantization (2026.eacl-industry)
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| Challenge: | Existing work clusters channels using token dimension, which is suboptimal for grouping . a common challenge in LLM quantization is supporting "group-wise" quantization . |
| Approach: | They propose a method to group channels with similar activation distributions using tokens . they propose shuffle operation that reduces relative GSM8K error by 86% . |
| Outcome: | The proposed method reduces GSM8K error by 86% in both INT4 and MXFP4 formats compared to baselines . |
PlanGenLLMs: A Modern Survey of LLM Planning Capabilities (2025.acl-long)
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| Challenge: | Existing studies have focused on developing LLMs to automate complex planning tasks. |
| Approach: | They propose to provide a comprehensive overview of current LLM planners to fill this gap . they examine performance criteria including completeness, executability, optimality, representation, generalization, and efficiency . |
| Outcome: | The proposed survey examines performance criteria for LLM planners and highlights their strengths and weaknesses. |
PlanGPT: Enhancing Urban Planning with a Tailored Agent Framework (2025.acl-industry)
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| Challenge: | Empirical tests demonstrate that PlanGPT framework has achieved advanced performance, providing comprehensive support that significantly enhances professional planning efficiency. |
| Approach: | They propose a specialized AI agent framework tailored for urban and spatial planning that integrates a customized local database retrieval system and domain-specific knowledge activation capabilities. |
| Outcome: | Empirical tests show that PlanGPT framework significantly improves planning efficiency . it integrates a customized database retrieval system, domain-specific knowledge activation capabilities, and advanced tool orchestration mechanisms. |
K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (2021.findings-emnlp)
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| Challenge: | Existing pre-trained language models are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. |
| Approach: | They propose a knowledge-injected pre-trained language model that can be transferred to both natural language understanding and generation tasks. |
| Outcome: | The proposed model significantly outperforms baselines across the board in e-commerce scenarios. |
Language Embeddings for Typology and Cross-lingual Transfer Learning (2021.acl-long)
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| Challenge: | Recent efforts to leverage multilingual datasets highlight potential of multilingual models that can perform well across various languages. |
| Approach: | They propose to generate language representations that capture relationships among languages and evaluate them using WALS and two extrinsic tasks. |
| Outcome: | The proposed model can be leveraged in cross-lingual tasks without parallel data . the proposed model is based on the World Atlas of Language Structures (WALS) and two extrinsic tasks . |
XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration (2026.acl-long)
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Nuo Chen, Andre Lin HuiKai, Jiaying Wu, Junyi Hou, Zining Zhang, Qian Wang, Xidong Wang, Bingsheng He
| Challenge: | Existing systems are designed for general-purpose scientific text generation and fail to support high-quality scientific writing beyond surface-level polishing. |
| Approach: | They propose a human-AI collaboration framework for academic paper revision based on criteria-guided intent alignment and context-aware modeling. |
| Outcome: | The proposed framework outperforms existing LLMs and rivals the quality of proprietary ones. |
Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models (2025.findings-acl)
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Yingqian Cui, Pengfei He, Jingying Zeng, Hui Liu, Xianfeng Tang, Zhenwei Dai, Yan Han, Chen Luo, Jing Huang, Zhen Li, Suhang Wang, Yue Xing, Jiliang Tang, Qi He
| Challenge: | Chain-of-Thought (CoT) reasoning has improved the performance of large language models (LLMs) however, the detailed reasoning process in CoT often incurs long generation times and high computational costs due to the inclusion of unnecessary steps. |
| Approach: | They propose a method to identify critical reasoning steps using perplexity as a measure of their importance. |
| Outcome: | The proposed method achieves a better balance between reasoning accuracy and efficiency of CoT. |
BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models (2020.findings-emnlp)
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| Challenge: | Existing knowledge representation learning methods do not use graph contextualized knowledge. |
| Approach: | They propose to model subgraphs in a medical KG and integrate it with a pre-trained language model to do knowledge generalization. |
| Outcome: | The proposed model achieves state-of-the-art on several medical NLP tasks . it improves on MedERNIE, and the proposed model is effective . |
Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (2023.emnlp-main)
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| Challenge: | Extensive experiments show that MEO significantly improves computational efficiency . compared to dense networks, sparsely activated networks only employ a few parameters for each input . |
| Approach: | They propose a method that merges multiple experts into one to reduce computation costs . they demonstrate that a sparse Mixture of Experts (MoE) can reduce the cost by activating a small subset of parameters for each input . |
| Outcome: | The proposed approach reduces the computational cost to that of a single expert by 83.3% compared to 82.6% in vanilla MoE. |
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition (2020.emnlp-main)
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| Challenge: | Existing methods to augment pre-trained language models with disease knowledge are lacking. |
| Approach: | They propose a method to augment BERT-like pre-trained language models with disease knowledge. |
| Outcome: | The proposed method improves on a suite of BERT models over three tasks. |
Similarizing the Influence of Words with Contrastive Learning to Defend Word-level Adversarial Text Attack (2023.findings-acl)
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| Challenge: | Neural language models are vulnerable to word-level adversarial text attacks . previous word-based search methods assume important words influence prediction . |
| Approach: | They propose a method for similarizing the influence of words with contrast learning that encourages model to learn sentence representations in which words of varying importance have a more uniform influence on prediction. |
| Outcome: | The proposed method is compatible with various training methods and improves model robustness against various adversarial attacks. |
Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Media (2025.acl-long)
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| Challenge: | Social media platforms are experiencing a growing presence of AI-Generated Texts (AIGTs) however, the misuse of AIGTs could have profound implications for public opinion . |
| Approach: | They collect a dataset with 2.4M posts from 3 major social media platforms . they then construct a diverse dataset to train and evaluate AIGT detectors . |
| Outcome: | The proposed dataset analyzes 2.4M posts from 3 major social media platforms from 2022 to 2024 . it finds that Medium and Quora show marked increases in AAR . |
RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)
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Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang, Peng He, Yue Ma, Haochen Han, Wenhao Li, Tiao Tan, Yongjian Li, Fangming Liu, Gong Yifan, Sheng Zhang
| Challenge: | Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting. |
| Approach: | They propose a representation-aware model merging framework for continual learning without access to historical data. |
| Outcome: | The proposed framework outperforms baselines in knowledge retention and generalization across five NLP tasks and multiple continual learning scenarios. |
A Boundary Offset Prediction Network for Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Named entity recognition (NER) is a fundamental task in natural language processing . span-based methods assign entity types to text spans, resulting in imbalanced sample space . |
| Approach: | They propose a method that predicts boundary offsets between candidate and nearest spans . the method integrates entity type and span representations to generate type-aware boundary offset . |
| Outcome: | The proposed method outperforms existing methods on eight widely-used NER datasets. |
A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism (2022.emnlp-main)
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| Challenge: | Argument mining (AM) is a challenging task as it requires recognizing complex argumentation structures involving multiple subtasks. |
| Approach: | They propose a generative framework where expected outputs of AM are framed as a simple target sequence. |
| Outcome: | The proposed framework achieves state-of-the-art on two AM benchmarks. |
SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution (2025.findings-acl)
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| Challenge: | Large Language Models excel in code generation benchmarks, but these benchmarks focus on single-file scenarios with constrained context scope. |
| Approach: | They propose an open-source framework to effectively resolve GitHub issues using a code file retrieval module and a model-based code editing module. |
| Outcome: | The proposed approach achieves state-of-the-art performance on two GitHub benchmarks. |
GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion (2026.acl-long)
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| Challenge: | Existing quantization-based approaches to knowledge Graph Completion (KGC) are incomplete. |
| Approach: | They propose a framework that generates semantically coherent discrete codes for KG entities . they introduce a Granular Semantic Enhancement module that injects hierarchical knowledge into the codebook . |
| Outcome: | The proposed framework outperforms existing text-based and embedding-based baselines in the KGC domain. |
SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams (2026.findings-acl)
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Chenglong Wang, Canjia Li, Xingzhao Zhu, Yifu Huo, Huiyu Wang, Weixiong Lin, Yun Yang, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Tong Xiao
| Challenge: | Existing approaches to generate relevance judgments are limited due to dynamic nature of query distributions. |
| Approach: | They propose a self-evolving relevance model approach to generalize queries to practical search scenarios . they use a multi-agent sample miner and a relevance annotator to generate reliable labels . |
| Outcome: | The proposed approach can achieve significant performance gains on a large-scale industrial platform, validated by offline multilingual evaluations and online testing. |
Word-level Commonsense Knowledge Selection for Event Detection (2024.lrec-main)
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| Challenge: | Event Detection (ED) is a task of automatically extracting multi-class trigger words . Xie and Tu, 2022, use a Context-specific Knowledge Selector to select commonsense knowledge of words based on living contexts . |
| Approach: | They use a Context-specific Knowledge Selector to select the exact commonsense knowledge of words from a large knowledge base. |
| Outcome: | The proposed approach achieves the F1-score of about 78.3% on the ACE-2005 dataset. |
Mitigating Selection Bias in Large Language Models via Permutation-Aware GRPO (2026.acl-long)
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| Challenge: | Existing inference-time debiasing ignores that the same question should yield consistent answers across permutations. |
| Approach: | They propose a permutation-aware group-relative policy optimization which enforces permutations-consistent semantic reasoning. |
| Outcome: | The proposed model outperforms strong baselines across seven benchmarks while maintaining high overall performance. |
LongWanjuan: Towards Systematic Measurement for Long Text Quality (2024.findings-emnlp)
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| Challenge: | Existing efforts to improve data quality have focused on deduplication and the evaluation of data diversity and difficulty. |
| Approach: | They propose a set of metrics to evaluate the quality of long texts by evaluating three fundamental linguistic dimensions: coherence, cohesion, and complexity. |
| Outcome: | The proposed model improves on long-text tasks with over 160B tokens and categorizes long texts into holistic, aggregated, and chaotic types. |
Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)
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| Challenge: | Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on halluciation detection remains underexplored. |
| Approach: | They conduct an empirical evaluation of CoT prompting in Large Language Models (LLMs) to examine their impact on hallucination detection methods. |
| Outcome: | The proposed method significantly affects the internal states and token probability distributions of the LLM. |
More Than Meets the Eye: Measuring the Semiotic Gap in Vision-Language Models via Semantic Anchorage (2026.acl-long)
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| Challenge: | Vision-Language Models excel at photorealistic generation, but struggle to represent abstract meanings. |
| Approach: | They propose a benchmark that replaces high-fidelity visual detail with schematic iconicity by generating paired, sense-anchored visualizations for literal and idiomatic readings. |
| Outcome: | The proposed benchmark replaces high-fidelity visual detail with schematic iconicity by generating paired, sense-anchored visualizations for literal and idiomatic readings. |
Planning Like Human: A Dual-process Framework for Dialogue Planning (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) operate in a reactive mode, often resulting in efficiency issues or suboptimal performance. |
| Approach: | They propose a dual-process dialogue planning framework that leverages the dual-process theory of human cognition and a deliberative Monte Carlo Tree Search mechanism to emulate human-like conversational dynamics. |
| Outcome: | The proposed framework outperforms existing methods in achieving high-quality dialogues and operational efficiency. |
Negative Training for Neural Dialogue Response Generation (2020.acl-main)
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| Challenge: | Existing models have undesirable generation behaviors, such as generic (boring) responses. |
| Approach: | They propose a framework to minimize undesirable generation behaviors in deep learning models . they use generated samples to feed negative training signals to fine-tune the model . |
| Outcome: | The proposed framework reduces the hit rate of malicious responses or discourages frequent responses and improves response diversity. |
Harnessing Consistency for Robust Test-Time LLM Ensemble (2026.findings-eacl)
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Zhichen Zeng, Qi Yu, Xiao Lin, Ruizhong Qiu, Xuying Ning, Tianxin Wei, Yuchen Yan, Jingrui He, Hanghang Tong
| Challenge: | Existing efforts to improve LLM ensemble quality have focused on model consistency, but failures are often due to heterogeneous tokenization schemes and varying model expertise. |
| Approach: | They propose a plug-and-play technique that harnesses model consistency for robust LLM ensemble. |
| Outcome: | The proposed technique improves ensemble performance and robustness against erroneous signals. |
Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Fine-tuning (2025.emnlp-main)
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| Challenge: | Language models such as GPT and Llama have shown remarkable ability on diverse natural language tasks, yet their performance on complex table tasks is suboptimal. |
| Approach: | They propose a generator-validator paradigm to iteratively generate-then-validate training data from language models to fine-tune stronger Table-Specialist models that can specialize in a given task, without using manually-labeled data. |
| Outcome: | The proposed model outperforms vanilla language models on diverse table tasks and can match or surpass GPT-4 level quality. |
A Flexible Multi-Task Model for BERT Serving (2022.acl-short)
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| Challenge: | a proposed BERT-based multi-task framework is suitable for iterative and incremental development of the tasks. |
| Approach: | They propose an efficient BERT-based multi-task framework that is suitable for iterative and incremental development of the tasks. |
| Outcome: | The proposed framework achieves 99.6% of performance of the full fine-tuning method while reducing up to two thirds of its overhead. |
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time (2025.emnlp-main)
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Junyu Zhang, Runpei Dong, Han Wang, Xuying Ning, Haoran Geng, Peihao Li, Xialin He, Yutong Bai, Jitendra Malik, Saurabh Gupta, Huan Zhang
| Challenge: | Existing monotonic scaling methods for large reasoning models are not reliable. |
| Approach: | They propose a universal framework for modulating reasoning progress in large reasoning models at test time. |
| Outcome: | The proposed framework unifies and generalizes existing monotonic scaling methods and enables flexible and dense slow-to-fast reasoning modulation. |
Syntactic Graph Convolutional Network for Spoken Language Understanding (2020.coling-main)
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| Challenge: | Existing work on slot filling and intent detection builds joint models without prior knowledge of linguistic knowledge. |
| Approach: | They propose a joint model that integrates syntactic structure for learning slot filling and intent detection jointly. |
| Outcome: | The proposed model outperforms existing models on two public benchmark datasets and further improves on slot filling and intent detection. |
LogicTree: Structured Proof Exploration for Coherent and Rigorous Logical Reasoning with Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have remarkable multi-step reasoning capabilities, but they still face challenges in complex logical reasoning. |
| Approach: | They propose an algorithm-guided search framework that automates structured proof exploration and ensures logical coherence. |
| Outcome: | The proposed framework outperforms o3-mini and chain-of-thought with average gains of 23.6% and 12.5% on five datasets. |
R3-SQL: Ranking Reward and Resampling for Text-to-SQL (2026.findings-acl)
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| Challenge: | Existing rankers assign inconsistent scores to functionally equivalent SQL queries . ranking cannot recover when the correct SQL is absent from the pool. |
| Approach: | They propose a Text-to-SQL framework that rewards ranking and resampling . it first groups candidates by execution result and ranks groups for consistency . |
| Outcome: | The proposed framework achieves 75.03 execution accuracy on BIRD-dev, a new state of the art among methods using models with disclosed sizes. |
LLM-based Rumor Detection via Influence Guided Sample Selection and Game-based Perspective Analysis (2025.acl-long)
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Zhiliang Tian, Jingyuan Huang, Zejiang He, Zhen Huang, Menglong Lu, Linbo Qiao, Songzhu Mei, Yijie Wang, Dongsheng Li
| Challenge: | Existing methods for rumor detection on social media are limited by limited modeling capacity and insufficient training corpora. |
| Approach: | They propose an SFT-based rumor detection model with Influence guided Sample selection and Game-based multi-perspective analysis to address these issues. |
| Outcome: | The proposed model outperforms existing SOTA on three datasets. |
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks (2020.coling-main)
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| Challenge: | Recent work on data augmentation techniques that interpolate inputs and labels shows strong effectiveness in image classification. |
| Approach: | They propose to integrate mixup to transformer-based pre-trained architecture for NLP tasks while keeping the whole end-to-end training system. |
| Outcome: | The proposed framework improves on GLUEbenchmark and transformer-based learning models while keeping the whole end-to-end training system. |
Security Challenges in Natural Language Processing Models (2023.emnlp-tutorial)
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| Challenge: | Large-scale natural language processing models are vulnerable to security issues, such as backdoor attacks, private data leakage, and imitation attacks. |
| Approach: | They will dive into three emerging security issues in NLP research, i.e., backdoor attacks, private data leakage, and imitation attacks. |
| Outcome: | This tutorial will cover three emerging security issues in NLP research, i.e., backdoor attacks, private data leakage, and imitation attacks. |
See the World, Discover Knowledge: A Chinese Factuality Evaluation for Large Vision Language Models (2025.findings-acl)
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Jihao Gu, Yingyao Wang, Pi Bu, Chen Wang, Ziming Wang, Tengtao Song, Donglai Wei, Jiale Yuan, Yingxiu Zhao, Yancheng He, Shilong Li, Jiaheng Liu, Meng Cao, Jun Song, Yingshui Tan, Xiang Li, Wenbo Su, Xiaoyong Zhu, Bo Zheng
| Challenge: | Existing models for large vision language models do not fully reflect their knowledge capacity and reliability, resulting in erroneous outputs that do not align with the image content or provide answers lacking knowledge evidence. |
| Approach: | They propose a Chinese-based benchmark for visual factuality across 8 major topics and 56 subtopics and a multi-hop question construction. |
| Outcome: | The proposed model decouples visual factuality into two parts: seeing the world and discovering knowledge. |
Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss (2024.findings-acl)
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| Challenge: | Accurately modeling idiomatic or non-compositional language has been a longstanding challenge in natural language processing (NLP). |
| Approach: | They propose an approach to model idiomaticity effectively using a triplet loss that incorporates the asymmetric contribution of components words to an idiomatic meaning by using adaptive contrastive learning and resampling miners. |
| Outcome: | The proposed model outperforms previous models significantly on a SemEval challenge and outperformed previous alternatives in many metrics. |
Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator (2023.findings-acl)
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| Challenge: | Existing transformer models are computationally demanding and prohibitively costly for long sequences due to the quadratic complexity of its selfattention module. |
| Approach: | They propose a transformer-based model that inherits weights from large pretrained models by removing redundancies in hidden sequences using the ready-made Fast Fourier Transform operator. |
| Outcome: | The proposed model outperforms the standard BART model on the long-range modeling benchmark LRA with significant improvements in speed and space. |
MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models (2026.acl-long)
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Jie Cao, Tianwei Lin, Bo Yuan, Rolan Yan, Hongyang He, Wenqiao Zhang, Juncheng Li, Dongping Zhang, Siliang Tang, Yueting Zhuang
| Challenge: | Existing methods for parameter-efficient fine-tuning (PEFT) are limited by computational costs and performance degradation. |
| Approach: | They propose a method that integrates Low-Rank Adaptation and Mixture-of-Experts (MoE) they propose combining expert load imbalance and representation collapse to improve LLM performance . |
| Outcome: | The proposed method outperforms homogeneous MoE-LoRA architectures in performance and parameter efficiency. |
STEER-BENCH: A Benchmark for Evaluating the Steerability of Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models can adapt outputs to align with community-specific norms, perspectives and communication styles. |
| Approach: | They propose a benchmark to assess community-specific steering using contrasting reddit communities. |
| Outcome: | STEER-BENCH assesses how well large language models understand community-specific instructions, their resilience to adversarial steering attempts, and their ability to accurately represent cultural and ideological perspectives. |
Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model (2023.eacl-main)
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| Challenge: | Existing taxonomies focus on adding concepts to the leaf nodes of the existing tree, which does not fully utilize the taxonomy’s knowledge and is unable to update the original taxomy structure. |
| Approach: | They propose a two-stage method called ATTEMPT for taxonomy completion that inserts new concepts into the correct position by finding a parent node and labeling child nodes. |
| Outcome: | The proposed method performs best on taxonomy completion and extension tasks, surpassing existing methods. |
SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models (2024.emnlp-main)
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| Challenge: | Existing methods fail to fully exploit the knowledge embedded in models from previous tasks . Existing techniques fail to exploit the information embedded in previous tasks, resulting in a large number of replay samples to achieve good results. |
| Approach: | They propose a method that uses attention weights to extract knowledge from previous tasks . they use a data replay strategy to extract the knowledge from the previous tasks. |
| Outcome: | The proposed method achieves comparable or even better performance with only 1/10 of replayed data used by other methods. |
MORPHEUS: Modeling Role from Personalized Dialogue History by Exploring and Utilizing Latent Space (2024.emnlp-main)
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Yihong Tang, Bo Wang, Dongming Zhao, Jinxiaojia Jinxiaojia, Zhangjijun Zhangjijun, Ruifang He, Yuexian Hou
| Challenge: | Personalized Dialogue Generation relies on external role data, which can be scarce and raise privacy concerns. |
| Approach: | They propose a framework to extract role information from dialogue history . they use persona codebook to represent roles in latent space and posterior distribution of role information . |
| Outcome: | The proposed framework can generalize across roles, even for unseen roles. |
Style-Compress: An LLM-Based Prompt Compression Framework Considering Task-Specific Styles (2024.findings-emnlp)
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| Challenge: | Prompt compression reduces inference time and costs while maintaining informativeness for different usage scenarios. |
| Approach: | They propose a framework that adapts a smaller language model to compress prompts for a larger model on a new task without additional training. |
| Outcome: | The proposed framework outperforms two baseline models in four tasks . iteratively generates and selects effective compressed prompts as task-specific demonstrations . |
SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction (2020.acl-main)
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| Challenge: | Aspect terms and opinion terms are key problems of fine-grained aspect-based sentiment analysis. |
| Approach: | They propose a method to extract aspect and opinion terms as pairs from a sentence . they use shared spans to extract the terms under supervision of span boundaries . |
| Outcome: | The proposed method outperforms state-of-the-art methods on both aspects and opinion terms extraction tasks. |
Dive into Deep Learning for Natural Language Processing (D19-2)
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| Challenge: | GluonNLP is a powerful new toolkit that automates the most laborious aspects of deep learning for NLP. |
| Approach: | This hands-on tutorial demonstrates how to scale unsupervised pre-training techniques with Apache MXNet and GluonNLP. |
| Outcome: | This hands-on tutorial examines the challenges of scaling these models and algorithms effectively with Apache MXNet and GluonNLP. |
Modeling Content Importance for Summarization with Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Existing studies on content importance do not consider semantics and context when evaluating importance. |
| Approach: | They apply information theory to pre-trained language models to define the concept of importance from the perspective of information amount. |
| Outcome: | Experiments on CNN/Daily Mail and New York Times show that the proposed model can model the importance of content better than previous methods based on F1 and ROUGE scores. |
Threshold-driven Pruning with Segmented Maximum Term Weights for Approximate Cluster-based Sparse Retrieval (2024.emnlp-main)
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| Challenge: | Using rank score thresholding, sparse retrieval skips the index at cluster and document levels. |
| Approach: | They propose a pruning control scheme with a probabilistic guarantee on rank-safeness competitiveness. |
| Outcome: | The proposed pruning control scheme improves accuracy and safeness while delivering low latency on single-threaded CPU. |
Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs (2021.emnlp-main)
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| Challenge: | Existing methods for multi-label document classification ignore the heterogeneous graphical structures of metadata and labels. |
| Approach: | They propose a neural network based approach to multi-label document classification that uses two heterogeneous graphs to model metadata and labels. |
| Outcome: | The proposed approach outperforms state-of-the-art models on two benchmark datasets. |
MoRI: Learning Motivation-Grounded Reasoning for Scientific Ideation in Large Language Models (2026.acl-long)
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| Challenge: | Existing LLMs emulate human research workflows but lack scientific grounding . empirical results show that MoRI outperforms strong commercial LLM models . |
| Approach: | They propose a framework that explicitly learns scientific reasoning from research motivations to methodologies. |
| Outcome: | The proposed framework outperforms commercial LLMs and agentic baselines in novelty, technical rigor, and feasibility. |
Why LLMs Hallucinate on Structured Knowledge: A Mechanistic Analysis of Reasoning over Linearized Representations (2026.acl-long)
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Shanghao Li, Jinda Han, Yibo Wang, Yuanjie Zhu, Zihe Song, Langzhou He, Kenan Kamel A Alghythee, Philip S. Yu
| Challenge: | Existing literature primarily addresses this problem through external interventions such as retrieval augmentation and prompt engineering at the input or output level. |
| Approach: | They find that LLMs can still produce hallucinated outputs when using structured external knowledge. |
| Outcome: | The proposed models fail to ground the provided knowledge, causing the model to revert to parametric memory. |
INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent (2025.acl-long)
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Haohang Li, Yupeng Cao, Yangyang Yu, Shashidhar Reddy Javaji, Zhiyang Deng, Yueru He, Yuechen Jiang, Zining Zhu, K.p. Subbalakshmi, Jimin Huang, Lingfei Qian, Xueqing Peng, Jordan W. Suchow, Qianqian Xie
| Challenge: | Recent advances have underscored the potential of large language model (LLM)-based agents in financial decision-making. |
| Approach: | They propose to evaluate LLM agents using 13 different LLMs as backbone models across various market environments and tasks. |
| Outcome: | The proposed framework assesses the reasoning and decision-making capabilities of 13 different LLMs across various market environments and tasks. |
Eye Movement Features Can Predict Human Preferences on Machine-Generated Texts (2026.acl-srw)
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| Challenge: | Existing studies on eye movement in text quality assessment are limited . eye-movement features are important predictors of human judgments of text quality, but are costly and inconsistent. |
| Approach: | They propose to capture eye-movement features during screen reading of LLM-generated text using a dataset that includes eye-motion recordings, reading-time measurements, and post-reading evaluations. |
| Outcome: | The proposed dataset shows that eye-movement features can significantly improve models over other probabilistic metrics, including negative log-likelihood (NLL). |
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling (2022.coling-1)
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Guanting Dong, Daichi Guo, Liwen Wang, Xuefeng Li, Zechen Wang, Chen Zeng, Keqing He, Jinzheng Zhao, Hao Lei, Xinyue Cui, Yi Huang, Junlan Feng, Weiran Xu
| Challenge: | Existing slot filling models memorize inherent patterns of entities and contexts from training data. |
| Approach: | They propose a perturbed semantic structure awareness transferring method for slot filling models . they use two MLM-based training strategies to learn contextual semantic structure and word distribution . |
| Outcome: | The proposed method outperforms existing methods and gains strong generalization while preventing model from memorizing inherent patterns of entities and contexts. |
LLaSA: Large Language and Structured Data Assistant (2025.naacl-long)
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Yao Xu, Shizhu He, Jiabei Chen, ZengXiangrong ZengXiangrong, Bingning Wang, Guang Liu, Jun Zhao, Kang Liu
| Challenge: | Structured knowledge grounding (SKG) tasks are a key part of many NLP applications. |
| Approach: | They propose a framework for enhancing LLMs' ability to handle structured data . they represent various types of structured data in a unified hypergraph format . |
| Outcome: | The proposed framework outperforms existing methods on SKG tasks using LoRA finetuning. |
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning (2021.acl-short)
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| Challenge: | Existing methods of OOD detection only focus on whether a sample is correctly classified . lack of real OOD examples leads to poor prior knowledge about these unknown intents . |
| Approach: | They propose a supervised contrastive learning objective to minimize intra-class variance . they employ an adversarial augmentation mechanism to obtain pseudo diverse views . |
| Outcome: | The proposed method minimizes intra-class variance by pulling together in-domain intents belonging to the same class and maximizes inter-class variation by pushing apart samples from different classes. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
ProMedical: Hierarchical Fine-Grained Criteria Modeling for Medical LLM Alignment via Explicit Injection (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are difficult to align with high-stakes medical standards due to dissonance between coarse-grained preference signals and complex protocols. |
| Approach: | They propose a framework that aligns Large Language Models with medical standards . they use a dataset generated via a human-in-the-loop pipeline to augment medical instructions . |
| Outcome: | The proposed framework disentangles safety constraints from general proficiency, enabling precise guidance during reinforcement learning. |
How Do Your Code LLMs perform? Empowering Code Instruction Tuning with Really Good Data (2024.emnlp-main)
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Yejie Wang, Keqing He, Dayuan Fu, Zhuoma GongQue, Heyang Xu, Yanxu Chen, Zhexu Wang, Yujia Fu, Guanting Dong, Muxi Diao, Jingang Wang, Mengdi Zhang, Xunliang Cai, Weiran Xu
| Challenge: | Recent research has shown that code pre-trained models improve coding capabilities. |
| Approach: | They propose a code data pruning strategy to identify which datasets are high-quality code instruction data. |
| Outcome: | The proposed model achieves state-of-the-art performance using fewer training data. |
Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study (2024.eacl-short)
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| Challenge: | pharmacovigilance event extraction is a key field of healthcare that involves identifying, evaluating, understanding, and preventing adverse effects. |
| Approach: | They investigate the ability of large language models (LLMs) to extract adverse events from medical text. |
| Outcome: | The proposed model performs reasonably well with demonstration selection strategies, but falls short compared to fully fine-tuned small models. |
HiGMem: A Hierarchical and LLM-Guided Memory System for Long-Term Conversational Agents (2026.findings-acl)
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| Challenge: | Existing memory systems rely on vector similarity for retrieval, resulting in bloated evidence sets . existing systems produce little additional recall, but this approach lowers retrieval precision . |
| Approach: | They propose a two-level event-turn memory system that uses event summaries as semantic anchors to predict which related turns are worth reading. |
| Outcome: | The proposed system achieves the best F1 on four of five question categories and improves adversarial F1 from 0.54 to 0.78 over A-Mem while retrieving an order of magnitude fewer turns. |
On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation (2025.findings-acl)
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Xueru Wen, Jie Lou, Xinyu Lu, Yuqiu Ji, Xinyan Guan, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Debing Zhang, Le Sun
| Challenge: | Large language models exhibit behavior that deviates from the boundaries of their knowledge during response generation. |
| Approach: | They propose a framework that allows large language models to explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals. |
| Outcome: | The proposed framework enables LLMs to explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals. |
DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy Slot Filling Task (2023.findings-emnlp)
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Guanting Dong, Tingfeng Hui, Zhuoma GongQue, Jinxu Zhao, Daichi Guo, Gang Zhao, Keqing He, Weiran Xu
| Challenge: | Recent prompt-based generative frameworks have shown impressive capabilities in sequence labeling tasks. |
| Approach: | They propose a multi-task demonstration-based generative framework for noisy slot filling that captures input perturbations at different granularities. |
| Outcome: | The proposed framework outperforms baseline methods and achieves strong generalization. |
DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections (2021.eacl-main)
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Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, Ilya Eckstein
| Challenge: | Using pre-trained models, we learn to jointly predict words and entities from multiple text sources without any human supervision. |
| Approach: | They propose to learn rich self-supervised entity representations from large amounts of associated text. |
| Outcome: | The proposed models outperform baseline models on downstream tasks in the TV-Movies domain, and scale to very large corpora. |
UCS: Estimating Unseen Coverage for Improved In-Context Learning (2026.findings-acl)
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| Challenge: | Existing selection methods prioritize heuristic notions of relevance or diversity and provide limited insight into the coverage of a demonstration set. |
| Approach: | They propose a training-free, subset-level coverage prior that is unrevealed by a model-consistent embedding and a Smoothed Good-Turing estimator to estimate the number of unrevelled clusters within a candidate subset. |
| Outcome: | Experiments on multiple intent-classification and reasoning benchmarks show that augmenting strong baselines with UCS improves ICL accuracy by 2-6% under the same selection budget. |
QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs (2026.findings-acl)
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| Challenge: | Recent studies have shown that large language models (LLMs) can be effective for correcting factual inaccuracies but can still suffer from hallucinations. |
| Approach: | They propose a queue-based self-correction framework that addresses parameter bias during sequential model editing. |
| Outcome: | The proposed framework outperforms baseline models while maintaining competitive performance in single-turn editing. |
LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop (2025.emnlp-demos)
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| Challenge: | Existing systems that provide personalised, curriculum-aligned feedback are time-intensive and time-consuming. |
| Approach: | They propose a modular, LLM-based system that generates personalised, curriculum-aligned feedback in science education. |
| Outcome: | The proposed system generates personalised, curriculum-aligned feedback in science education. |
Intuitive or Dependent? Investigating LLMs’ Behavior Style to Conflicting Prompts (2024.acl-long)
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| Challenge: | Extensive experiments with seven Large Language Models reveal their varying behaviors. |
| Approach: | They investigate the behaviors of Large Language Models when faced with conflicting prompts versus their internal memory. |
| Outcome: | Extensive experiments with seven LLMs reveal their varying behaviors. |
When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents (2026.acl-long)
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Jiahe Guo, Xiangran Guo, Yulin Hu, Zimo Long, Xingyu Sui, Xuda Zhi, Yongbo Huang, Hao He, Weixiang Zhao, Yanyan Zhao, Bing Qin
| Challenge: | Existing research on personalized LLM agents focuses on the effectiveness of personalized responses. |
| Approach: | They propose a benchmark to quantify intent legitimation in personalized interactions . they propose 'detection-reflection' method that detects intent legititimation from internal representation space . |
| Outcome: | The proposed method reduces safety degradation by using internal representation space. |
Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method (2024.findings-acl)
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| Challenge: | Using a novel approach, we can evaluate an agent’s bargaining abilities as an asymmetric incomplete information game. |
| Approach: | They propose an approach that integrates a deterministic Offer Generator and an LLM Narrator to create natural language sentences for generated offers. |
| Outcome: | The proposed approach improves the buyer’s deal rates from 26.67% to 88.88% and brings a ten times multiplication of profits on all baselines, even a model that has not been aligned. |
Facilitating Long Context Understanding via Supervised Chain-of-Thought Reasoning (2025.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have enabled them to process increasingly longer sequences, ranging from 2K to 2M tokens and even beyond. |
| Approach: | They propose a synthetic dataset in the financial domain that integrates Chain-of-Thought reasoning into LLMs in a supervised manner to facilitate effective long-context understanding. |
| Outcome: | The proposed model outperforms standard GPT-4o-mini on the Loong benchmark and fine tunes LLaMA-3.1-8B-Instruct on the model, achieving a 28.0% gain on the financial subset. |
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture (2023.findings-emnlp)
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Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo Wang
| Challenge: | Existing low-resource learning techniques focus on label annotation while neglecting the natural language explanation of a data point. |
| Approach: | They propose a novel architecture that leverages an explanation-generation model to produce explanations guided by human explanations and a prediction model that utilizes generated explanations toward prediction faithfully. |
| Outcome: | The proposed architecture produces explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a data diversity-based AL sampling strategy that benefits from the explanation annotations. |
Learning Adaptive Segmentation Policy for End-to-End Simultaneous Translation (2022.acl-long)
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| Challenge: | Existing methods to perform simultaneous speech-to-text translation ignore contextual information and suffer from low translation quality. |
| Approach: | They propose an adaptive segmentation policy for simultaneous speech-to-text translation . it learns to segment the source streaming speech into meaningful units . |
| Outcome: | The proposed method achieves a good accuracy-latency trade-off over state-of-the-art methods on English-German and Chinese-English. |
Attention Consistency for LLMs Explanation (2025.findings-emnlp)
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| Challenge: | Existing interpretability methods face limitations such as low resolution and high computational cost. |
| Approach: | They propose a multi-layer attention consistency score to estimate the importance of input tokens in large language models. |
| Outcome: | The proposed heuristic achieves a favorable trade-off between interpretability quality and computational efficiency . |
Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory (2025.acl-long)
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| Challenge: | Recent studies have shown that scaling test-time compute can also effectively improve reasoning. |
| Approach: | They propose a probabilistic method to efficiently predict scaling performance and identify the best prompting strategy under large sampling times. |
| Outcome: | The proposed method significantly improves the scaling performance of majority voting on large language models. |
AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks (2023.findings-acl)
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Xinyi He, Mengyu Zhou, Mingjie Zhou, Jialiang Xu, Xiao Lv, Tianle Li, Yijia Shao, Shi Han, Zejian Yuan, Dongmei Zhang
| Challenge: | Tabular data analysis is performed everyday across various domains. |
| Approach: | They propose to use a dataset of 467k tables with supervision labels for four types of field metadata. |
| Outcome: | The proposed framework improves the understanding capability of tabular models by incorporating distribution and knowledge information. |
Neural Incompatibility: The Unbridgeable Gap of Cross-Scale Parametric Knowledge Transfer in Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have a transparent brain with accessible parameters that encode extensive knowledge, which can be analyzed, located and transferred. |
| Approach: | They propose a new paradigm that aligns parametric spaces of LLMs using several training steps without following training. |
| Outcome: | The proposed model aligns parametric spaces across scales using only training steps without following training. |
BBTv2: Towards a Gradient-Free Future with Large Language Models (2022.emnlp-main)
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| Challenge: | Recent work on parameter-efficient tuning (PET) only tunes a small portion of parameters while keeping most of the parameters of the LLM unchanged. |
| Approach: | They propose an improved version of Black-Box Tuning to tune PTMs through gradient descent . they prepend continuous prompts to every layer of the PTM and propose a divide-and-conquer gradient-free algorithm to optimize the prompts alternately. |
| Outcome: | The proposed method achieves comparable performance to full model tuning and state-of-the-art parameter-efficient methods under few-shot settings while maintaining much fewer tunable parameters. |
Greenback Bears and Fiscal Hawks: Finance is a Jungle and Text Embeddings Must Adapt (2024.emnlp-industry)
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| Challenge: | Financial documents are filled with specialized terminology, arcane jargon, and curious acronyms that pose challenges for general-purpose text embeddings. |
| Approach: | They propose to fine tune financial text embeddings finetuned on a carefully constructed dataset of 14.3M query-passage pairs including both public and proprietary financial documents. |
| Outcome: | The proposed embeddings achieve Recall@1 of 62.8% on a held-out test set, vs. only 39.2% for the best general-purpose text embeddING from OpenAI. |
Natural Language to Structured Query Generation via Meta-Learning (N18-2)
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| Challenge: | Conventional supervised training is a pervasive paradigm for NLP problems . however, examples of the same problem may vary widely . a few-shot meta-learning scenario is used to learn multiple models . |
| Approach: | They propose a learning protocol that treats each example as a unique pseudo-task . they use a few-shot meta-learning scenario to reduce the original learning problem to a single example . |
| Outcome: | The proposed learning protocol achieves 1.1%–5.4% accuracy gains over non-meta-learning counterparts on a WikiSQL dataset. |
Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction (2021.acl-long)
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| Challenge: | Existing models for ECE tend to explore relative position information and suffer from the dataset bias. |
| Approach: | They propose to generate adversarial examples where relative position is no longer indicative feature of cause clauses to address the dataset bias. |
| Outcome: | The proposed method performs on par with existing state-of-the-art methods on the original ECE dataset and is more robust against adversarial attacks compared to existing models. |
AudioPrivacy: Parallel Audio Dataset for Speaker Profiling with Diverse Audio Types and Rich Attributes (2026.findings-acl)
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| Challenge: | Speech signals convey abundant speaker-related metadata, yet current privacy research focuses on identity-centric voiceprint protection, leaving sensitive Speaker Attribute Privacy (SAP) underexplored. |
| Approach: | They propose a large-scale Chinese dataset to evaluate speaker-related privacy leakage . the dataset includes 227.3 hours of audio from 1,000 speakers . |
| Outcome: | The proposed model systematically evaluates speaker-related privacy leakage in everyday scenarios. |
Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring (2025.naacl-long)
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Honglin Mu, Han He, Yuxin Zhou, Yunlong Feng, Yang Xu, Libo Qin, Xiaoming Shi, Zeming Liu, Xudong Han, Qi Shi, Qingfu Zhu, Wanxiang Che
| Challenge: | Existing black-box jailbreak methods often rely on model feedback . existing methods may be intercepted by content moderators during the search process . |
| Approach: | They propose a method that guides malicious prompt construction by local training a mirror model of the target black-box model through benign data distillation. |
| Outcome: | The proposed method achieves a 92% attack success rate and 80% stealth rate on a subset of AdvBench. |
When and How to Augment Your Input: Question Routing Helps Balance the Accuracy and Efficiency of Large Language Models (2025.findings-naacl)
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| Challenge: | augmented generation of knowledge-based long-tail questions can be useful for large language models, but can cause significant latency. |
| Approach: | They propose an adaptive question routing framework that uses a query router to augment input to the right time. |
| Outcome: | The proposed framework surpasses existing approaches in accuracy and efficiency on benchmarks such as AmbigNQ, HotpotQA, MMLU-STEM, and PopQA. |
EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents (2025.acl-long)
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Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, Heng Ji
| Challenge: | Existing language model agents excel in planning and reasoning, but lack creativity in unfamiliar environments. |
| Approach: | They propose a benchmark suite of room escape game environments to challenge agents with creative reasoning, unconventional tool use and iterative problem-solving to uncover implicit goals. |
| Outcome: | The proposed framework can perform with 40% fewer steps and hints and performs robustly across difficulty levels. |
RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering (2025.findings-emnlp)
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| Challenge: | Existing retrieval approaches often overlook patient-specific factual knowledge embedded in EHRs . existing retrieval frameworks often overlook this factual information, limiting its effectiveness in clinical decision-making. |
| Approach: | They propose a recurrence generation-augmented retrieval framework that synergizes factual and conceptual knowledge from dual sources. |
| Outcome: | The proposed framework improves on factual-aware medical QA benchmarks. |
LatticeGen: Hiding Generated Text in a Lattice for Privacy-Aware Large Language Model Generation on Cloud (2024.findings-naacl)
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Mengke Zhang, Tianxing He, Tianle Wang, Lu Mi, Niloofar Mireshghallah, Binyi Chen, Hao Wang, Yulia Tsvetkov
| Challenge: | Currently, the server controls the generated text, but users can't keep it private . prompted generation is a common interaction paradigm for large language models on cloud . |
| Approach: | They propose a protocol where the server handles most of the computation while the client controls the sampling operation. |
| Outcome: | The proposed protocol protects both prompt and generation under strong attacks. |
LongHeads: Multi-Head Attention is Secretly a Long Context Processor (2024.findings-emnlp)
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| Challenge: | Large language models struggle to process lengthy inputs due to limited length generalization and attention’s quadratic computational demands. |
| Approach: | They propose a training-free framework that allows each head to attend to important context chunks instead of allowing each head a full sentence . |
| Outcome: | The proposed framework unlocks multi-head attention's untapped potential by allowing each head to attend to important context chunks instead of the full sentence. |
POP: Prefill-Only Pruning for Efficient Large Model Inference (2026.findings-acl)
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| Challenge: | Existing structured pruning methods suffer from significant accuracy degradation . Existing pruning methods are expensive and require specialized hardware and kernels to perform . |
| Approach: | They propose a stage-agnostic pruning approach that overlooks asymmetric roles between prefill and decode stages. |
| Outcome: | The proposed pruning approach achieves 1.37 speedup in prefill latency with minimal performance loss. |
PAD-Net: An Efficient Framework for Dynamic Networks (2023.acl-long)
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| Challenge: | Dynamic networks can significantly improve the model’s representation power with acceptable computational cost. |
| Approach: | They propose a partially dynamic network to transform redundant dynamic parameters into static ones and iterative mode partition to partition dynamic and static parameters efficiently. |
| Outcome: | The proposed network surpasses fully dynamic networks by +0.7% top-1 acc with only 30% dynamic parameters for DY-Conv and +1.9% average score in language understanding with only 50% dynamic parameters. |
Pattern-revising Enhanced Simple Question Answering over Knowledge Bases (C18-1)
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| Challenge: | Simple question answering over knowledge bases is one of the most important natural language processing tasks. |
| Approach: | They propose to conduct pattern extraction and entity linking first and put forward pattern revising procedure to mitigate the error propagation problem. |
| Outcome: | The proposed method outperforms the current state-of-the-art in this task by an absolute large margin. |
LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have been successful in understanding language and processing text, but their cost prohibits their practical applications. |
| Approach: | They propose a multi-agent collaboration method that breaks down lengthy documents into smaller, more manageable chunks and organizes the member agents to read their assigned chunks. |
| Outcome: | The proposed method achieves 16.42% and 1.63% accuracy gains over existing models on single-hop and multi-hop QA settings. |
CAPE: A Chinese Dataset for Appraisal-based Emotional Generation in Large Language Models (2025.findings-naacl)
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| Challenge: | Existing LLMs fail to capture the nuances of human emotions, making their interactions seem impersonal or inadequate. |
| Approach: | They propose a two-stage automatic data generation framework to generate a Chinese dataset called CAPE . their data is a cognitive appraisal theory-based Emotional corpus that accounts for personal and situational factors. |
| Outcome: | The proposed framework can generate human-like responses in conversation with large language models. |
An Empirical Study on Neural Keyphrase Generation (2021.naacl-main)
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| Challenge: | Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. |
| Approach: | They propose to compare the generalizability of KPG models with other models by analyzing the most crucial factors that may affect their generalizarability. |
| Outcome: | The proposed model can be used to predict keyphrases from a set of input sequences, and it can be compared with existing models. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions? |
| Approach: | They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. |
| Outcome: | The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure. |
Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents (2026.acl-long)
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Yuanchen Bei, Tianxin Wei, Xuying Ning, Yanjun Zhao, Zhining Liu, Xiao Lin, Yada Zhu, Hendrik Hamann, Jingrui He, Hanghang Tong
| Challenge: | Existing benchmarks evaluate multi-session memory in text-only conversations or assess multimodal understanding within localized contexts. |
| Approach: | They propose a benchmark for evaluating multimodal long-term conversational memory in MLLM agents. |
| Outcome: | The proposed framework assesses key memory capabilities along three functional dimensions: memory extraction and test-time adaptation, memory reasoning, and memory knowledge management. |
Towards semantic reliable clinical QA: Query pipeline optimization for cancer patient question answering systems (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are promising for medical Question-Answering but suffer from hallucinations that jeopardize patient safety. |
| Approach: | They propose a three-level controllable metadata-aware framework optimized for Cancer Patient QA (CPQA) they propose combining semantic retrieval with real-time Boolean search to overcome metadata blindness. |
| Outcome: | The proposed framework improves the answer accuracy of Claude-3-haiku by 5.24% over chain-of-thought prompting and about 3% over a naive RAG setup. |
Controllable Dictionary Example Generation: Generating Example Sentences for Specific Targeted Audiences (2022.acl-long)
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| Challenge: | Traditionally, example sentences are created by linguistics experts, which are labor-intensive and knowledge-intensive . linguistic experts create dictionary examples for polysemous words, which can be difficult for polygraphs . authors propose a model that generates reasonable examples for targeted words . |
| Approach: | They propose a controllable dictionary example sentence generation model that generates appropriate examples for targeted words . model allows users to provide explicit control over attributes related to readability . |
| Outcome: | The proposed model generates reasonable examples for targeted words even for polysemous words while providing explicit control over readability attributes. |
Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders (2023.findings-emnlp)
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| Challenge: | Existing methods to rank sentences using pre-trained embeddings create a gap due to different optimization objectives. |
| Approach: | They propose a pre-trained embedding process that optimizes informative sentences . they use sentence-word bipartite graphs to model intra-sentential distinctive features . |
| Outcome: | The proposed model outperforms heavy BERT- or RoBERTa-based sentence ranking methods by providing summary-worthy representations. |
Understanding Client Reactions in Online Mental Health Counseling (2023.acl-long)
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| Challenge: | Communication success relies heavily on reading participants’ reactions, but little research is on how listeners' reactions shape trajectories and outcomes of conversations. |
| Approach: | They propose to use client reactions to predict counseling outcomes by using an annotation framework that encompasses counselors’ strategies and client reaction behaviors. |
| Outcome: | The proposed framework can predict counselors' strategies and client reaction behaviors against a large-scale text-based counseling dataset. |
Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering (2025.coling-main)
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| Challenge: | Recent studies indicate that Large Language Models model rich knowledge, but it is often not activated and awakened. |
| Approach: | They propose a framework that leverages richer context to enhance question answering . Explicit awakening fine-tunes a context generator to create a synthetic, compressed document that functions as symbolic context. |
| Outcome: | The proposed framework mimics the human ability to answer questions using only thinking and recalling to compensate for knowledge gaps. |
Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)
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Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu
| Challenge: | In the evolving landscape of large language models, the predominant focus has been on English and Chinese. |
| Approach: | They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding. |
| Outcome: | The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks. |
The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | a test suite to evaluate commonsense reasoning capability of neural machine translation is presented . language models pretrained on large-scale corpora achieve a commonsensing accuracy of lower than 72% on target translations of this test suite. |
| Approach: | They propose a test suite to evaluate the commonsense reasoning capability of neural machine translation. |
| Outcome: | The proposed test suite performs poorly on commonsense reasoning of the three ambiguity types in terms of reasoning accuracy and reasoning consistency. |
Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection (2024.findings-acl)
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Ruibo Chen, Yihan Wu, Lichang Chen, Guodong Liu, Qi He, Tianyi Xiong, Chenxi Liu, Junfeng Guo, Heng Huang
| Challenge: | Existing data selection methods for instruction-following large language models rely on unreliable scores or use downstream tasks for selection. |
| Approach: | They propose a method that utilizes the VLM itself as a filter to select high-quality instruction-tuning data. |
| Outcome: | The proposed method can reach better results compared to full data settings with merely about 15% samples and can achieve superior performance against competitive baselines. |
FacLens: Transferable Probe for Foreseeing Non-Factuality in Fact-Seeking Question Answering of Large Language Models (2025.emnlp-main)
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| Challenge: | Existing non-factuality detection methods require response generation, which incurs significant computational overhead. |
| Approach: | They propose a lightweight model called Factuality Lens which effectively probes hidden representations of fact-seeking questions for the NFP task. |
| Outcome: | The proposed model is able to probe hidden representations of fact-seeking questions and reduce development costs. |
PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation (2022.findings-aacl)
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Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhihua Wu, Zhen Guo, Hua Lu, Xinxian Huang, Xin Tian, Xinchao Xu, Yingzhan Lin, Zheng-Yu Niu
| Challenge: | Experimental results show PLATO-XL achieves state-of-the-art results across multiple conversational tasks. |
| Approach: | They propose to train PLATO-XL models with up to 11 billion parameters, trained on Chinese and English social media conversations. |
| Outcome: | The proposed model achieves state-of-the-art on multiple conversational tasks, verifying its potential as a foundation model of conversational AI. |
EnigmaToM: Improve LLMs’ Theory-of-Mind Reasoning Capabilities with Neural Knowledge Base of Entity States (2025.findings-acl)
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| Challenge: | Existing ToM reasoning methods rely excessively on off-the-shelf LLMs, reducing their efficiency and limiting their applicability to high-order ToM. |
| Approach: | They propose a neuro-symbolic framework that integrates a Neural Knowledge Base of Entity States and knowledge injection to enhance ToM reasoning. |
| Outcome: | The proposed framework improves ToM reasoning on ToMi, HiToM, and FANToM benchmarks. |
ART: rule bAsed futuRe-inference deducTion (2023.emnlp-main)
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Mengze Li, Tianqi Zhao, Bai Jionghao, Baoyi He, Jiaxu Miao, Wei Ji, Zheqi Lv, Zhou Zhao, Shengyu Zhang, Wenqiao Zhang, Fei Wu
| Challenge: | Existing studies focus on language-based premises and deduce valid conclusions from visual observations. |
| Approach: | They propose a rule-based deductive reasoning task that uses video to deduce the correct future event . they use commonsense knowledge to annotate video and a strong baseline to conduct reasoning . |
| Outcome: | Empirical studies validate the rationality of ARTNet in deductive reasoning upon visual observations . ART is a method that rigorously follows a set of explicit constraints to deduce valid conclusions from empirical facts . |
REST: Retrieval-Based Speculative Decoding (2024.naacl-long)
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| Challenge: | Retrieval-based speculative decoding (REST) is a new language model generation algorithm . it uses existing knowledge to generate draft tokens, allowing for seamless integration and acceleration of any language model. |
| Approach: | They propose a new algorithm that uses a draft language model to generate tokens from existing knowledge. |
| Outcome: | The proposed method achieves a speedup of 1.62 to 2.36 on code or text generation. |
PersLEARN: Research Training through the Lens of Perspective Cultivation (2023.acl-demo)
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Yu-Zhe Shi, Shiqian Li, Xinyi Niu, Qiao Xu, Jiawen Liu, Yifan Xu, Shiyu Gu, Bingru He, Xinyang Li, Xinyu Zhao, Zijian Zhao, Yidong Lyu, Zhen Li, Sijia Liu, Lin Qiu, Jinhao Ji, Lecheng Ruan, Yuxi Ma, Wenjuan Han, Yixin Zhu
| Challenge: | PersLEARN is a tool designed to facilitate the cultivation of scientific perspectives . junior researchers struggle to identify the perspectives reflected in the literature and struggle to develop their own viewpoints. |
| Approach: | They propose a tool to facilitate the cultivation of scientific perspectives by interacting with a prompt-based model and allowing students to develop their own perspectives explicitly. |
| Outcome: | The proposed tool outperforms baseline approaches across multiple domains of literature from different perspectives. |
CMHKF: Cross-Modality Heterogeneous Knowledge Fusion for Weakly Supervised Video Anomaly Detection (2025.acl-long)
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| Challenge: | Existing methods focus mainly on visual modalities, neglecting rich multi-modality information. |
| Approach: | They propose a framework that integrates cross-modality knowledge from video, audio and text to improve anomaly detection and localization. |
| Outcome: | The proposed framework improves detection and localization of anomalies using video-level labels. |
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained Relationships (2021.findings-emnlp)
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| Challenge: | Existing knowledge-enhanced pretrained language models focus on entity information and ignore fine-grained relationships between entities. |
| Approach: | They propose to incorporate KG into the language learning process to obtain a KG-enhanced pretrained Language Model. |
| Outcome: | The proposed model improves on several knowledge-driven tasks, such as entity typing and relation classification, compared with the state-of-the-art knowledge-enhanced PLMs. |
PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs (2023.emnlp-main)
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Rahul Goel, Waleed Ammar, Aditya Gupta, Siddharth Vashishtha, Motoki Sano, Faiz Surani, Max Chang, HyunJeong Choe, David Greene, Chuan He, Rattima Nitisaroj, Anna Trukhina, Shachi Paul, Pararth Shah, Rushin Shah, Zhou Yu
| Challenge: | PRESTO dataset contains 550K contextual multilingual conversations between humans and virtual assistants. |
| Approach: | They propose to use a dataset of 550K contextual multilingual conversations between humans and virtual assistants to study some of the more challenging aspects of parsing realistic conversations. |
| Outcome: | The dataset contains 550K contextual conversations between humans and virtual assistants. |
WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models (2024.acl-long)
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| Challenge: | Existing web agents only handle one input modality and are evaluated only in simplified web simulators or static web snapshots, greatly limiting their applicability in real-world scenarios. |
| Approach: | They propose a large multimodal model-powered web agent that can complete user instructions end-to-end by interacting with real-world websites. |
| Outcome: | The proposed agent achieves 59.1% task success rate, surpassing both GPT-4 and WebVoyager setups. |
Lunar-Bench: Towards Evaluating Task-Oriented Reasoning of LLMs in Lunar Exploration Scenarios (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on static, context-independent reasoning tasks and fail to capture constraints and dependencies of lunar missions. |
| Approach: | They propose a benchmark to assess the task-oriented reasoning and decision-making performance of large language models through 3,000 tasks derived from mission procedures and documentation. |
| Outcome: | The proposed model achieves 47.8% accuracy compared with 65.1% for human experts on 36 representative missions. |
Exploring Memorization in Fine-tuned Language Models (2024.acl-long)
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Shenglai Zeng, Yaxin Li, Jie Ren, Yiding Liu, Han Xu, Pengfei He, Yue Xing, Shuaiqiang Wang, Jiliang Tang, Dawei Yin
| Challenge: | Existing studies have shown that pre-trained langauge models tend to memorize and regenerate segments of their pre-training corpus when prompted appropriately. |
| Approach: | They conduct the first comprehensive analysis to explore language models’ memorization during fine-tuning across tasks. |
| Outcome: | The proposed analysis shows that memorization presents a strong disparity among different fine-tuning tasks. |
Large Language Models Can Not Perform Well in Understanding and Manipulating Natural Language at Both Character and Word Levels? (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) still exhibit significant deficiencies in basic language understanding and manipulation. |
| Approach: | They propose a bilingual benchmark to assess the performance of Large language models . they use a set of 15 simple text editing tasks to examine their capabilities . |
| Outcome: | The proposed benchmark aims to assess the performance of Large language models in basic language tasks. |
Exploring Logically Dependent Multi-task Learning with Causal Inference (2020.emnlp-main)
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| Challenge: | Hierarchical multi-task learning models can utilize task dependencies by stacking encoders and outperform democratic ones. |
| Approach: | They propose a model that utilizes the labels of all lower-level tasks and a Gumbel sampling model to deal with cascading errors. |
| Outcome: | The proposed model outperforms democratic models on six out of seven subtasks and achieves state-of-the-art on the two English and one Chinese datasets. |
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training (2020.coling-main)
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| Challenge: | Existing few-shot relation classifiers struggle to distinguish them with few annotated instances due to high co-occurrence of some relations . |
| Approach: | They propose a few-shot relation classification model with two mechanisms to decouple easily-confused relations. |
| Outcome: | The proposed model achieves comparable and even better results to strong baselines in terms of accuracy. |
Industry Scale Semi-Supervised Learning for Natural Language Understanding (2021.naacl-industry)
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| Challenge: | Obtaining human annotation is expensive and time-consuming process. |
| Approach: | They propose a semi-supervised learning pipeline which leverages millions of unlabeled examples to improve natural language understanding tasks. |
| Outcome: | The proposed pipeline can be used to improve natural language understanding tasks. |
UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model (2023.findings-emnlp)
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Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, Qin Jin, Liang He, Xin Lin, Fei Huang
| Challenge: | Existing studies for visually-situated language understanding have shown shallow zero-shot visual text recognition ability when fed a low-resolution image with salient text information. |
| Approach: | They propose a model for universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM) their model is jointly finetuned on a wide range of visually situated language understanding tasks via a unified instruction format. |
| Outcome: | The proposed model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks across 5 domains: documents, tables, charts, natural images, and webpage screenshots. |
Weighted self Distillation for Chinese word segmentation (2022.findings-acl)
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| Challenge: | Recent researches show that multi-criteria resources and n-gram features are beneficial to Chinese word segmentation (CWS). |
| Approach: | They propose a framework that uses weighted self distillation to learn Chinese word segmentation using unigram features. |
| Outcome: | The proposed framework achieves state-of-the-art or competitive performance on SIGHAN Bakeoff datasets. |
RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis (2025.findings-acl)
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Pengzuo Wu, Yuhang Yang, Guangcheng Zhu, Chao Ye, Hong Gu, Xu Lu, Ruixuan Xiao, Bowen Bao, Yijing He, Liangyu Zha, Wentao Ye, Junbo Zhao, Haobo Wang
| Challenge: | Existing benchmarks for large language models focus on simple, flat table structures. |
| Approach: | They propose a benchmark to evaluate the performance of both Large Language Models and Multimodal LLMs across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs and Multimodal LLM models across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
ARCH: Efficient Adversarial Regularized Training with Caching (2021.findings-emnlp)
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Simiao Zuo, Chen Liang, Haoming Jiang, Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Tuo Zhao
| Challenge: | Existing approaches to regularize models require generating a perturbation for each sample in each epoch. |
| Approach: | They propose an adversarial regularization method where perturbations are generated and cached once every several epochs. |
| Outcome: | The proposed method significantly eases the computational burden (saves up to 70% of computational time) it produces a notably better (in most of the tasks) or comparable model generalization. |
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)
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| Challenge: | Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance. |
| Approach: | They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities. |
| Outcome: | The proposed framework outperforms baseline methods on low-resource tasks. |
Conformalized Answer Set Prediction for Knowledge Graph Embedding (2025.naacl-long)
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| Challenge: | Knowledge graph embeddings (KGE) map entities and predicates into numerical vectors, providing non-classical reasoning capabilities based on similarities and analogies between entities and relations. |
| Approach: | They propose to use knowledge graph embeddings to provide non-classical reasoning capabilities by exploiting similarities and analogies between entities and relations. |
| Outcome: | The proposed model can generate answer sets with probabilistic guarantees on four benchmark datasets and is scaled well with respect to the difficulty of the query. |
PsychEval: A Multi-Session and Multi-Therapy Benchmark for High-Realism AI Psychological Counselor (2026.findings-acl)
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Qianjun Pan, Junyi Wang, Jie Zhou, Yutao Yang, Junsong Li, Kaiyin Xu, Yougen Zhou, Yihan Li, JingYuan Zhao, Qin Chen, Ningning Zhou, Kai Chen, Liang He
| Challenge: | Existing models focus on a single therapy, but complex cases require flexible strategies among various therapies. |
| Approach: | They propose a multi-session, multi-therapy, and highly realistic benchmark . it is designed to address three key challenges: 1) can we train a highly realistic AI counselor? 2) How to systematically evaluate an AI counselor?" |
| Outcome: | The proposed benchmark is annotated with extensive professional skills and includes over 677 meta-skills and 4577 atomic skills. |
CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation (2025.naacl-short)
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| Challenge: | Existing methods to ground large language models fail to adequately attend to all contexts . position bias is hindered by retrieval-augmented generation, which requires constant attention . |
| Approach: | They propose to augment and distill training instances with their perturbed positions to encourage consistent predictions . they also propose to balance COnsistency and Rank Distillation by combining noise-controlled perturbations with augmentation and distillation. |
| Outcome: | The proposed method outperforms existing methods in diverse RAG benchmarks. |
An MRC Framework for Semantic Role Labeling (2022.coling-1)
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| Challenge: | Existing work on semantic role labels ignores the semantic connection between the two tasks . et al. (2010) defined two types of semantic roles: core roles and non-core roles. |
| Approach: | They propose to use machine reading comprehension to bridge the gap between these two tasks . they formalize predicate disambiguation as multiple-choice machine reading understanding . |
| Outcome: | The proposed framework achieves state-of-the-art or comparable results to previous work . it uses the descriptions of candidate senses of a given predicate as options to select the correct sense . |
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing methods for Named Entity Recognition (NER) use a similarity metric to measure semantic similarity between test samples and referents, but their performance is limited due to the label scarcity. |
| Approach: | They propose a novel approach to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an entity class. |
| Outcome: | The proposed approach outperforms state-of-the-art models with a significant margin in most cases. |
Audio MultiChallenge: A Multi-Turn Evaluation of Spoken Dialogue Systems on Natural Human Interaction (2026.acl-long)
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Advait Gosai, Tyler Vuong, Utkarsh Tyagi, Steven Li, Wenjia You, Miheer Bavare, Arda Uçar, Zhongwang Fang, Brian Jang, Bing Liu, Yunzhong He
| Challenge: | End-to-end (E2E) spoken dialogue systems are replacing cascaded pipelines for voice-based human-AI interaction. Existing benchmarks evaluate these systems on synthetic speech and single-turn tasks, leaving multi-turn conversational ability underexplored. |
| Approach: | They propose an open-source benchmark to evaluate spoken dialogue systems under natural multi-turn interaction patterns. |
| Outcome: | The proposed model fails on the highest-performing model with 54.65% pass rate. |
Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional proficiency in instruction-following, making them increasingly integral to various applications. |
| Approach: | They establish a benchmark to evaluate the robustness of instruction-following LLMs against prompt injection attacks, assessing their ability to discern which instructions to follow and which to disregard. |
| Outcome: | The proposed model is overly sensitive to prompt injection attacks, focusing on the latter part of the prompt without fully understanding the context. |
Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona (2023.acl-long)
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| Challenge: | Existing personalized dialogue agents model persona profiles from sparse or dense persona descriptions and dialogue histories. |
| Approach: | They propose a model that clusters dense persona descriptions into sparse categories and generates personalized responses from dialogue histories. |
| Outcome: | The proposed model improves on Chinese and English datasets. |
On the Perception Bottleneck of VLMs for Chart Understanding (2025.findings-emnlp)
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| Challenge: | a perception bottleneck in large vision-language models is critical for chart understanding . instruction tuning improves the extraction capability of LVLMs, but the vision encoder remains a bottleneck . |
| Approach: | They propose to decompose the perception bottleneck into two components . the vision encoder bottleneck is where visual representation fails to encapsulate the correct information . |
| Outcome: | The proposed approach significantly mitigates the vision encoder bottleneck and improves the ability of LVLMs to comprehend charts. |
TamEdit: Trajectory-Aware Meta-Learning for Specificity-Preserving Continual Knowledge Editing (2026.acl-long)
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| Challenge: | Existing methods for continual knowledge editing focus on single edits or preventing knowledge forgetting. |
| Approach: | They propose a meta-learning method that preserves specificity for continual knowledge editing by capturing relationships between different single edits within the trajectory. |
| Outcome: | Experiments show that TamEdit outperforms baselines in continual editing while preserving general capabilities. |
Not All Terms Matter: Recall-Oriented Adaptive Learning for PLM-aided Query Expansion in Open-Domain Question Answering (2025.acl-long)
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| Challenge: | Open-domain question answering (ODQA) systems typically adopt a retriever-reader architecture, where the retriever finds relevant documents, and the reader extracts or synthesizes answers. |
| Approach: | They propose a method that iteratively adjusts the importance weights of QE terms based on their relevance, refining term distinction and enhancing the separation of relevant terms. |
| Outcome: | The proposed method improves retrieval accuracy and overall performance on four ODQA datasets and five QE methods. |
The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) (2024.findings-acl)
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Shenglai Zeng, Jiankun Zhang, Pengfei He, Yiding Liu, Yue Xing, Han Xu, Jie Ren, Yi Chang, Shuaiqiang Wang, Dawei Yin, Jiliang Tang
| Challenge: | Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model generation with proprietary and private data, where data privacy is . a privacy issue that is currently under-explored, is posed by RAG. |
| Approach: | They propose to use retrieval-augmented generation (RAG) to facilitate language model generation with proprietary and private data where data privacy is a pivotal concern. |
| Outcome: | The proposed attack methods demonstrate that RAG can mitigate the old risks, i.e., leakage of the LLMs’ training data. |
MixRED: A Mix-lingual Relation Extraction Dataset (2024.lrec-main)
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| Challenge: | Existing research focuses on monolingual relation extraction, but there is a significant gap in understanding relation extraction in the mix-lingual scenario. |
| Approach: | They propose a task of considering relation extraction in the mix-lingual scenario . they construct a human-annotated dataset to support the task . |
| Outcome: | The proposed task evaluates state-of-the-art supervised models and large language models on the human-annotated dataset MixRED. |
Evaluating LLMs’ Assessment of Mixed-Context Hallucination Through the Lens of Summarization (2025.findings-acl)
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| Challenge: | Large language models generate coherent text and follow instructions across diverse tasks, but a critical challenge in scaling LLM applications is hallucination, where the generated content lacks factual grounding or deviates from the intended discourse context. |
| Approach: | They use summarization as a representative task to evaluate LLMs' capability in detecting mixed-context hallucinations, specifically distinguishing between factual and non-factual hallucinos. |
| Outcome: | The proposed model distinguishes between factual and non-factual hallucinations, and their performance bottlenecks. |
Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions (2022.emnlp-industry)
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Rindra Ramamonjison, Haley Li, Timothy Yu, Shiqi He, Vishnu Rengan, Amin Banitalebi-dehkordi, Zirui Zhou, Yong Zhang
| Challenge: | Existing systems for operations research use NLP to suggest formulations of optimization problems. |
| Approach: | They propose an augmented intelligence system that can be used to simplify and enhance the modeling experience for operations research. |
| Outcome: | The proposed system validates and edits the proposed formulations with a dataset of linear programming problems drawn from various application domains. |
TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph (2022.coling-1)
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| Challenge: | Existing target-oriented dialogs take a local and greedy strategy for response generation, where global planning is absent. |
| Approach: | They propose a global planning method for target-oriented dialog on a commonsense knowledge graph to adjust local response generation towards the global target. |
| Outcome: | The proposed method can reach the target with a higher success rate, fewer turns, and more coherent responses. |
Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement (2025.emnlp-industry)
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Haotan Guo, Jianfei He, Jiayuan Ma, Hongbin Na, Zimu Wang, Haiyang Zhang, Qi Chen, Wei Wang, Zijing Shi, Tao Shen, Ling Chen
| Challenge: | Phonetic Cloaking Replacement (PCR) is a problem in content moderation in China. |
| Approach: | They organize PCR into a four-way surface-form taxonomy and compile PCR-ToxiCN, a dataset of 500 phonetically cloaked offensive posts gathered from the RedNote platform. |
| Outcome: | The proposed model achieves only an F1-score and zero-shot chain-of-thought prompting pushes performance even lower. |
Open Event Extraction from Online Text using a Generative Adversarial Network (D19-1)
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| Challenge: | Existing approaches to extract structured representations of open-domain events are limited . a recent study shows that the model outperforms the baseline approaches for extracting events from online texts . |
| Approach: | They propose an event extraction model based on Generative Adversarial Nets which captures latent events with a generator network and a discriminator to distinguish documents reconstructed from latent and original events. |
| Outcome: | The proposed model outperforms baseline models on two Twitter and a news article datasets. |
Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations (2026.acl-long)
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| Challenge: | Prior work has found that explanations can easily convince users that inaccurate VLM predictions are correct. |
| Approach: | They propose to evaluate two complementary qualities of VLM-generated explanations via two quality scoring functions to improve their accuracy. |
| Outcome: | The proposed explanations improve accuracy on the A-OKVQA, VizWiz, and MMMU-Pro tasks by 11.1%, including a 15.4% reduction in falsely believing incorrect predictions. |
Finding the Dominant Winning Ticket in Pre-Trained Language Models (2022.findings-acl)
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| Challenge: | Existing studies on pre-trained language models show that they can fine-tune parameters but achieve good downstream performance. |
| Approach: | They find that a dominant winning ticket takes up 0.05% of the parameters and is transferable across different tasks. |
| Outcome: | The proposed model can achieve comparable performance with the full-parameter model, the authors show . the dominant winning ticket takes up 0.05% of the parameters, and the model is transferable across tasks, they show - the authors conclude . |
COPR: Continual Human Preference Learning via Optimal Policy Regularization (2025.findings-acl)
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Han Zhang, Lin Gui, Yu Lei, Yuanzhao Zhai, Yehong Zhang, Zhuo Zhang, Yulan He, Hui Wang, Yue Yu, Kam-Fai Wong, Bin Liang, Ruifeng Xu
| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is effective for aligning Large Language Models with human preferences, but its complex process limits its ability to continually learn human feedback. |
| Approach: | They propose a non-RL offline method to convert historical optimal policies into optimization constraints when continually learning new preferences. |
| Outcome: | The proposed method outperforms strong CL baselines in terms of reward-based evaluations and human assessment. |
Should I Believe in What Medical AI Says? A Chinese Benchmark for Medication Based on Knowledge and Reasoning (2025.acl-short)
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| Challenge: | Large language models (LLMs) generate hallucinations when handling unfamiliar information. |
| Approach: | They propose a Chinese benchmark to evaluate large language models' knowledge and reasoning capabilities in medication tasks. |
| Outcome: | The proposed benchmark evaluates models in indication, dosage and administration, contraindicated population, mechanisms of action, drug recommendation, and drug interaction across six datasets. |
Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer (2025.acl-long)
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Guodong Du, Zitao Fang, Jing Li, Junlin Li, Runhua Jiang, Shuyang Yu, Yifei Guo, Yangneng Chen, Sim Kuan Goh, Ho-Kin Tang, Daojing He, Honghai Liu, Min Zhang
| Challenge: | Foundational models and their checkpoints have advanced deep learning, boosting performance across applications. |
| Approach: | They propose a method for pruning fine-tuned models by calculating differences between them and original model. |
| Outcome: | The proposed method can improve performance across vision, NLP, and multi-modal benchmarks. |
Prompt Optimization via Adversarial In-Context Learning (2024.acl-long)
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Do Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Zhao, Nancy Chen, Kenji Kawaguchi, Michael Shieh, Junxian He
| Challenge: | Existing methods to optimize prompts for in-context learning are based on adversarial learning and are computationally efficient and extensible to other LLMs and tasks. |
| Approach: | They propose a method to optimize prompts for in-context learning by a generator and a discriminator. |
| Outcome: | The proposed method improves state-of-the-art prompt optimization techniques on 13 generation and classification tasks including summarization, arithmetic reasoning, machine translation, data-to-text generation, and the MMLU and big-bench hard benchmarks. |
DSCD: Large Language Model Detoxification with Self-Constrained Decoding (2025.emnlp-main)
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| Challenge: | Existing methods for decoding large language models (LLMs) are based on external constraints and require additional resource overhead and loss of generation fluency. |
| Approach: | They propose a method for LLMs detoxification without parameter fine-tuning that strengthens the inner token distribution while weakening that of hallucination and toxic layer during output generation. |
| Outcome: | Extensive experiments on open-source LLMs and public datasets demonstrate DSCD's state-of-the-art (SOTA) performance in detoxification and generation fluency, with superior efficiency compared to existing methods. |
Contextual Interaction for Argument Post Quality Assessment (2023.emnlp-main)
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| Challenge: | Existing methods for assessing the quality of natural language arguments are limited . existing methods focus on evaluating individual argument posts, but they often fail to distinguish between arguments with a narrow quality gap. |
| Approach: | They propose to use supervised contrastive learning to model arguments' quality . large language models with in-context examples harness the power of LLMs . |
| Outcome: | The proposed approach outperforms state-of-the-art models on a publicly available dataset . it shows that the LLMs with in-context examples are more effective than baseline models . |
WebSTAR: Scalable Data Synthesis for Computer Use Agents with Step-Level Filtering (2026.acl-long)
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| Challenge: | Existing datasets rely on human demonstrations, limiting scalability. |
| Approach: | They propose a scalable data synthesis pipeline that transforms noisy rollouts into reliable supervision without human annotation. |
| Outcome: | The proposed pipeline transforms noisy rollouts into reliable supervision without human annotation. |
Bootstrapped Unsupervised Sentence Representation Learning (2021.acl-long)
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| Challenge: | Existing approaches to learn sentence representations rely on quality labeled data. |
| Approach: | They propose a Siamese Network which maximizes similarity between two augmented views of each sentence. |
| Outcome: | The proposed method outperforms state-of-the-art methods on STS and classification tasks. |
Data Poisoning for In-context Learning (2025.findings-naacl)
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| Challenge: | In-context learning (ICL) has emerged as a capability of large language models (LLMs) but there is limited understanding of its vulnerability against data poisoning attacks. |
| Approach: | They propose an attack method that exploits ICL’s unique learning mechanisms by identifying discrete text perturbations that influence LLM hidden states. |
| Outcome: | The proposed attack method exploits ICL’s learning mechanisms by identifying discrete text perturbations that influence LLM hidden states. |
WinoLogic: A Zero-Shot Logic-based Diagnostic Dataset for Winograd Schema Challenge (2021.emnlp-main)
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| Challenge: | Recent success of neural language models on the Winograd Schema Challenge has called for further investigation of commonsense reasoning ability of these models. |
| Approach: | They propose a logic-based framework that focuses on high-quality commonsense knowledge. |
| Outcome: | The proposed framework focuses on high-quality commonsense knowledge. |
PEER: Pre-training ELECTRA Extended by Ranking (2023.findings-acl)
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| Challenge: | Existing models for pre-training require expensive pre-trainer computation cost . ELECTRA model can perform replaced token detection (RTD) task with reduced pre- training cost compared to current models . |
| Approach: | They propose to extend a discriminator-based replaced token detection task into a ranker-based task . they propose to use a binary classifier to perform a more precise task with negligible additional computation cost. |
| Outcome: | The proposed model outperforms state-of-the-art models with ELECTRA in GLUE tasks given the same cost. |
Reformatted Alignment (2024.findings-emnlp)
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| Challenge: | Current methods to improve data quality are labor-intensive or prone to factual errors caused by LLM hallucinations. |
| Approach: | They propose a method which reformats the responses of instruction data into a format that better aligns with pre-established criteria and the collated evidence. |
| Outcome: | The proposed approach minimizes human annotation, hallucination, and the difficulty in scaling, remaining orthogonal to existing alignment techniques. |
PychoAgent: Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events (2025.emnlp-main)
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Mengzhu Liu, Zhengqiu Zhu, Chuan Ai, Chen Gao, Xinghong Li, Lingnan He, Kaisheng Lai, Yingfeng Chen, Xin Lu, Yong Li, Quanjun Yin
| Challenge: | Social media's rich information content and spatiotemporal granularity provide unique opportunities for emotion prediction and management. |
| Approach: | They propose a Psychology-driven generative Agent framework for explainable panic prediction based on emotion arousal theory. |
| Outcome: | The proposed framework improves panic emotion prediction performance by 13% to 21% compared to baseline models. |
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models (2021.naacl-main)
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| Challenge: | Recent studies reveal a security threat to natural language processing models, called the Backdoor Attack. |
| Approach: | They propose to hack a model by modifying one single word embedding vector without sacrificing accuracy on clean samples. |
| Outcome: | The proposed method is more efficient and stealthier on sentiment analysis and sentence-pair classification tasks. |
What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time (2026.acl-long)
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| Challenge: | Existing TTRL methods rely on positive pseudo-labeling strategies to enhance reasoning capabilities. |
| Approach: | They propose a test-time reinforcement learning framework that mitigates label noise amplification by deriving pseudo-rewards from majority voting consensus. |
| Outcome: | The proposed framework mitigates label noise amplification by implementing selective positive pseudo-labeling and entropy-gated negative p-labeled pruning. |
QuASE: Question-Answer Driven Sentence Encoding (2020.acl-main)
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| Challenge: | Question-answering (QA) data often encodes essential information in many facets . a growing interest of QA has led to many large-scale QA datasets available to the community . |
| Approach: | They propose a question-answer driven sentence encoding framework to learn representations from QA data. |
| Outcome: | The proposed framework learns representations from QA data, using BERT or other state-of-the-art contextual language models. |
Beyond the First Error: Process Reward Models for Reflective Mathematical Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for training effective PRMs focus on the first incorrect step and all preceding steps, assuming that all subsequent steps are incorrect. |
| Approach: | They propose a data annotation method specifically designed to score the long CoT reasoning process by using an LLM-based judger for annotation. |
| Outcome: | The proposed method improves PRMs' ability to identify effective self-correction behaviors and reasoning based on erroneous steps. |
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading Comprehension (2021.findings-emnlp)
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| Challenge: | unified Aspect-based Sentiment Analysis (ABSA) aims to couple aspect terms with their corresponding opinion terms, which might make it easier to predict sentiment polarities. |
| Approach: | They propose a new paradigm to pair aspect terms with their corresponding opinion terms . they propose to use a machine learning paradigm to solve the unified ABSA task . |
| Outcome: | The proposed framework can solve the ABSA task without any additional data annotation or transformation. |
Tensor Product Generation Networks for Deep NLP Modeling (N18-1)
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| Challenge: | Using Tensor Product Representations (TPRs) we propose a new architecture for natural language processing based on the principle that hypothesis space for learning includes network hypotheses that are independently known to be suitable for performing the target task. |
| Approach: | They propose a Tensor Product Generation Network (TPGN) which is capable of carrying out TPR computation but uses unconstrained deep learning to design its internal representations. |
| Outcome: | The proposed architecture outperforms baselines on the COCO dataset and can interpret internal representations and operations. |
UICOMPASS: UI Map Guided Mobile Task Automation via Adaptive Action Generation (2025.emnlp-main)
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| Challenge: | Mobile task automation is an emerging technology that leverages AI to automatically execute routine tasks by users’ commands on mobile devices like Android. |
| Approach: | They propose a UI Map-guided LLM-based approach to automate mobile tasks using static analysis and LLMs. |
| Outcome: | The proposed approach achieves a 15.87% higher task execution success rate than SOTA approaches even when only APK is available. |
Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases (2024.findings-emnlp)
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| Challenge: | Definition bias is a negative phenomenon that can mislead models. |
| Approach: | They propose a framework that measures definition bias, bias-aware fine-tuning and task-specific bias mitigation to mitigate definition bias in information extraction. |
| Outcome: | The proposed framework mitigates definition bias in information extraction tasks by measuring definition bias, bias-aware fine-tuning, and task-specific bias mitigation. |
Eliciting Knowledge from Experts: Automatic Transcript Parsing for Cognitive Task Analysis (P19-1)
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| Challenge: | Cognitive task analysis (CTA) is a type of analysis used to elicit and represent the knowledge and thought processes of domain experts. |
| Approach: | They propose a weakly-supervised framework for automated CTA transcript parsing . they partition the parser process into a sequence labeling task and a text span-pair relation extraction task with distant supervision from human-curated protocol files. |
| Outcome: | The proposed framework reduces human labor and scales the task to a small scale. |
SpecCache: Speculative KV Cache Reuse for Efficient RAG Serving (2026.acl-long)
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Zijian Wen, Tao Zhang, Shuangwu Chen, Shenghao Ye, Yu Guo, Qirui Chen, Jingxian Shuai, Yunpeng Hou, Huasen He, null Jianyang
| Challenge: | Retrieval-Augmented Generation (RAG) improves LLMs but faces high prefill latency during long contexts. |
| Approach: | They propose a method that uses deep-layer hidden-state norms to guide token selection . they propose to use deep-layered hidden-status norms as a proxy to guide the token selection. |
| Outcome: | The proposed SpecCache outperforms state-of-the-art (SOTA) benchmarks. |
Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers (2025.tacl-1)
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Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar, Tianxing He, Noah A. Smith, Navin Goyal, Yulia Tsvetkov
| Challenge: | Inductive biases in transformers can cause hierarchical generalization without explicitly encoding structural bias. |
| Approach: | They investigate sources of inductive bias in transformer models and their training that could cause such preference for hierarchical generalization. |
| Outcome: | The proposed model can generalize to novel syntactic forms without explicit bias . the proposed model is able to generalize on a dataset with a hierarchical grammar . |
Is Grokking Worthwhile? Functional Analysis and Transferability of Generalization Circuits in Transformers (2026.findings-acl)
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| Challenge: | Large Language Models struggle with the "curse of two-hop reasoning" in compositional tasks. |
| Approach: | They propose to form a "Generalization Circuit" during a prolonged "grokking" phase . they argue that grokkking is the process of integrating memorized atomic facts into an easy-acquire reasoning path. |
| Outcome: | The proposed model is superior to non-grokked models, but it requires a large computational cost . the study shows that grokking is not the sudden acquisition of a new reasoning paradigm . |
Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have proven to be highly effective in addressing a wide range of complex tasks. |
| Approach: | They propose a method that asks teachers to identify and explain student’s mistakes and then asks them to provide customized instruction learning data. |
| Outcome: | The proposed method reduces the chance of teachers guessing incorrectly with flawed rationales, improving instructional data quality. |
A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models (2021.naacl-main)
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| Challenge: | Existing methods to accelerate inference speed of pre-trained language models are limited to local representations of exit layer . current models are associated with large memory requirement and high computational cost, which slow down inference and further encumber the application of PLMs. |
| Approach: | They propose a method to exit early without passing through all inference layers . they take into consideration all the linguistic information embedded in the past layers a global perspective . |
| Outcome: | The proposed method outperforms existing methods by a large margin . it uses linguistic information embedded in the past layers and future features . the proposed method is scalable and cost-effective . |
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification (2021.naacl-main)
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| Challenge: | Recent work on aspect-level sentiment classification has shown that syntactic information is effective in capturing long-range syntaktic relations that are obscure from the surface form. |
| Approach: | They propose a graph ensemble technique that integrates syntactic structures with GNNs to better leverage syntaktic information in the face of parsing errors. |
| Outcome: | The proposed model outperforms models with single dependency tree and beats other models without adding model parameters. |
Graph Hawkes Transformer for Extrapolated Reasoning on Temporal Knowledge Graphs (2022.emnlp-main)
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| Challenge: | Existing methods for entity prediction cannot predict when an event will occur . there are many facts not related to the query that can confuse the model . |
| Approach: | They propose a temporal knowledge Graph reasoning model based on Graph Hawkes Transformer . the model captures instantaneous structural and temporal evolution information . |
| Outcome: | The proposed model performs much better under long-term evolution scenarios. |
Distillation with Explanations from Large Language Models (2024.lrec-main)
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| Challenge: | Large language models (LLMs) like ChatGPT and GPT-4 have made remarkable progress in various NLP tasks while also providing explanations alongside their answers. |
| Approach: | They propose to use Large language models (LLMs) to generate more accurate answers and corresponding free-text explanations by combining ground truth labels and answers-explanations generated by LLMs. |
| Outcome: | The proposed method achieves improved predictive performance and generates explanations that exhibit greater alignment with the model’s task outputs. |
SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)
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Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li
| Challenge: | Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy. |
| Approach: | They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration. |
| Outcome: | The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent . |
Relevant Emotion Ranking from Text Constrained with Emotion Relationships (N18-1)
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| Challenge: | Existing methods to detect emotions from text are lexicon-based and learning-based . experimental results show that the proposed framework is better than state-of-the-art methods . |
| Approach: | They propose to use a multi-label classification problem to generate a ranked list of relevant emotions. |
| Outcome: | The proposed framework performs better than state-of-the-art methods and multi-label learning methods on two real-world corpora. |
Table-R1: Region-based Reinforcement Learning for Table Understanding (2026.findings-acl)
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Zhenhe Wu, Jian Yang, Zhongjiang He, Changzai Pan, Jiaheng Liu, Xianjie Wu, Yu Zhao, Shuangyong Song, Yongxiang Li, Zhoujun Li, Xuelong Li
| Challenge: | Tables are a widely used data format that poses unique challenges for language models due to their structured row-column interactions. |
| Approach: | They propose a region-based reinforcement learning approach that integrates region evidence into reasoning steps. |
| Outcome: | The proposed method outperforms baseline models on three benchmark datasets and significantly reduces the reasoning token consumption by 67.5%. |
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog (2022.findings-naacl)
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| Challenge: | Existing task-oriented dialog systems suffer from error propagation from inaccurate dialog states and responses. |
| Approach: | They propose a back reconstruction approach for end-to-end task-oriented dialog system . they use back reconstruction to reconstruct the original input context from the generated dialog states . |
| Outcome: | Extensive experiments on MultiWOZ 2.0 and CamRest676 show the effectiveness of the proposed model. |
Rethinking Text-based Protein Understanding: Retrieval or LLM? (2025.emnlp-main)
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| Challenge: | Recent studies have focused on integrating protein-related knowledge into large language models through continued pretraining and multi-modal alignment. |
| Approach: | They propose a retrieval-enhanced method which significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios. |
| Outcome: | The proposed method significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios. |
BigTokDetect: A Clinically-Informed Vision–Language Modeling Framework for Detecting Pro-Bigorexia Videos on TikTok (2026.eacl-long)
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Minh Duc Chu, Kshitij Pawar, Zihao He, Roxanna Sharifi, Ross M. Sonnenblick, Magdalayna Curry, Laura DAdamo, Lindsay Young, Stuart Murray, Kristina Lerman
| Challenge: | Social media platforms face escalating challenges in detecting harmful content that promotes muscle dysmorphic behaviors and cognitions (bigorexia). |
| Approach: | They propose a framework for detecting pro-bigorexia content on TikTok using an expert-annotated multimodal benchmark dataset of over 2,200 Tiktok videos labeled by clinical psychiatrists. |
| Outcome: | The proposed framework improves on fine-grained subcategories while commercial models achieve the highest accuracy on primary categories. |
Lexical Diversity-aware Relevance Assessment for Retrieval-Augmented Generation (2025.acl-long)
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| Challenge: | Extensive experiments on widely used benchmarks demonstrate the efficacy of our approach, yielding a 10.6% accuracy improvement on HotpotQA. |
| Approach: | They propose a Lexical Diversity-aware RAG method to address the biases in relevant information retrieval and utilization induced by lexical diversity. |
| Outcome: | Extensive experiments on widely used benchmarks show the proposed method yields a 10.6% accuracy improvement on HotpotQA. |
Understanding Spatial Relations through Multiple Modalities (2020.lrec-1)
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| Challenge: | Existing work on common sense reasoning and understanding of spatial relations is limited. |
| Approach: | They propose a spatial model that uses both textual and visual information to predict spatial relations between two entities in an image. |
| Outcome: | The proposed model improves prediction accuracy and coverage and deals with unseen subjects, objects and relations. |
Beyond the Tip of Efficiency: Uncovering the Submerged Threats of Jailbreak Attacks in Small Language Models (2025.findings-acl)
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| Challenge: | Small language models (SLMs) have become increasingly prominent in the deployment on edge devices due to their high efficiency and low computational cost. |
| Approach: | They evaluate the security performance of 13 state-of-the-art small language models under various jailbreak attacks. |
| Outcome: | The proposed methods demonstrate that SLMs are quite susceptible to jailbreak attacks and some are even vulnerable to harmful prompts. |
Chumor 2.0: Towards Better Benchmarking Chinese Humor Understanding from (Ruo Zhi Ba) (2025.findings-acl)
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Ruiqi He, Yushu He, Longju Bai, Jiarui Liu, Zhenjie Sun, Zenghao Tang, He Wang, Hanchen Xia, Rada Mihalcea, Naihao Deng
| Challenge: | Existing studies on humor in non-English languages lack culturally nuanced humor in other languages. |
| Approach: | They construct a Chinese humor explanation dataset using a reddit-like platform . they test ten LLMs and find they are significantly better than existing LLM models . |
| Outcome: | The proposed dataset is the first and largest Chinese humor explanation dataset. |
Optimizing Reasoning for Text-to-SQL with Execution Feedback (2025.findings-acl)
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| Challenge: | Large language models excel in many reasoning tasks, but their ability to leverage Chain-of-Thought (CoT) reasoning remains underexplored. |
| Approach: | They propose a framework that iteratively optimizes open-source LLMs by combining CoT reasoning with off-policy and on-poly DPO, relying solely on execution accuracy as feedback. |
| Outcome: | The proposed framework improves execution accuracy on BIRD and Spider datasets. |
Dynamic Personality in LLM Agents: A Framework for Evolutionary Modeling and Behavioral Analysis in the Prisoner’s Dilemma (2025.findings-acl)
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| Challenge: | Current models rely on static personality traits but lack natural selection processes and direct psychological metrics, failing to accurately capture authentic dynamic personality variations. |
| Approach: | They propose a framework that uses game payoffs as environmental feedback to drive adaptive personality evolution and analyze correlations between personality metrics and behavior. |
| Outcome: | The proposed framework reveals new behavioral patterns of agents and evaluates personality-behavior relationships, advancing agent-based social simulations and human-AI symbiosis research. |
DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning (2024.acl-long)
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Yejie Wang, Keqing He, Guanting Dong, Pei Wang, Weihao Zeng, Muxi Diao, Weiran Xu, Jingang Wang, Mengdi Zhang, Xunliang Cai
| Challenge: | Numerous code large language models (LLMs) have been proposed to enhance code generation performance. |
| Approach: | They propose a diverse instruction model DolphCoder with self-evaluating for code generation that learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability. |
| Outcome: | The proposed model achieves superior performance on the HumanEval and MBPP benchmarks, demonstrating new insights for future code instruction tuning work. |
Evaluating and Improving Graph to Text Generation with Large Language Models (2025.naacl-long)
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| Challenge: | Recent advances in large language models have revolutionized natural language processing due to their zero-and-short-shot capabilities. |
| Approach: | They propose a tuning-free prompting approach for graph-to-text generation tasks. |
| Outcome: | The proposed approach improves LLMs on graph-to-text generation tasks incrementally. |
Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing (2022.acl-short)
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| Challenge: | Text-to-SQL parsing aims to parse natural language questions into SQL queries . current attention-based approaches can only model alignments at the token level . |
| Approach: | They propose a method to leverage explicit lexico-logical alignments by identifying possible phrase-level alignments and injecting them as additional contexts into the parsing procedure. |
| Outcome: | The proposed approach improves performance by 3.4% on Squall. |
Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)
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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao
| Challenge: | Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications. |
| Approach: | They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions. |
| Outcome: | The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions. |
InfiMM: Advancing Multimodal Understanding with an Open-Sourced Visual Language Model (2024.findings-acl)
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Haogeng Liu, Quanzeng You, Yiqi Wang, Xiaotian Han, Bohan Zhai, Yongfei Liu, Wentao Chen, Yiren Jian, Yunzhe Tao, Jianbo Yuan, Ran He, Hongxia Yang
| Challenge: | InfiMM is a multimodal large language model that adapts to complex vision-language tasks. |
| Approach: | They present a Multimodal Large Language Model that adapts to intricate vision-language tasks using large-scale training data and comprehensive training strategies. |
| Outcome: | Empirical evaluations across a variety of benchmarks underscore InfiMM’s remarkable capability in multimodal understanding. |
MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness (2025.emnlp-main)
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| Challenge: | Large language models produce non-existing facts when faced with questions outside their parametric knowledge, which undermines their reliability. |
| Approach: | They propose a method that separates the learning of answer prediction and confidence estimation during fine-tuning on instruction data. |
| Outcome: | Experiments on multiple models and different model sizes show that the proposed method outperforms baselines by up to 25% in average precision. |
A Label-Aware Autoregressive Framework for Cross-Domain NER (2022.findings-naacl)
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| Challenge: | Existing approaches to named entity recognition (NER) focus on reducing discrepancy between tokens and tokens, but transfer of valuable label information is often not considered or ignored. |
| Approach: | They propose a framework that borrows entity information from the source domain to enhance NER in the target domain. |
| Outcome: | The proposed model improves over the state-of-the-art model on several datasets. |
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning (2024.findings-acl)
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| Challenge: | Instruction tuning is critical to large language models but its success heavily relies on the training data quality. |
| Approach: | They propose a paradigm that synergizes a teacher LLM’s reflection and introspection with the data selection capability of the student LLM to automatically refine existing instruction-tuning data. |
| Outcome: | The proposed method achieves much stronger and top-tier 7B and 13B LLMs without collecting brand-new data. |
PAPERMIND: Benchmarking Agentic Reasoning and Critique over Scientific Papers in Multimodal LLMs (2026.findings-acl)
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Yanjun Zhao, Tianxin Wei, Jiaru Zou, Xuying Ning, Yuanchen Bei, Lingjie Chen, Simmi Rana, Wendy H. Yang, Hanghang Tong, Jingrui He
| Challenge: | Existing benchmarks assess integrated and agent-oriented scientific reasoning in isolation . Existing systems assess integrated reasoning in isolated tasks . |
| Approach: | They propose a benchmark to evaluate integrated and agent-oriented scientific reasoning over research papers. |
| Outcome: | The proposed benchmark evaluates integrated and agent-oriented scientific reasoning over scientific papers. |
TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (2021.emnlp-main)
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| Challenge: | Existing methods to extract entities and relations from unstructured texts are difficult to handle due to the overlapping triple problem. |
| Approach: | They propose a translation decoding schema for joint extraction of entities and relations from unstructured texts to form factual triples. |
| Outcome: | The proposed model can handle the overlapping triple problem, and is 2 times faster than the state-of-the-art models. |
Efficient Data Learning for Open Information Extraction with Pre-trained Language Models (2023.findings-emnlp)
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| Challenge: | Experimental results indicate that, compared to previous SOTA methods, OK-IE requires only 1/100 of the training data (900 instances) and 1/120 of the time (3 minutes) to achieve comparable results. |
| Approach: | They propose a framework that transforms OpenIE into the pre-training task form of the T5 model, thereby reducing the need for extensive training data. |
| Outcome: | The proposed framework transforms OpenIE into the pre-training task form of the T5 model, reducing the need for extensive training data and significantly reducing training time. |
Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning (2025.acl-long)
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Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Qiushi Sun, Kanzhi Cheng, Junxian He, Jun Liu, Zhiyong Wu
| Challenge: | Existing methods for enhancing LLM reasoning rely on supervisory signals . current methods rely heavily on outcome supervision and auxiliary reward models . |
| Approach: | They propose a gen-eralizable and purely unsupervised self-training framework to enhance LLM reasoning without supervision. |
| Outcome: | The proposed framework improves LLM reasoning without supervision without external supervision. |
Types of Out-of-Distribution Texts and How to Detect Them (2021.emnlp-main)
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| Challenge: | Current NLP models produce unreliable or catastrophic predictions when training and test distributions differ . current models tend to produce unreliability or even catastrophic predictions that hurt user trust. |
| Approach: | They categorize examples as exhibiting a background shift or semantic shift and use calibration and density estimation methods to detect OOD examples. |
| Outcome: | The proposed methods beat calibration methods in background shift settings and perform worse in semantic shift settings. |
ChipSeek: Optimizing Verilog Generation via EDA-Integrated Reinforcement Learning (2026.acl-long)
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Zhirong Chen, Kaiyan Chang, Zhuolin Li, Cangyuan Li, Xinyang He, Chujie Chen, Mengdi Wang, Haobo Xu, Yinhe Han, Huawei Li, Ying Wang
| Challenge: | Existing approaches to optimize Register-Transfer Level (RTL) code fail to simultaneously optimize functional correctness and hardware efficiency metrics such as Power, Performance, and Area (PPA). |
| Approach: | They propose a hierarchical reward based reinforcement learning framework that integrates direct feedback from EDA simulators and synthesis tools into a reward mechanism. |
| Outcome: | The proposed framework integrates direct feedback from EDA simulators and synthesis tools into a hierarchical reward based reinforcement learning framework. |
Direct Speech-to-Speech Translation With Discrete Units (2022.acl-long)
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Ann Lee, Peng-Jen Chen, Changhan Wang, Jiatao Gu, Sravya Popuri, Xutai Ma, Adam Polyak, Yossi Adi, Qing He, Yun Tang, Juan Pino, Wei-Ning Hsu
| Challenge: | Existing direct speech-to-speech translation models rely on text generation as an intermediate step. |
| Approach: | They propose a direct speech-to-speech translation model that translates speech from one language to another without relying on intermediate text generation. |
| Outcome: | The proposed model produces 6.7 BLEUs in the Fisher Spanish-English dataset when trained without any text transcripts and with text supervision. |
The Data Frontier for Large Language Models: Selection, Synthesis, and Tools (2026.acl-tutorials)
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| Challenge: | acquiring and curating high-quality training data remains a significant bottleneck . acquiring such high-quality data is a key challenge for researchers and practitioners . |
| Approach: | This tutorial provides a comprehensive and practical guide to the state-of-the-art in data research directions for LLMs. |
| Outcome: | The tutorial covers methods for curating the most valuable information from vast, noisy datasets and the synthetic data revolution. |
PivotFEC: Enhancing Few-shot Factual Error Correction with a Pivot Task Approach using Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for Factual Error Correction (FEC) use mask-then-correct paradigms . however, the lack of datasets containing false claims has impeded progress . |
| Approach: | They propose a method that enhances few-shot FEC with a pivot task approach using large language models. |
| Outcome: | The proposed method outperforms its few-shot counterpart by 7.9 points in SARI . it improves widely-adopted SARI metrics by 11.3 compared to the best-performing methods . |
Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models (2024.findings-acl)
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| Challenge: | Temporal knowledge graph question answering (TKGQA) is one of the most challenging QA tasks due to the temporal constraints hidden in questions and the answers sought from dynamic structured knowledge. |
| Approach: | They propose a generative temporal knowledge graph question answering framework which guides LLMs to answer temporal questions through two phases: Subgraph Retrieval and Answer Generation. |
| Outcome: | The proposed framework exploits LLM’s intrinsic knowledge to mine temporal constraints and structural links in the questions without extra training, thus narrowing down the subgraph search space in both temporal and structural dimensions. |
Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning (2024.emnlp-main)
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| Challenge: | Existing methods to correct outdated or erroneous knowledge in large language models (LLMs) are slow and cumbersome, resulting in catastrophic knowledge forgetting and degradation of model performance. |
| Approach: | They propose a RetriEval-augmented ContInuous Prompt lEarning method that converts knowledge statements into short and informative continuous prompts, prefixed to the LLM’s input query embedding. |
| Outcome: | The proposed method improves the performance of large language models (LLMs) while maintaining the overall performance of the model. |
Effects of Human Adversarial and Affable Samples on BERT Generalization (2023.findings-emnlp)
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| Challenge: | Limiting quantities of training data is considered a key impediment to achieving generalizability in machine learning. |
| Approach: | They examine the impact of training data quality, not quantity, on a model’s generalizability by comparing human-adversarial and human-affable training samples. |
| Outcome: | The proposed model performance improves with 10-30% h-adversarial instances in text classification and relation extraction tasks. |
P-React: Synthesizing Topic-Adaptive Reactions of Personality Traits via Mixture of Specialized LoRA Experts (2025.findings-acl)
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| Challenge: | Existing studies on personalized large language models focus on modeling explicit character profiles, while ignoring the underlying personality traits that truly shape behaviors and decision-making. |
| Approach: | They propose a personalized large language model (LLM) that captures implicit Big Five personality traits and integrates a Personality Specialization Loss to capture individual trait expressions. |
| Outcome: | The proposed model improves on Big Five personality traits and integrates a Personality Specialization Loss (PSL) to capture individual trait expressions. |
CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition (2025.emnlp-main)
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| Challenge: | Existing vision-language models are not equipped to read diverse languages and scripts found in historical materials. |
| Approach: | They propose to train an open-weight vision-language model for historical text recognition on CHURRO-DS, the largest historical text-recognition dataset to date. |
| Outcome: | The proposed model outperforms existing vision-language models on CHURRO-DS, the largest historical text recognition dataset to date. |
Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering (2025.acl-long)
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| Challenge: | Large language models (LLMs) have recently pushed open-domain question answering (ODQA) to new heights. |
| Approach: | They propose an embedding-level framework that enhances both the retriever and the reader by reordering query representations via lightweight linear layers under an unsupervised contrastive learning objective. |
| Outcome: | The proposed framework outperforms baselines in accuracy and efficiency across three open-source LLMs, three retrieval methods, and four ODQA benchmarks. |
HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing (2025.naacl-long)
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| Challenge: | Existing models that memorize past tokens have “flat” memory architectures that restrict the context window. |
| Approach: | They propose a framework that imitates human memorization behavior by preserving tokens from early input segments, passing memory embeddings along the sequence, and recalling relevant information from history. |
| Outcome: | The proposed framework outperforms existing models in language modeling and question-answering tasks and achieves comparable or superior generation quality to long-context models with 2 57 fewer parameters and 2.5 116 less inference memory. |
Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation (2021.eacl-main)
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| Challenge: | Public datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP). |
| Approach: | They identify leakage of training data into test data on several publicly available datasets used to evaluate NLP tasks, including named entity recognition and relation extraction. |
| Outcome: | The proposed model can memorize and generalize data on several publicly available datasets and is compared against previously unseen data. |
CA-GAR: Context-Aware Alignment of LLM Generation for Document Retrieval (2025.findings-acl)
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| Challenge: | Recent techniques such as Generation-Augmented Retrieval (GAR) and Generative Document Retrieleval (GDR) leverage LLMs to enhance retrieval performance but face key challenges: GAR’s generated content may not always align with the target document corpus, while GDR limits the generative capacity of LLM. |
| Approach: | They propose a Context-Aware Generation-Augmented Retrieval approach which integrates corpus information into their generation process. |
| Outcome: | Experimental results show that CA-GAR outperforms existing methods on seven tasks and four non-English languages. |
RGR-KBQA: Generating Logical Forms for Question Answering Using Knowledge-Graph-Enhanced Large Language Model (2025.coling-main)
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| Challenge: | Existing methods for Knowledge Base Question Answering (KBQA) face hallucination problems, resulting in low accuracy. |
| Approach: | They propose a retrieval-generate-retrieve framework that uses a Retrieve-Generate framework to retrieve factual knowledge from a knowledge graph. |
| Outcome: | Experimental results show that RGR-KBQA improves on CWQ and WebQSP datasets. |
A General Framework to Enhance Fine-tuning-based LLM Unlearning (2025.findings-acl)
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Jie Ren, Zhenwei Dai, Xianfeng Tang, Hui Liu, Jingying Zeng, Zhen Li, Rahul Goutam, Suhang Wang, Yue Xing, Qi He, Hui Liu
| Challenge: | Existing approaches to remove copyrighted and privacy-sensitive data from Large Language Models (LLMs) have been proposed to remove specific data from LLMs without requiring full retraining. |
| Approach: | They propose a general framework that enhances the utility of fine-tuning-based methods by distinguishing target data and suppressing related generations. |
| Outcome: | The proposed framework improves the unlearning and utility of fine-tuning-based methods by distinguishing the target data and suppressing related generations. |
GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text (2024.emnlp-main)
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| Challenge: | Existing resources for standardized, easily accessible IGT data limit their applicability to linguistic research. |
| Approach: | They compile the largest existing corpus of interlinear glossed text data from a variety of sources and use it to generate annotated text. |
| Outcome: | The proposed model outperforms SOTA models on monolingual corpora by 6.6%. |
Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models (2025.findings-acl)
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| Challenge: | Sentence embedding is essential for many NLP tasks, but reliance on manual labels limits scalability. |
| Approach: | They propose a method for controlling the generation direction of large language models in the latent space by integrating ranking information and semantic information. |
| Outcome: | The proposed method achieves new SOTA performance with a modest cost in ranking sentence synthesis. |
VisualEDU: A Benchmark for Assessing Coding and Visual Comprehension through Educational Problem-Solving Video Generation (2025.findings-emnlp)
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| Challenge: | VisualEDU is a benchmark to evaluate VLMs' ability to produce coherent video from text . advanced proprietary models show promise, but struggle with increasing task complexity . |
| Approach: | VisualEDU is a benchmark to evaluate VLMs' ability to produce coherent video from text . it integrates meta-prompt learning, visual and code feedback, and a drawing toolkit to enhance output quality. |
| Outcome: | VisualEDU is a benchmark to evaluate VLMs' ability to produce coherent video from text . it integrates meta-prompt learning, visual and code feedback, and a drawing toolkit to improve output quality. |
Eliminating Biased Length Reliance of Direct Preference Optimization via Down-Sampled KL Divergence (2024.emnlp-main)
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| Challenge: | Existing studies attributed verbosity to biased labels, but new research shows that DPO can be effective in mitigating verboses. |
| Approach: | They propose to use a method to reduce the amount of verbosity in LLMs by using a downsampling approach. |
| Outcome: | The proposed approach overcomes the problem of verbosity by reducing the length reliance of the proposed algorithm. |
CAST: Achieving Stable LLM-based Text Analysis for Data Analytics (2026.findings-acl)
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| Challenge: | Text analysis of tabular data relies on two core operations: summarization for corpus-level theme extraction and tagging for row-level labeling. |
| Approach: | They propose a framework that enhances output stability by constraining the model’s latent reasoning trajectory. |
| Outcome: | The proposed framework improves stability by constraining the model's latent reasoning trajectory. |
Learning from Cognition: Enhancing RL Efficiency for LLM Reasoning via Hierarchical Metacognitive Decomposition and Refinement (2026.acl-long)
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Zexu Sun, Yongcheng Zeng, Erxue Min, Heyang Gao, Bokai Ji, Dugang Liu, Xing Tang, Xiuqiang He, Xu Chen
| Challenge: | Recent advances in Large Language Models have demonstrated notable inferential capacities via reinforcement learning (RL) however, “zero-RL” approaches relying on fixed prompt templates introduce substantial sampling inefficiencies for weak LLMs. |
| Approach: | They propose a hierarchical metacognitive RL framework that decomposes zero-accuracy problems into subproblems and prompts the policy to refine answers by referencing previous wrong solutions. |
| Outcome: | The proposed framework improves sample utilization and sample efficiency and accelerates convergence compared to baselines. |
CAMIEval: Enhancing NLG Evaluation through Multidimensional Comparative Instruction-Following Analysis (2025.naacl-long)
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| Challenge: | Evaluating the quality of texts generated by language models has always been a challenging task in natural language processing (NLP). |
| Approach: | They propose a multidimensional comparative evaluation method based on instruction-following that combines relevance, factuality, and adherence with a concrete Chain-of-Thoughts process to enhance the accuracy of evaluations. |
| Outcome: | The proposed method outperforms existing methods in correlation with human evaluations on two NLG evaluation benchmarks. |
AERA Chat: An Interactive Platform for Automated Explainable Student Answer Assessment (2025.emnlp-demos)
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| Challenge: | Existing systems that use pretrained language models to score student answers are noisy and unreliable. |
| Approach: | They propose a visualization platform for automated student answer assessment that leverages multiple LLMs to generate rationales. |
| Outcome: | The proposed platform enables educators to mark tasks and researchers to evaluate rationale quality from different models. |
A Scalable Framework for Table of Contents Extraction from Complex ESG Annual Reports (2023.emnlp-main)
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| Challenge: | Existing methods for document classification focus on local layout, sidelining holistic comprehension of content and organisation. |
| Approach: | They propose a framework for Table of Contents extraction that uses hierarchical structure to extract text from ESG annual reports. |
| Outcome: | The proposed framework outperforms the state-of-the-art with a fraction of running time. |
Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification (2022.acl-long)
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| Challenge: | a new framework for patent approval prediction is proposed to address this problem . novelty scores are based on comparing an application with millions of prior arts . |
| Approach: | They propose a framework that unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. |
| Outcome: | The proposed framework unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. |
MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training (2025.acl-long)
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| Challenge: | Existing methods for complex instruction-following with elaborate constraints rely on a weaker model, especially GPT-4, limiting their application. |
| Approach: | They propose a Multi-granularity Self-Contrastive Training framework to improve instruction alignment without relying on a stronger model. |
| Outcome: | The proposed framework improves instruction-following with elaborate constraints without external supervision on coarse and fine granularity. |
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation (2024.emnlp-main)
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Xuan He, Dongfu Jiang, Ge Zhang, Max Ku, Achint Soni, Sherman Siu, Haonan Chen, Abhranil Chandra, Ziyan Jiang, Aaran Arulraj, Kai Wang, Quy Do, Yuansheng Ni, Bohan Lyu, Yaswanth Narsupalli, Rongqi Fan, Zhiheng Lyu, Bill Yuchen Lin, Wenhu Chen
| Challenge: | Existing video metrics are lagging behind in providing reliable scores over generated videos due to lack of large-scale human-annotated dataset. |
| Approach: | They propose to use VideoFeedback to train a human-annotated multi-aspect score over 37.6K synthesized videos from 11 existing video generative models. |
| Outcome: | The proposed model outperforms the prior best metrics by 50 points in the test. |
MuGER2: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering (2022.findings-emnlp)
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| Challenge: | Conventional HQA models retrieve coarse- or fine-grained evidence to reason the answer . however, they neglect a more general scenario requiring reasoning over heterogeneous data to answer a question. |
| Approach: | They propose a multi-granularity evidence retrieval and reasoning approach to answer questions over heterogeneous data using tables and passages linked to table cells. |
| Outcome: | The proposed approach significantly boosts the performance on the HybridQA dataset. |
How Susceptible are Large Language Models to Ideological Manipulation? (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have the potential to exert substantial influence on public perceptions and interactions with information. |
| Approach: | They examine how LLMs can learn and generalize ideological biases from their instruction-tuning data. |
| Outcome: | The LLMs show a startling ability to absorb ideology from one topic and generalize it to even unrelated ones. |
Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models (2026.findings-acl)
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| Challenge: | Recent advances on prompting and post-training have enabled LLMs to perform step-wise reasoning tasks, but they tend to explore unproductive solution paths without effective backtracking or strategy adjustment. |
| Approach: | They propose a framework that empowers LLMs to “think about how to think” and dynamically adapts reasoning strategies in real-time. |
| Outcome: | The proposed framework outperforms previous SOTA methods by 9-12% in accuracy while reducing inference time by 28-35% under the same compute budget. |
Revisiting Scaling Laws for Language Models: The Role of Data Quality and Training Strategies (2025.acl-long)
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| Challenge: | Existing scaling laws suggest augmenting model size and training data results in enhanced performance, but recent studies reveal deviations, particularly in large language models, where performance improvements decelerate—a phenomenon known as sub-scaling. |
| Approach: | They propose a sub-optimal scaling law that better predicts performance in sub-scaling regimes by examining data quality and training strategies. |
| Outcome: | The proposed scaling law better predicts performance in sub-scaling regimes, highlighting the importance of data quality and diversity. |
Self-Attention Guided Copy Mechanism for Abstractive Summarization (2020.acl-main)
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| Challenge: | Abstractive summarization models have been widely used to extract words from source into summary, but how to ensure that important words in source are copied remains a challenge. |
| Approach: | They propose a Transformer-based model to enhance copy mechanism by identifying the importance of each source word based on the degree centrality. |
| Outcome: | The proposed model outperforms baseline methods on CNN/Daily Mail and Gigaword datasets. |
CODEPROMPTZIP: Code-specific Prompt Compression for Retrieval-Augmented Generation in Coding Tasks with LMs (2026.findings-acl)
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| Challenge: | Existing prompt compression techniques for natural language lack fine-grained control over compression ratios. |
| Approach: | They propose a code-aware prompt compression framework for RAG that enables precise length control while preserving critical information. |
| Outcome: | The proposed framework outperforms baselines on three code-related tasks while maintaining the most informative tokens. |
Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models (2021.eacl-main)
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| Challenge: | Existing calibration methods rescale posterior distributions of classifiers after training. |
| Approach: | They propose to use a noise contrastive estimation technique to train an energy-based model during finetuning of pretrained text encoders. |
| Outcome: | The proposed model can reach a better calibration competitive to strong baselines with little or no loss in accuracy. |
Exploiting Noisy Data in Distant Supervision Relation Classification (N19-1)
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| Challenge: | Existing approaches to relation classification are noisy and time-consuming . RCEND uses noisy data to split noisy data into correctly and incorrectly labeled data . |
| Approach: | They propose a framework to enhance relation classification by exploiting noisy data . they use an instance discriminator with reinforcement learning to split noisy data into correctly and incorrectly labeled data based on the noisy data. |
| Outcome: | The proposed method outperforms the state-of-the-art models on relation classification . the proposed method is based on a semi-supervised learning method . |
DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity Recogition (2022.coling-1)
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| Challenge: | Existing approaches to named entity recognition ignore domain-specific information and suffer from subtype conflicts. |
| Approach: | They propose a machine reading comprehension framework which can identify domain-specific semantic differences and mitigate the subtype conflicts between domains. |
| Outcome: | The proposed framework can identify domain-specific semantic differences and mitigate the subtype conflicts between domains. |
How do decoding algorithms distribute information in dialogue responses? (2023.findings-eacl)
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| Challenge: | Using different decoding algorithms, we find that human dialogue generation is beneficial for adherence to the Uniform Information Density principle. |
| Approach: | They investigate whether decoding algorithms implicitly follow the Uniform Information Density principle by distributing information evenly in utterances. |
| Outcome: | The proposed method encourages non-uniform responses, but under low/high surprisal conditions, resulting in poor quality responses. |
KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation (2022.findings-naacl)
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| Challenge: | Existing vision-and-language pretraining approaches rely on external object detectors to encode images in a multi-modal transformer framework. |
| Approach: | They propose an object-aware end-to-end VLP framework which feeds image grid features from CNNs into the Transformer and learns the multi-modal representations jointly. |
| Outcome: | The proposed framework achieves competitive or superior performances on vision-language tasks. |
Cut the Deadwood Out: Backdoor Purification via Guided Module Substitution (2025.findings-emnlp)
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| Challenge: | Model NLP models are often trained on datasets from untrusted platforms, posing significant risks of data poisoning attacks. |
| Approach: | They propose a retraining-free method that selectively replaces modules in the victim model based on a trade-off signal between utility and backdoor. |
| Outcome: | The proposed method outperforms even the strongest defense baseline against challenging attacks like LWS. |
Towards Visual Question Answering on Pathology Images (2021.acl-short)
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| Challenge: | Pathology imaging is used for identifying the causes and effects of diseases or injuries. |
| Approach: | They propose a pathological visual question answering framework to analyze pathology images and answer medical questions related to these images. |
| Outcome: | The proposed framework performs self-supervised pretraining and finetuning end-to-end to learn powerful visual and textual representations jointly and automatically identifies and excludes noisy self-controlled examples from pretraining. |
MdEval: Massively Multilingual Code Debugging (2026.findings-acl)
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Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li
| Challenge: | Existing benchmarks primarily focus on Python and are limited in terms of language diversity. |
| Approach: | They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions. |
| Outcome: | The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task. |
Agentic Verification for Ambiguous Query Disambiguation (2026.findings-acl)
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Youngwon Lee, Seung-won Hwang, Ruofan Wu, Feng Yan, Danmei Xu, Moutasem Akkad, Zhewei Yao, Yuxiong He
| Challenge: | Prior Diversify-then-Verify pipelines generate interpretations and then retrieve evidence . ambiguous queries require RAG to disambiguate into interpretations that can be answered from corpus . |
| Approach: | They propose a novel approach that unifies diversification with verification by integrating retriever relevance and generator answerability feedback early. |
| Outcome: | The proposed approach improves grounding-aware F1 by 23% over baselines across multiple LLMs. |
Think Faster Than Words: Efficient LLM Chain-of-Thought Reasoning via Dynamic Shortcut Decoding (2026.acl-long)
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| Challenge: | Existing methods that prune or employ early stopping to reduce latency often compromise reasoning reliability. |
| Approach: | They propose a shortcut decoding framework that integrates probes over internal hidden states with step-level entropy to detect convergence of reasoning during generation and adaptively selects between a fast-exit path and a stability-verified path to remove redundant steps while preserving answer correctness. |
| Outcome: | The proposed framework reduces token usage by approximately 35% and maintains accuracy comparable to full CoT decoding. |
Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities (2024.findings-eacl)
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| Challenge: | Existing studies treat ideology as a liberal/conservative binary and fail to capture the spectrum of ideologies that may organically emerge in interconnected online communities. |
| Approach: | They propose a method that uses finetuning language models to probe nuanced ideologies of online communities by analyzing discussions of the 2020 election on Twitter. |
| Outcome: | The proposed approach shows higher alignment than baselines for the proposed approach. |
BERT-QE: Contextualized Query Expansion for Document Re-ranking (2020.findings-emnlp)
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| Challenge: | Existing methods to expand query use pseudo relevance feedback (PRF) but they are under-equipped to evaluate the relevance of information pieces used for expansion. |
| Approach: | They propose a query expansion model that leverages the BERT model to select relevant document chunks for expansion. |
| Outcome: | The proposed model significantly outperforms existing models on the TREC Robust04 and GOV2 test collections. |
RAP-ID: Mechanistic Prompt Injection Detection via Impostor Behavior Analysis (2026.findings-acl)
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| Challenge: | Existing defenses rely on externally deployed guardrail models or response inspection . current defenses depend on external guardrails or response inspecting . |
| Approach: | They propose a mechanistic, train-free detection framework that operates exclusively on internal state dynamics during the initial forward pass. |
| Outcome: | The proposed framework achieves competitive performance with significant overall improvements compared to heuristic methods. |
Do Text-to-Vis Benchmarks Test Real Use of Visualisations? (2024.emnlp-main)
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| Challenge: | Existing benchmarks for visualisations are limited and do not reflect real-world use. |
| Approach: | They analysed visualisation code from Python, R, Javascript, and Vega to find similarities and differences between real-world and benchmark datasets. |
| Outcome: | The results show that benchmark datasets do not test the same distribution of chart types, attributes, and actions as real-world examples. |
Does ChatGPT Know That It Does Not Know? Evaluating the Black-Box Calibration of ChatGPT (2024.lrec-main)
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| Challenge: | Recent performance of ChatGPT in downstream tasks is questionable, but does it know that it does not know? |
| Approach: | They propose to use three types of proxy confidence to evaluate ChatGPT's black-box calibration ability. |
| Outcome: | The proposed model exhibits a positive correlation with accuracy in TruthfulQA and a negative correlation in the ModAr dataset. |
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning. (2022.coling-1)
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| Challenge: | Reasoning and knowledge-related skills are considered as fundamental skills for natural language understanding (NLU) tasks. |
| Approach: | They propose a method to diagnose correlations between an NLU dataset and a specific skill. |
| Outcome: | The proposed method is able to diagnose correlations between dataset and logical reasoning skill on 8 MRC and 3 NLI datasets. |
Help me write a Poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing (2022.emnlp-main)
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| Challenge: | Recent work in training large language models to follow natural language instructions has opened up exciting opportunities for natural language interface design. |
| Approach: | They propose to train large language models to follow natural language instructions and to test whether LLMs improve the quality of the generated content. |
| Outcome: | The proposed system is competitive to publicly available LLMs trained on instructions and can satisfy unseen compositional instructions. |
d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models (2026.acl-long)
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Leyi Pan, Shuchang Tao, Yunpeng Zhai, Zheyu Fu, Liancheng Fang, Minghua He, Lingzhe Zhang, Zhaoyang Liu, Bolin Ding, Aiwei Liu, Lijie Wen
| Challenge: | Existing RL methods suffer from reliability bottlenecks due to reward sparsity and intractable computations . d-TreeRPO provides fine-grained and verifiable step-wise reward signals . |
| Approach: | They propose a reliable reinforcement learning framework for diffusion large language models that leverages tree-structured rollouts and bottom-up advantage computation based on verifiable outcome rewards. |
| Outcome: | The proposed framework outperforms baseline models and achieves significant improvements across reasoning benchmarks. |
CoCoNUTS: Concentrating on Content while Neglecting Uninformative Textual Styles for AI-Generated Peer Review Detection (2026.acl-long)
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| Challenge: | Existing detectors rely on stylistic cues to distinguish between surface-level language refinement and genuine content generation. |
| Approach: | They propose a content-based detection paradigm to detect substantive AI-generation . they propose 'CoCoDet' detector that can detect surface-level language refinement . |
| Outcome: | The proposed detector achieves a macro F1 score of 98.24% on permissible machine-polished reviews and maintains 3.89% false positive rate on real-world reviews. |
An Adaptive Prompt Generation Framework for Task-oriented Dialogue System (2023.findings-emnlp)
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| Challenge: | Existing black-box large language models (LLMs) have excellent performance in task-oriented dialogue (TOD) tasks, but obtaining suitable prompts for specific tasks is challenging. |
| Approach: | They propose a black-box large language model that generates domain and slot information in the belief state, which serves as prior knowledge for subsequent prompt generation. |
| Outcome: | The proposed framework outperforms existing prompting methods on the MultiWOZ 2.0 dataset. |
BP4ER: Bootstrap Prompting for Explicit Reasoning in Medical Dialogue Generation (2024.lrec-main)
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| Challenge: | Medical dialogue generation (MDG) has gained increasing attention due to its substantial practical value. |
| Approach: | They propose a method which explicitly models MDG’s multi-step reasoning process and iteratively enhances this reasoning process. |
| Outcome: | The proposed method outperforms state-of-the-art methods across objective and subjective evaluations on two publicly available datasets. |
CAPID: Context-Aware PII Detection for Question-Answering Systems (2026.eacl-srw)
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Mariia Ponomarenko, Sepideh Abedini, Masoumeh Shafieinejad, D. B. Emerson, Shubhankar Mohapatra, Xi He
| Challenge: | Existing approaches mainly redact all PII, disregarding the fact that some may be contextually relevant to the user’s question, resulting in a degradation of response quality. |
| Approach: | They propose a method that fine-tunes a locally owned small language model that filters sensitive information before it is passed to LLMs for QA. |
| Outcome: | The proposed approach outperforms baselines in span, relevance and type accuracy while preserving significantly higher utility under anonymization. |
Creative Natural Language Generation (2023.emnlp-tutorial)
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| Challenge: | This tutorial aims to bring awareness of the important and emerging research area of open-domain creative generation. |
| Approach: | They will review recent studies on creative language generation at sentence level as well as longer forms of text. |
| Outcome: | This paper reviews recent studies on creative language generation at sentence level as well as longer forms of text. |
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated considerable proficiency in general natural language processing tasks. |
| Approach: | They propose a parameter-efficient sparsity crafting method which crafts dense models into sparse models using the mixture-of-experts architecture. |
| Outcome: | The proposed method significantly reduces computational costs and GPU memory requirements, while maintaining the quality of approximation in function space. |
Are We Done with MMLU? (2025.naacl-long)
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Aryo Pradipta Gema, Joshua Ong Jun Leang, Giwon Hong, Alessio Devoto, Alberto Carlo Maria Mancino, Rohit Saxena, Xuanli He, Yu Zhao, Xiaotang Du, Mohammad Reza Ghasemi Madani, Claire Barale, Robert McHardy, Joshua Harris, Jean Kaddour, Emile Van Krieken, Pasquale Minervini
| Challenge: | MMLU is widely adopted but its ground truth errors obscure the true capabilities of LLMs. |
| Approach: | They propose a framework for identifying dataset errors using a novel error annotation protocol and a subset of 5,700 manually re-annotated questions. |
| Outcome: | The proposed framework is based on 5,700 re-annotated questions from the MMLU benchmark. |
CLMTracing: Black-box User-level Watermarking for Code Language Model Tracing (2025.emnlp-main)
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| Challenge: | Open-source code language models (code LMs) are a growing threat for intellectual property protection. |
| Approach: | They propose a black-box code LM watermarking framework that uses rule-based watermarks and utility-preserving injection method for user-level model tracing. |
| Outcome: | The proposed framework shows that it performs well across multiple state-of-the-art code LMs and is harmless compared to existing baselines. |
PAWS: Paraphrase Adversaries from Word Scrambling (N19-1)
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| Challenge: | Existing paraphrase identification datasets lack sentence pairs with high word overlap without being paraphrases. |
| Approach: | They propose a workflow for generating pairs of sentences with high word overlap . they use controlled word swapping and back translation followed by fluency and paraphrase judgments . |
| Outcome: | The proposed dataset has 108,463 well-formed paraphrase and non-paraphrase pairs with high lexical overlap. |
How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior (2026.acl-long)
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Zidi Xiong, Yuping Lin, Wenya Xie, Pengfei He, Zirui Liu, Jiliang Tang, Himabindu Lakkaraju, Zhen Xiang
| Challenge: | In practice, memory designs vary widely across agents due to their diverse objectives and functionalities. |
| Approach: | They conduct an empirical study on how memory management choices impact the LLM agents’ behavior, especially their long-term performance. |
| Outcome: | The proposed methods show that LLM agents display an experience-following property, which results in highly similar agent outputs. |
OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models (2024.acl-long)
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| Challenge: | Existing N-ToM benchmarks lack ambiguous and artificial narratives, lack of personality traits and preferences, and limited diversity in the questions posed. |
| Approach: | They propose a benchmark to assess Neural Theory-of-Mind (N-ToM) with longer and clearer narrative stories, characters with explicit personality traits, actions triggered by character intentions, and questions designed to challenge LLMs’ abilities of modeling characters’ mental states. |
| Outcome: | The proposed test aims to assess the performance of LLMs in the physical and psychological worlds. |
Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Large-scale training datasets make training neural machine translation models difficult. |
| Approach: | They propose to identify inactive training examples which contribute less to the model performance and introduce data rejuvenation to improve NMT models' training. |
| Outcome: | The proposed framework stabilizes and accelerates the training process of NMT models, resulting in models with better generalization capability. |
Language Model Based Unsupervised Dependency Parsing with Conditional Mutual Information and Grammatical Constraints (2024.naacl-long)
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| Challenge: | Existing methods for unsupervised dependency parsing use difficult to interpret dependence scores. |
| Approach: | They propose to use Conditional Mutual Information (CMI) to measure bi-lexical dependence and incorporate grammatical constraints into unsupervised parsing. |
| Outcome: | The proposed model outperforms state-of-the-art models and grammar-based models in five languages and eight datasets. |
Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding (2023.emnlp-main)
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| Challenge: | Chain-of-Thought (CoT) is a technique that guides large language models to decompose complex tasks into multi-step reasoning processes. |
| Approach: | They propose a two-step reasoning framework based on prompt tuning to implement step-by-step thinking for MLMs on NLU tasks. |
| Outcome: | The proposed framework outperforms baselines and achieves state-of-the-art performance on two NLU tasks. |
Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework (2026.acl-industry)
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Ziqiang Zhang, Jing Ma, Zilong Wang, Jiayuan Chen, Yi Qiao, Yu He, Wei Zhang, Dai Cheng, Xiaoyu Shen
| Challenge: | a production-grade pricing system for tourism is challenging due to unstructured nature of travel orders and ever-evolving pricing policies. |
| Approach: | They propose a production-grade pricing system with a strict decision boundary . they propose to combine structured extraction and bounded policy/path selection with interpretable condition trees . |
| Outcome: | The proposed system processed 3,960 orders in six months and reduced the order management team from 15-20 to 3 . the system reduced the per-order handling time from 10 minutes to 2 minutes. |
Don’t Act Blindly: Robust GUI Automation via Action-Effect Verification and Self-Correction (2026.acl-long)
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Yuzhe Zhang, Xianwei Xue, Xingyong Wu, Mengke Chen, Chen Liu, Xinran He, Run Shao, Feiran Liu, Huanmin Xu, Qiutong Pan, Haiwei Wang
| Challenge: | Existing GUI agents assume deterministic environment responses, generating actions without verifying whether previous operations succeeded. |
| Approach: | They propose a GUI agent that explicitly models action outcomes and recovery under noisy environments. |
| Outcome: | The proposed agent reduces failure loops and improves recovery success in noisy environments while maintaining competitive standard task performance. |
Layer-wise Minimal Pair Probing Reveals Contextual Grammatical-Conceptual Hierarchy in Speech Representations (2025.emnlp-main)
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| Challenge: | a recent study evaluated the extent to which SLMs encode nuanced syntactic and conceptual features . acoustic and phonetic features are shallow, but the extent of nuance is unclear . |
| Approach: | a new study evaluates contextual syntactic and semantic features in transformer-based speech language models . authors compare SLMs to linguistic competence assessments for large language models. |
| Outcome: | a new study compares SLMs with linguistic competence assessments to assess speech recognition and understanding . the results show that SLM models encode grammatical features more robustly than conceptual ones . |
BullStop: A Mobile App for Cyberbullying Prevention (2020.coling-demos)
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| Challenge: | Existing tools to combat cyberbullying mostly use wordlists or lack flexibility to cope with the evolving nature of social media. |
| Approach: | BullStop is a mobile app for detecting and preventing cyberbullying and online abuse on social media platforms. |
| Outcome: | BullStop detects and prevents cyberbullying and online abuse on social media platforms and can automatically initiate actions such as deleting offensive messages and blocking bullies on behalf of the user. |
LeanK: Learnable K Cache Channel Pruning for Efficient Decoding (2025.emnlp-main)
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| Challenge: | Existing efforts to optimize the key-value (KV) cache include: (1) Eviction, which discards cache of less important tokens; (2) Selection, which retains the full KV cache but selectively reads relevant entries. |
| Approach: | They propose a learning-based method that prunes unimportant key (K) cache channels by leveraging static channel sparsity. |
| Outcome: | Experiments show that LeanK reduces GPU memory and accelerates decoding without sacrificing accuracy. |
FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation (2025.acl-demo)
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| Challenge: | FlagEvalMM is an evaluation framework designed to assess multimodal models . it is designed to be used for vision-language understanding and generation tasks . |
| Approach: | They propose an evaluation framework that decouples model inference from evaluation through an independent evaluation service. |
| Outcome: | The evaluation framework offers accurate and efficient insights into model strengths and limitations. |
SWE-Swiss: A Multi-Task Fine-Tuning and RL Recipe for High-Performance Issue Resolution (2026.findings-acl)
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Zhenyu He, Qingping Yang, Wei Shen, Xiaojian Zhong, Kechi Zhang, Chenxin An, Wenlei Shi, Tianle Cai, Di He, Jiaze Chen, Jingjing Xu
| Challenge: | SWE-Swiss-32B demonstrates strong generalization to other common LLM benchmarks. |
| Approach: | They propose a two-phase training recipe that decomposes issue resolution into three core skills: Localization, Repair, and Unit Test Generation. |
| Outcome: | The proposed model achieves a 60.2% score on the SWE-bench Verified benchmark and is in the top-tier performance bracket of much larger models. |
Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset (2021.emnlp-main)
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| Challenge: | Existing evaluations on the population task are either not accurate (automatic evaluation with randomly sampled negative examples) or of small scale (human annotation). |
| Approach: | They propose a reasoning over commonsense knowledge bases (CSKBs) that are free-text and have a human annotation set to probe commonsensical reasoning. |
| Outcome: | The proposed model is based on a human-annotated evaluation set and is compared with existing models on the population task. |
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding (P19-1)
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| Challenge: | Neural machine translation models are sensitive to noises in input sentences . one special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. |
| Approach: | They propose to embed phonetic and textual information into neural machine translation datasets to improve robustness to homophone noises. |
| Outcome: | The proposed method improves the robustness of neural machine translation to homophone noises on clean test sets. |
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Gradient-based explanation methods are increasingly used to interpret neural models in natural language processing (NLP) however, in the context of Aspect-based Sentiment Analysis, only specific dimensions are pertinent. |
| Approach: | They propose a Gradient-based explanation framework that leverages an information bottleneck to refine word embeddings into a concise intrinsic dimension, maintaining essential features and omitting unrelated information. |
| Outcome: | The proposed framework improves both the models’ performance and explanations’ clarity by identifying sentiment-aware features. |
COIN – an Inexpensive and Strong Baseline for Predicting Out of Vocabulary Word Embeddings (2022.coling-1)
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| Challenge: | Word embedding models only include terms that occur a sufficient number of times in training corpora. |
| Approach: | They propose a method for predicting word embeddings for out of vocabulary terms using word2vec. |
| Outcome: | The proposed method surpasses several methods on benchmark tasks and is inexpensive to compute. |
Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification (2020.emnlp-main)
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| Challenge: | Experimental results show that D-MILN outperforms recent weakly-supervised baselines . document-level multi-aspect sentiment classification requires a lot of manual aspect-level annotations - which is time-consuming and laborious . |
| Approach: | They propose a novel Diversified Multiple Instance Learning Network to achieve DMSC with only document-level weak supervision. |
| Outcome: | The proposed method outperforms weakly-supervised baselines on TripAdvisor and BeerAdvocate datasets. |
Answer-focused and Position-aware Neural Question Generation (D18-1)
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| Challenge: | Recent neural network-based approaches generate interrogative words that do not match the answer type. |
| Approach: | They propose an answer-focused and position-aware neural question generation model to address these issues. |
| Outcome: | The proposed model outperforms the baseline and outperformed the state-of-the-art system. |
SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection (2026.findings-acl)
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| Challenge: | Conference call transcripts contain significant redundancy and industry-specific terminology that creates obstacles for language models. |
| Approach: | They propose a Sparse Autoencoder for Financial Representation Enhancement framework to extract key information from earnings conference call transcripts and eliminate redundancy. |
| Outcome: | The proposed method outperforms baselines in analyzing earnings conference call transcripts. |
Current Agents Fail to Leverage World Model as Tool for Foresight (2026.acl-long)
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Cheng Qian, Emre Can Acikgoz, Bingxuan Li, Xiusi Chen, Yuji Zhang, Bingxiang He, Qinyu Luo, Gokhan Tur, Dilek Hakkani-Tür, Yunzhu Li, Heng Ji
| Challenge: | Generative world models could be used to enhance agents' cognition . agents are expected to operate in settings where tasks unfold over long horizons and involve intricate chains of interdependent decisions. |
| Approach: | They propose to use vision-language models as external simulators to enhance cognition . they find that agents rarely invoke simulation and misuse predicted rollouts . |
| Outcome: | The proposed model could be used to predict future states rather than short-horizon reasoning . the model could also be used for real-world planning and robotics . |
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation (2023.acl-long)
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| Challenge: | Existing methods to generate empathetic responses are monotonous and generic, resulting in shallow empathy and few connections to the context. |
| Approach: | They propose to use explicit control to guide the empathy expression and a framework DiffusEmp to unify the utilization of dialogue context and attribute-oriented control signals. |
| Outcome: | The proposed framework outperforms baselines on EmpatheticDialogue in terms of controllability, informativeness, diversity, and diversity without the loss of context-relatedness. |
Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning (2020.emnlp-main)
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| Challenge: | Existing methods for integrating knowledge graphs into pre-trained language models have been poorly implemented. |
| Approach: | They propose a self-supervised entity masking scheme that exploits relational knowledge underlying the text. |
| Outcome: | The proposed model achieves improved performance on five benchmarks, including question answering and knowledge base completion. |
MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus (2026.findings-acl)
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Yexing Du, Kaiyuan Liu, Bihe Zhang, Youcheng Pan, Bo Yang, Liangyu Huo, Xiyuan Zhang, Jian Xie, Daojing He, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Multimodal Large Language Models (MLLMs) have advanced Chinese Classical Studies (CCS) but the audio dimension of CCS remains underexplored due to a lack of high-quality, domain-specific audio corpora. |
| Approach: | They propose a 119-hour audio corpus comprising 22,000 audio samples to bridge this gap . it encompasses a diverse range of literary genres across six tasks . |
| Outcome: | The proposed corpus encompasses a diverse range of literary genres across six tasks: Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Speech Emotion Captioning (SEC), Spoken Question Answering ( SQA), Speech Understanding (SU), and Speech Reasoning (SR). |
Query-Driven Multimodal GraphRAG: Dynamic Local Knowledge Graph Construction for Online Reasoning (2025.findings-acl)
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| Challenge: | Existing approaches to build knowledge graphs with LLMs are constrained by static knowledge bases and ineffective multimodal data integration. |
| Approach: | They propose a Query-Driven Multimodal GraphRAG framework that dynamically constructs local knowledge graphs tailored to query semantics. |
| Outcome: | The proposed framework outperforms unsupervised competitors in cross-modal understanding of complex queries. |
CNNSum: Exploring Long-Context Summarization with Large Language Models in Chinese Novels (2025.findings-acl)
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| Challenge: | Currently, long-context summarization mainly relies on memory ability. |
| Approach: | They propose a multi-scale long-context summarization benchmark based on Chinese novels . they use human-driven annotations to analyze long-constituency models . |
| Outcome: | The proposed benchmark features human-driven annotations across four subsets with lengths ranging from 16k to 128k. |
A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation (2022.findings-acl)
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Tianxiang Sun, Xiangyang Liu, Wei Zhu, Zhichao Geng, Lingling Wu, Yilong He, Yuan Ni, Guotong Xie, Xuanjing Huang, Xipeng Qiu
| Challenge: | Existing methods to measure instance difficulty use generalization and threshold-tuning . a new approach to learn to exit is based on hash functions to assign tokens to a fixed exiting layer. |
| Approach: | They propose a Hash-based Early Exiting approach that replaces learn-to-exit modules with hash functions to assign each token to a fixed exiting layer. |
| Outcome: | The proposed approach improves on learning to exit and predicting instance difficulty. |
LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark (2026.findings-acl)
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Guangyi Liu, Pengxiang Zhao, Liang Liu, Zhiming Chen, Yuxiang Chai, Yaozhen Liang, WenHao Wang, Siheng Chen, Zhengxi Lu, Shuai Ren, Hao Wang, Shibo He, Yong Liu, Wenchao Meng
| Challenge: | Mobile GUI agents show promise in automating tasks but face significant generalization challenges in long-tail scenarios. |
| Approach: | They propose a benchmark framework for mobile GUI agents that measures the performance of GUI agents by analyzing their performance. |
| Outcome: | The LearnGUI benchmark outperforms existing methods in offline and online evaluations and demonstrates consistent gains across model architectures. |
Enhancing Local Feature Extraction with Global Representation for Neural Text Classification (D19-1)
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| Challenge: | Existing methods for text classification learn long dependency by deeply stacking or hybrid modeling. |
| Approach: | They propose a global-based local feature extraction architecture with global information incorporated into the local feature extractor. |
| Outcome: | The proposed architecture outperforms the previous best models on eight benchmark datasets. |
Unsupervised Text Style Transfer for Controllable Intensity (2026.findings-eacl)
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| Challenge: | Unsupervised Text Style Transfer (UTST) aims to transfer the stylistic properties of a given text without parallel text pairs. |
| Approach: | They propose a SFT-then-PPO paradigm to fine-tune an LLM with parallel data and reward functions for distinguishing stylistic intensity in hierarchical levels. |
| Outcome: | The proposed system can transfer stylistic properties without parallel text pairs even for adjacent levels of intensity. |
GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR (2026.acl-long)
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| Challenge: | Existing parameter-efficient methods for RLVR face limitations . low-rank adaptation methods do not account for the distinct optimization dynamics . |
| Approach: | They propose a low-rank adaptation method tailored for RLVR that exploits the anisotropic structure of RL update subspace and extracts its principal directions via Singular Value Decomposition (SVD). |
| Outcome: | Experiments on large reasoning models show that GeoRA outperforms strong low-rank baselines across RLVR settings while showing stronger generalization and less forgetting on out-of-domain tasks. |
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge (2020.emnlp-main)
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| Challenge: | Paraphrase identification requires specialized domain knowledge to perform . state-of-the-art neural models and non-expert human annotators have poor performance on PARADE . |
| Approach: | They propose a benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge. |
| Outcome: | The proposed dataset shows state-of-the-art models and non-expert human annotators have poor performance on PARADE. |
PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts (2023.findings-acl)
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| Challenge: | Toward building more robust and reliable conversational systems, we introduce a prompt-based learning approach to automatically generate effective adversarial examples to probe DST models. |
| Approach: | They propose a prompt-based learning approach to automatically generate effective adversarial examples to probe DST models. |
| Outcome: | The proposed framework leads to the greatest reduction in accuracy and the best attack success rate while maintaining good fluency and a low perturbation ratio. |
FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema (2025.coling-main)
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| Challenge: | naive prompts can enhance the task performance of large language models, but they are resource-intensive. |
| Approach: | They propose an automatic prompt optimization method that refines naive prompts according to task outputs from in-box testing models. |
| Outcome: | The proposed method is based on a large-scale dataset and performed fairly across multiple models. |
Automatic Slide Updating with User-Defined Dynamic Templates and Natural Language Instructions (2026.findings-acl)
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| Challenge: | Existing automation methods follow fixed template filling and cannot support dynamic updates for diverse, user-authored decks. |
| Approach: | They propose a framework that combines multimodal slide parsing, natural language instruction grounding, and tool-augmented reasoning for tables, charts, and textual conclusions. |
| Outcome: | The proposed framework updates content while preserving layout and style while maintaining a strong reference baseline on DynaSlide. |
Implicit Sentiment Analysis with Event-centered Text Representation (2021.emnlp-main)
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| Challenge: | Existing methods for implicit sentiment analysis simply view noun phrases or entities in text as events or indirectly model events with sophisticated models. |
| Approach: | They propose an event-centric implicit sentiment analysis that utilizes the sentiment-aware event contained in a sentence to infer sentiment polarity. |
| Outcome: | The proposed model can detect sentiment in sentences without sentiment words and is compared to existing models on a benchmark dataset. |
MiCEval: Unveiling Multimodal Chain of Thought’s Quality via Image Description and Reasoning Steps (2025.naacl-long)
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Xiongtao Zhou, Jie He, Lanyu Chen, Jingyu Li, Haojing Chen, Victor Gutierrez Basulto, Jeff Z. Pan, Hanjie Chen
| Challenge: | Existing methods for evaluating the quality of reasoning steps in multimodal chain-of-thought are lacking. |
| Approach: | They propose a framework to evaluate the correctness of reasoning chains by evaluating the quality of both the description and each reasoning step. |
| Outcome: | The proposed framework improves interpretability and human judgments on four state-of-the-art MLLMs. |
Query-Efficient Textual Adversarial Example Generation for Black-Box Attacks (2024.naacl-long)
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| Challenge: | Existing black-box attacks require thousands of queries on the target model, making them expensive in real-world applications. |
| Approach: | They propose a new approach that guides word substitutions using prior knowledge from the training set to improve the attack efficiency. |
| Outcome: | The proposed approach reduces query-free attack and guided search attacks by a factor of 10 500 . it improves transferability and generalization by the ensemble of the ABPens in NLP . |
Omni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code Generation (2026.acl-long)
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Jiawei Zhou, Chi Zhang, Xiang Feng, Qiming Zhang, Haibo Qiu, Lihuo He, Dengpan Ye, Xinbo Gao, Jing Zhang
| Challenge: | a benchmark is designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured digital graphics into executable code. |
| Approach: | They propose a benchmark to evaluate the capability of Large Multimodal Models to convert digital graphics into executable code. |
| Outcome: | The proposed benchmark exposes the performance gap among leading LMMs . the benchmark features 1130 meticulously curated samples . |
ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation (2025.findings-acl)
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| Challenge: | Existing benchmarks lack the ability to automatically evaluate from users’ perspective and lack the explainability of the results of LLM agents’ code generation capabilities. |
| Approach: | They propose a new benchmark for LLM agents' automated evaluation by simulating user interaction. |
| Outcome: | The proposed benchmark can evaluate the generated projects by user interaction simulation and by code similarity through existing objective indicators. |
Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization (2023.acl-long)
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| Challenge: | Document-level multi-event extraction aims to extract the structural information from a given document automatically. |
| Approach: | They propose an alternative approach for document-level multi-event extraction with event proxy nodes and Hausdorff distance minimization. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two datasets with only a fraction of training time. |
2D-DPO: Scaling Direct Preference Optimization with 2-Dimensional Supervision (2025.findings-naacl)
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Shilong Li, Yancheng He, Hui Huang, Xingyuan Bu, Jiaheng Liu, Hangyu Guo, Weixun Wang, Jihao Gu, Wenbo Su, Bo Zheng
| Challenge: | Existing methods that optimize for scalar scores or ranking reward ignore multi-dimensional nature of human preferences. |
| Approach: | They propose to extend the preference of Direct Preference Optimization to two dimensions: segments and aspects. |
| Outcome: | The proposed framework decomposes the overall objective into multi-segment and multi-aspect objectives. |
Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline (2022.coling-1)
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| Challenge: | Pre-trained language models have demonstrated their effectiveness for few-shot table understanding, but few-shoot table understanding is rarely explored due to the deficiency of public table pre-training corpus and well-defined downstream benchmark tasks. |
| Approach: | They establish a benchmark dataset and use it to explore few-shot table understanding in Chinese. |
| Outcome: | The proposed model improves the few-shot table understanding in Chinese. |
SCOPE: Optimizing Key-Value Cache Compression in Long-context Generation (2025.acl-long)
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| Challenge: | Excessive compression during the prefill phase impairs comprehension of reasoning tasks . SCOPE is a framework that performs KV cache optimization during the decoding and prefill phases . |
| Approach: | They propose a framework that performs optimization during the prefill and decoding phases . they propose enabling a sliding strategy to select essential heavy hitters for the decoding phase . |
| Outcome: | Experiments show that SCOPE can optimize key-value cache for long-context generation tasks . the framework can preserve essential information while minimizing memory usage and transfer . |
Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation (2022.acl-long)
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| Challenge: | Experimental results show that backtranslation improves UNMT performance by reducing the data gap between training and inference. |
| Approach: | They propose an online method to remedy the source discrepancy between training and inference . they use pseudo parallel data with translated source and translated target to mimic inference scenario . |
| Outcome: | The proposed method outperforms baselines on several widely-used language pairs by remedying the style and content gaps. |
PrefScore: Pairwise Preference Learning for Reference-free Summarization Quality Assessment (2022.coling-1)
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| Challenge: | Existing studies on summarization evaluation without a human-written reference summary have shown high correlations with human ratings. |
| Approach: | They propose to judge summary quality by learning preference rank from corrupted summaries . they use Bradley-Terry power ranking model to learn preference rank . |
| Outcome: | Experiments on several datasets show that the proposed model can produce scores highly correlated with human ratings. |
A Unified Syntax-aware Framework for Semantic Role Labeling (D18-1)
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| Challenge: | Syntactic information has been paid a great attention over the role of enhancing SRL . but the gap between syntax-aware and syntax-gnostic SRL is smaller . a new framework proposes syntax-based SRL for a wide range of NLP tasks . |
| Approach: | They propose to extend existing models to investigate more effective ways of incorporating syntax into sequential neural networks. |
| Outcome: | The proposed framework outperforms existing models on CoNLL-2009 benchmarks in English and Chinese. |
C-LLM: Learn to Check Chinese Spelling Errors Character by Character (2024.emnlp-main)
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| Challenge: | Chinese Spell Checking (CSC) aims to detect and correct spelling errors in sentences. |
| Approach: | They propose a Chinese Spell Checking method that learns to check errors Character by Character. |
| Outcome: | The proposed method achieves a 2.1% enhancement in general scenarios and a significant improvement in vertical domain scenarios compared to existing methods. |
Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims (2022.naacl-main)
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| Challenge: | Existing datasets focus on a single medium, information domain or specific application . authors propose novel methods for automated veracity assessment based on Natural Language Inference . |
| Approach: | They propose to build a PANACEA dataset that combines different data sources with different foci to ensure a unique set of claims. |
| Outcome: | The proposed methods are competitive with SOTA methods and provide a detailed discussion. |
De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation (2021.acl-long)
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| Challenge: | Logical table-to-text generation is challenging where deep learning models capture surface-level spurious correlations rather than the causal relationships between the table x and the sentence y. |
| Approach: | They propose to use variational inference to estimate the confounders in the latent space and cooperate with the causal intervention based on Pearl’s do-calculus to alleviate the spurious correlations. |
| Outcome: | The proposed model outperforms baselines and achieves new state-of-the-art performance on two logical table-to-text datasets in terms of logical fidelity. |
Efficient Nearest Neighbor Language Models (2021.emnlp-main)
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| Challenge: | Non-parametric neural language models (NLMs) learn text distributions by memorizing training data points. |
| Approach: | They propose to use an external datastore to learn from a non-parametric language model. |
| Outcome: | The proposed methods achieve up to a 6x speed-up in inference speed while retaining comparable performance. |
Synonym-unaware Fast Adversarial Training against Textual Adversarial Attacks (2025.findings-naacl)
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| Challenge: | Existing adversarial defense methods rely on predetermined linguistic knowledge and assume that attackers’ synonym candidates are known, which is often unrealistic. |
| Approach: | They propose a Fast Adversarial Training method that leverages single-step perturbation generation and effective perturbation initialization to improve model robustness without requiring synonym awareness. |
| Outcome: | Experiments show that the proposed method outperforms existing models under character-level and word-level attacks while still maintaining the correct syntax. |
Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer (2024.acl-long)
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| Challenge: | Experimental results indicate that GPST significantly outperforms the existing speech language models in terms of word error rate, speech quality, and speaker similarity. |
| Approach: | They propose a hierarchical transformer that quantizes audio waveforms into two distinct types of discrete speech representations and integrates them within a transformer architecture. |
| Outcome: | The proposed model outperforms existing speech language models in word error rate, speech quality, and speaker similarity. |
SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis (2021.emnlp-main)
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| Challenge: | Crypto markets are forums where goods and services are exchanged between parties who use encryption to conceal their identities. |
| Approach: | They propose a stylometry-based multitask learning approach for natural language and model interactions using graph embeddings. |
| Outcome: | The proposed approach outperforms existing methods in four darknet forums with a lift of up to 2.5X on the mean retrieval rank and 2X on recall@10. |
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension (D19-1)
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| Challenge: | End-to-end reading comprehension models have been successful at extracting text answers, but there are still problems with generalizing them to abstractive numerical reasoning. |
| Approach: | They propose to augment a BERT-based reading comprehension model with a set of executable ‘programs’ which encompass simple arithmetic as well as extraction. |
| Outcome: | The proposed model can perform 33% absolute improvement on the DROP dataset, with very few training examples. |
PrAd: Prompt Adaptive Tuning for Decoder-only Language Models (2025.findings-emnlp)
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| Challenge: | Prompt-based methods suffer from increased input lengths and sensitivity to weight initialization . adapter-based approaches can substantially increase inference time . |
| Approach: | a new paradigm for prompt-based tuning addresses the problem of fine tuning pretrained models . prompt--based methods suffer from increased input lengths and sensitivity to weight initialization . a prompt-oriented approach employs adapters for flexible input transformation . |
| Outcome: | a proposed framework can achieve comparable or better performance and higher inference efficiency even in multi-task scenarios. |
All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection (2026.acl-long)
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Yuechen Jiang, Zhiwei Liu, Yupeng Cao, Yueru He, Ziyang Xu, Chen Xu, Zhiyang Deng, Prayag Tiwari, Xi Chen, Alejandro Lopez-Lira, Jimin Huang, Junichi Tsujii, Sophia Ananiadou
| Challenge: | RFC-Bench evaluates large language models on financial misinformation under realistic news . current models struggle to maintain coherent belief states without external grounding, study finds . |
| Approach: | They propose a benchmark for evaluating large language models on financial misinformation under realistic news. |
| Outcome: | The proposed model performs better when context is available, while reference-free settings expose significant weaknesses. |
MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs (2022.emnlp-demos)
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| Challenge: | Existing medical dialogue systems have the problems of weak scalability, insufficient knowledge, and poor controllability. |
| Approach: | They propose a medical conversational question-answering system based on the knowledge graph to improve scalability and controllability. |
| Outcome: | The proposed system can conduct knowledge-grounded dialogues with users, using a Chinese medical knowledge graph and a large-scale dataset. |
Hatevolution: What Static Benchmarks Don’t Tell Us (2025.findings-acl)
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| Challenge: | Language evolves rapidly following social dynamics and cultural shifts. |
| Approach: | They empirically evaluate the robustness of 20 language models across two evolving hate speech experiments and propose time-sensitive benchmarks for their work. |
| Outcome: | The proposed model evaluations show that the language models are misaligned between static and time-sensitive evaluations. |
Can LLM Agents Simulate Multi-Turn Human Behavior? Evidence from Real Online Customer Behavior Data (2026.acl-long)
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Yuxuan Lu, Jing Huang, Yan Han, Bingsheng Yao, Sisong Bei, Yaochen Xie, Yisi Sang, Qi He, Dakuo Wang
| Challenge: | Recent research shows that LLM Agents can generate “believable” human behaviors via prompt-only methods, leaving open questions of whether they can accurately generate step-by-step actions in multi-turn interaction tasks. |
| Approach: | They propose to use shopping data to evaluate LLMs' ability to accurately generate step-by-step actions in a multi-turn interaction task. |
| Outcome: | The proposed model achieves 17.26% action generation accuracy and 33.86% F1 score on final purchase prediction, representing improvements of 5.4% and 13.85% over baselines. |
TENP: Trapezoidal Expert Neuron Pruning For Mixture-of-Experts (2026.findings-acl)
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| Challenge: | Existing compression approaches remove entire experts, disrupting routing topology and harming performance, or rely on unstructured weight pruning with limited practical efficiency. |
| Approach: | They propose a structured **T**rapezoidal **E**xpert **N**euron **P**running framework that uses a trapezoidal pattern to identify and retain important experts while applying expert neuron pruning (ENP) to less important experts. |
| Outcome: | The proposed framework outperforms the full-parameter model by 10% on code generation tasks under a sparse activation of experts and a 40% routing sparsity. |
RAGViz: Diagnose and Visualize Retrieval-Augmented Generation (2024.emnlp-demo)
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| Challenge: | Large language models (LLMs) lack domain-specific knowledge and can cause hallucinations. |
| Approach: | They propose a RAG diagnosis tool that visualizes the attentiveness of the generated tokens in retrieved documents. |
| Outcome: | RAGViz provides token and document-level attention visualization and generation comparison upon context document addition and removal. |
Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing (2023.tacl-1)
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| Challenge: | Sequence-to-Sequence (S2S) models have been successful on text generation tasks . however, learning complex structures with S2S models remains challenging . |
| Approach: | They propose to use constrained decoding to model part-of-speech tagging, named entity recognition, constituency, and dependency parsing tasks with 3 lexically diverse linearization schemas and corresponding constrained coding methods. |
| Outcome: | The proposed methods outperform the state-of-the-art on four core tasks. |
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages (2024.naacl-long)
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Jiayi Wang, David Adelani, Sweta Agrawal, Marek Masiak, Ricardo Rei, Eleftheria Briakou, Marine Carpuat, Xuanli He, Sofia Bourhim, Andiswa Bukula, Muhidin Mohamed, Temitayo Olatoye, Tosin Adewumi, Hamam Mokayed, Christine Mwase, Wangui Kimotho, Foutse Yuehgoh, Anuoluwapo Aremu, Jessica Ojo, Shamsuddeen Muhammad, Salomey Osei, Abdul-Hakeem Omotayo, Chiamaka Chukwuneke, Perez Ogayo, Oumaima Hourrane, Salma El Anigri, Lolwethu Ndolela, Thabiso Mangwana, Shafie Mohamed, Hassan Ayinde, Oluwabusayo Awoyomi, Lama Alkhaled, Sana Al-azzawi, Naome Etori, Millicent Ochieng, Clemencia Siro, Njoroge Kiragu, Eric Muchiri, Wangari Kimotho, Toadoum Sari Sakayo, Lyse Naomi Wamba, Daud Abolade, Simbiat Ajao, Iyanuoluwa Shode, Ricky Macharm, Ruqayya Iro, Saheed Abdullahi, Stephen Moore, Bernard Opoku, Zainab Akinjobi, Abeeb Afolabi, Nnaemeka Obiefuna, Onyekachi Ogbu, Sam Ochieng’, Verrah Otiende, Chinedu Mbonu, Yao Lu, Pontus Stenetorp
| Challenge: | Recent advances in machine translation (MT) have focused on scaling multilingual machine translation models and evaluation data to hundreds of languages, including multiple under-resourced languages. |
| Approach: | They propose to use n-gram matching metrics to measure progress in multilingual machine translation to 13 typologically diverse African languages to create high-quality human evaluation data with simplified MQM guidelines. |
| Outcome: | The proposed metrics have a higher correlation with human judgments than n-gram matching metrics such as BLEU and METEOR. |
Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management (2026.acl-long)
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Weitao Ma, Xiaocheng Feng, Lei Huang, Xiachong Feng, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Bing Qin
| Challenge: | Existing approaches to memory management rely on final task performance as the primary reward, resulting in severe reward sparsity and ineffective credit assignment. |
| Approach: | They propose a framework for fine-grained feedback alignment using a Chunk-level step reward and Evidence-Anchored Reward Attribution to redistribute global rewards based on memory items utilized as evidence in reasoning. |
| Outcome: | The proposed framework outperforms baselines and supports generalization across different model configurations and backbones. |
QueryLink: Leveraging Query-Memory Alignment for Long-Term Reasoning in LLM Agents (2026.findings-acl)
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| Challenge: | Existing approaches to integrating external memory prioritize memory organization while overlooking a critical semantic gap between implicit, intent-driven queries and explicit, narrative-based memories. |
| Approach: | They propose a framework that leverages Query-Memory Alignment to project both queries and memories into a shared semantic space. |
| Outcome: | The proposed framework significantly outperforms SOTA methods on the LoCoMo and LongMemEval benchmarks and can be integrated as a plug-and-play component to boost existing vector-based systems like A-MEM. |
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods to solve complex logical reasoning problems are cumbersome for language models. |
| Approach: | They propose to use iterative methodology to construct a cognitive tree using language models . they propose to generate multiple responses by utilizing in-context examples . |
| Outcome: | The proposed model achieves a performance level comparable to that of GPT-3.5 . the proposed model contains fewer parameters than 5% of the model with 175B parameters . |
Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media (2022.naacl-main)
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| Challenge: | Existing approaches to detect vaccine attitudes on social media require abundant annotations and pre-defined aspect categories. |
| Approach: | They propose a semi-supervised approach to detect vaccine attitudes on social media . they use an autoencoding architecture to learn from unlabelled data the topical information of the domain . |
| Outcome: | The proposed model outperforms existing aspect-based models on stance detection and tweet clustering. |
Neural Caption Generation for News Images (L18-1)
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| Challenge: | Existing methods for automatic caption generation of images are lacking in the field of image-related applications. |
| Approach: | They propose a method for automatically generating captions for news images . they propose several deep neural network architectures built upon Recurrent Neural Networks . |
| Outcome: | The proposed method outperforms a traditional method on a BBC News dataset using automatic evaluation and human evaluation. |
InteMATs: Integrating Granularity-Specific Multilingual Adapters for Cross-Lingual Transfer (2023.findings-emnlp)
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| Challenge: | Existing work relies on full-model fine-tuning on large parallel datasets to enhance cross-lingual alignment of MLLMs. |
| Approach: | They propose an approach that integrates multilingual adapters trained on texts of different levels of granularity into multilingual models. |
| Outcome: | The proposed approach improves the performance of multilingual language models on low-resource languages. |
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (2022.acl-long)
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| Challenge: | Argument mining (AM) is a computational process that is used to analyze information in a debating system. |
| Approach: | They propose to use a large dataset to automate the manual process of debating . they propose to integrate claim extraction, stance classification and evidence extraction tasks . |
| Outcome: | The proposed tasks can extract claims, stances, evidence and more from a large dataset . the proposed tasks are highly efficient and can be applied to argument mining tasks . |
Multi-agent Learning for Neural Machine Translation (D19-1)
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| Challenge: | Experimental results show that training with more than one agent improves translation quality and improves accuracy. |
| Approach: | They propose to introduce diverse agents in an in- teractive updating process to train NMT models with an additional agent. |
| Outcome: | The proposed approach improves on NIST Chinese-English, IWSLT 2014 German- English, WMT 2014 English-German translation tasks and shows competitive performance on all tasks. |
PRINCE: Prefix-Masked Decoding for Knowledge Enhanced Sequence-to-Sequence Pre-Training (2022.emnlp-main)
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| Challenge: | Existing studies focus on injecting noises into the input sequence, but feasibility of injecting them into the decoding sequence remains an open question. |
| Approach: | They propose a pre-training paradigm that integrates knowledge-enhanced decoding with noises in the prefix to strengthen the representation learning of entities that span over multiple input tokens. |
| Outcome: | The proposed model achieves state-of-the-art results on two knowledge-driven data-to-text generation tasks with up to 2% BLEU gains. |
View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond (2022.coling-1)
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| Challenge: | Existing models for dialogue summarization focus on document summarizing on time and speaker-centered points, but this approach is limited in understanding the dialogue. |
| Approach: | They propose a 2D view of dialogue based on a time-speaker perspective where the time and speaker streams of dialogue can be obtained as strengthened input. |
| Outcome: | The proposed model outperforms existing models on the QMSum dataset and improves summary faithfulness and human evaluation. |
Vanessa: Visual Connotation and Aesthetic Attributes Understanding Network for Multimodal Aspect-based Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing methods to analyze images focus on superficial features or descriptions, omitting subtle contextual information. |
| Approach: | They propose a Visual Connotation and Aesthetic Attributes Understanding Network (Vanessa) for Multimodal Aspect-based Sentiment Analysis. |
| Outcome: | The proposed network captures both implicit and explicit sentimental cues and can be used to enrich textual sentiment analysis. |
FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure (2026.acl-demo)
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Fan Zhang, Mingzi Song, Rania Elbadry, Yankai Chen, Shaobo Wang, Yixi Zhou, Xunwen Zheng, Yueru He, Yuyang Dai, Georgi Nenkov Georgiev, Ayesha Gull, Muhammad Usman Safder, Fan Wu, Liyuan Meng, Fengxian Ji, Junning Zhao, Xueqing Peng, Jimin Huang, YU Chen, Xue Liu, Preslav Nakov, Zhuohan Xie
| Challenge: | FinReporting is an agentic workflow for localized cross-jurisdiction financial reporting . existing approaches assume a single-market setting and overlook structural differences across jurisdictions . |
| Approach: | They propose a workflow that decomposes financial reporting into auditable stages . they use Large Language Models to extract and summarize corporate disclosures . |
| Outcome: | The proposed system decomposes reporting into auditable stages . it improves consistency and reliability under heterogeneous reporting regimes. |
Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning (C18-1)
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| Challenge: | Existing methods for implicit discourse relation recognition ignore bidirectional interactions between two arguments and sparsity of pair patterns. |
| Approach: | They propose a neural Tensor network framework with interactive attention and sparse learning for implicit discourse relation recognition. |
| Outcome: | The proposed framework is effective on PDTB and can be used in text summarization, conversation system and so on. |
WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models (2023.emnlp-industry)
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Jun-Yan He, Zhi-Qi Cheng, Chenyang Li, Jingdong Sun, Wangmeng Xiang, Xianhui Lin, Xiaoyang Kang, Zengke Jin, Yusen Hu, Bin Luo, Yifeng Geng, Xuansong Xie
| Challenge: | Existing typography solutions lack adaptability, creativity, and computational efficiency. |
| Approach: | They propose a user-driven framework for artistic typography synthesis based on the Large Language Model (LLM) the LLM Engine interprets user inputs and generates actionable prompts for the other modules, transforming abstract concepts into tangible designs. |
| Outcome: | The proposed framework incorporates four key modules: the LLM Engine, SemTypo, StyTyPo, and TexTyPO. |
On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models (2024.acl-short)
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Dongyang Li, Junbing Yan, Taolin Zhang, Chengyu Wang, Xiaofeng He, Longtao Huang, Hui Xue’, Jun Huang
| Challenge: | Existing RAG methods focus on improving the task performance, without fine-grained process of knowledge. |
| Approach: | They propose a method that detects long-tail knowledge in large language models by analyzing retrieved documents and enhancing queries indiscriminately with retrieved information. |
| Outcome: | The proposed method achieves over 4x speedup in average inference time and consistent performance improvement in downstream tasks compared to existing pipelines. |
Better Literary Translation: A Multi-Aspect Data Generation and LLM Training Approach (2026.acl-industry)
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| Challenge: | Literary translation requires balancing expression fluency with literary effect due to the scarcity of high-quality training data and the difficulty of capturing nuanced quality trade-offs. |
| Approach: | They propose a multi-aspect iterative refinement framework that generates high-quality translation references and preference data through specialized LLM translators. |
| Outcome: | The proposed models outperform the ground truth for SFT by 8.65 CEA100 points while leveraging an explicit reward model for GRPO yields an additional 1.51 point improvement. |
Beyond Self-Report: Bridging the Intention-Behavior Gap in Critical Thinking Assessment via Interpretable Multi-Agent System (2026.acl-long)
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Zekun Li, Jifan Yu, Haoxuan Li, Ye He, Daniel Zhang-Li, Shangqing Tu, Joy Jia Yin Lim, Yikun Jiang, Jiaxin Yuan, Yu Zhang
| Challenge: | Accurate assessment of critical thinking is limited by the Intention Behavior Gap in psychology . evaluators that measure self-reported competence are limited by multiagent architectures . |
| Approach: | They propose a framework that operationalizes cognitive assessment into an interpretable multi-agent workflow with Assessment Chain-of-Thought. |
| Outcome: | The proposed framework aligns better with human expert ratings than gold-standard inventories on large-scale simulations and human participants. |
SEEK: Segmented Embedding of Knowledge Graphs (2020.acl-main)
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| Challenge: | Existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them far from satisfactory. |
| Approach: | They propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. |
| Outcome: | The proposed framework can achieve highly competitive relational expressiveness without increasing model complexity. |
Robustness-Aware Word Embedding Improves Certified Robustness to Adversarial Word Substitutions (2023.findings-acl)
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| Challenge: | Embedding interval bound constraint is important for NLP models to be certified robust, but adversarial examples can be crafted by synonym substitutions. |
| Approach: | They propose a triplet loss to train robustness-aware word embeddings for better certified robustness. |
| Outcome: | The proposed method outperforms state-of-the-art certified defense baselines and generalizes well to unseen substitutions. |
ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models (2023.emnlp-demo)
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Chenliang Li, He Chen, Ming Yan, Weizhou Shen, Haiyang Xu, Zhikai Wu, Zhicheng Zhang, Wenmeng Zhou, Yingda Chen, Chen Cheng, Hongzhu Shi, Ji Zhang, Fei Huang, Jingren Zhou
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior. |
| Approach: | They propose a framework that equips large language models with tool-use capabilities . they propose LLaMA and Chat-GLM as controllers, and a model-based agent framework . |
| Outcome: | The proposed framework equips open-source LLMs with tool-use capabilities . it provides a user-friendly system library with a customizable engine design . |
Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference (P18-1)
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| Challenge: | Existing approaches to natural language inference focus on interaction architectures of sentences . but, we propose to transfer knowledge from discourse markers to augment the model . |
| Approach: | They propose to transfer knowledge from discourse markers to augment the quality of the NLI model. |
| Outcome: | The proposed method achieves state-of-the-art performance on large-scale datasets. |
BiRRE: Learning Bidirectional Residual Relation Embeddings for Supervised Hypernymy Detection (2020.acl-main)
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| Challenge: | supervised hypernymy detection has been studied under various frameworks . supervised classifiers are more likely to suffer from "lexical memorization" |
| Approach: | They propose a representation learning framework called Bidirectional Residual Relation Embeddings to model the possibility of a term being mapped to another in the embedding space by hypernymy relations. |
| Outcome: | The proposed model outperforms baselines over evaluation frameworks. |
TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation (2026.findings-acl)
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Bangde Du, Minghao Guo, Songming He, Ziyi Ye, Xi Zhu, Weihang Su, Shuqi Zhu, Yujia Zhou, Yongfeng Zhang, Qingyao Ai, Yiqun Liu
| Challenge: | Existing studies show that advanced LLMs produce text indistinguishable from human writing. |
| Approach: | They propose a benchmark to assess persona simulation across diverse contexts by decomposing the evaluation into six fundamental capabilities including opinion consistency, memory recall, logical reasoning, persona tone, and syntactic style. |
| Outcome: | The proposed model achieves moderate accuracy but falls short of the basic capabilities needed to simulate personas in real-world contexts. |
Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network (2022.coling-1)
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| Challenge: | Text classification is a primary task in natural language processing (NLP). |
| Approach: | They propose a graph neural network (HINT) that makes full use of hierarchical information contained in the text for the task of text classification. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters. |
What Breaks Knowledge Graph based RAG? Benchmarking and Empirical Insights into Reasoning under Incomplete Knowledge (2026.eacl-long)
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Dongzhuoran Zhou, Yuqicheng Zhu, Xiaxia Wang, Hongkuan Zhou, Yuan He, Jiaoyan Chen, Steffen Staab, Evgeny Kharlamov
| Challenge: | Existing evaluation metrics and lenient answer matching criteria obscure meaningful comparisons. |
| Approach: | They propose a general method for constructing benchmarks and a method to assess KG-RAG methods under incomplete knowledge. |
| Outcome: | The proposed method systematically assesses KG-RAG methods under incomplete knowledge. |
Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts (2026.acl-long)
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| Challenge: | Existing methods for IE tasks suffer from inconsistent schema representation and implicitly intermediate reasoning . UC-UIE adopts a low-rank adapted hierarchical Mixture-of-Experts adapter for UIE tasks . |
| Approach: | They propose a framework that decomposes IE reasoning into three universal capabilities . UC-UIE adopts a low-rank Adaptation adapter to fine-tune LLMs for IE tasks . |
| Outcome: | The proposed framework outperforms full-parameter tuning methods with 1.24% trainable parameters and outperformed existing methods in generalization and interpretability. |
Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT (2023.emnlp-main)
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Xiaoshuai Song, Keqing He, Pei Wang, Guanting Dong, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
| Challenge: | Existing methods to fine-tune discriminative models address these challenges by focusing on in-domain intents. |
| Approach: | They evaluate ChatGPT on OOD intent discovery and generalized intent discovery tasks . they outline the strengths and weaknesses of ChatGPt and outline their results . |
| Outcome: | The proposed task aims to extend a closed intent classifier to open-world intent sets. |
Decomposing Argumentative Essay Generation via Dialectical Planning of Complex Reasoning (2024.findings-acl)
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| Challenge: | Argumentative Essay Generation (AEG) is a challenging task in computational argumentation, where detailed logical reasoning and effective rhetorical skills are essential. |
| Approach: | They propose an argumentative planning strategy for prompting large language models to generate high-quality essays by sketch planning and dialectical planning. |
| Outcome: | The proposed method generates more dialectical and persuasive essays with higher diversity compared to baselines. |
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling (2024.lrec-main)
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| Challenge: | Existing topic models rely on bag-of-words (BOW) representations to capture word order information. |
| Approach: | They propose a neural topic model that integrates contextualized word embeddings from BERT to learn the topic vector of a document without BOW information. |
| Outcome: | The proposed model generates more coherent and meaningful topics compared to existing models while accommodating unseen words in newly encountered documents. |
Safety in Large Reasoning Models: A Survey (2025.findings-emnlp)
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Cheng Wang, Yue Liu, Baolong Bi, Duzhen Zhang, Zhong-Zhi Li, Yingwei Ma, Yufei He, Shengju Yu, Xinfeng Li, Junfeng Fang, Jiaheng Zhang, Bryan Hooi
| Challenge: | Large Reasoning Models (LRMs) have a high level of advanced reasoning capabilities, but they are vulnerable and vulnerable. |
| Approach: | This paper presents the first comprehensive survey of Large Reasoning Models . it explores the new safety risks, attacks, and defense strategies specific to LRMs based on reasoning . |
| Outcome: | The proposed study examines the safety and security risks of large reasoning models. |
PhiloGPT: A Philology-Oriented Large Language Model for Ancient Chinese Manuscripts with Dunhuang as Case Study (2024.emnlp-main)
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Yuqing Zhang, Baoyi He, Yihan Chen, Hangqi Li, Han Yue, Shengyu Zhang, Huaiyong Dou, Junchi Yan, Zemin Liu, Yongquan Zhang, Fei Wu
| Challenge: | philology requires years of professional training in extensive knowledge memorization and manual textual retrieval. |
| Approach: | They curated the PhiloCorpus-ZH, a rich collec-tion of ancient Chinese texts spanning a millennium with 30 diverse topics, including firsthand folk copies. |
| Outcome: | The PhiloCorpus-ZH corpus facilitated the development of the first LLM tailored for discovering ancient Chinese manuscripts. |
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding (2023.emnlp-main)
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Taolin Zhang, Ruyao Xu, Chengyu Wang, Zhongjie Duan, Cen Chen, Minghui Qiu, Dawei Cheng, Xiaofeng He, Weining Qian
| Challenge: | Existing methods for pre-training KEPLMs with relational triples are difficult to adapt to close domains due to the lack of sufficient domain graph semantics. |
| Approach: | They propose a Knowledge-enhanced language representation learning framework for various closed domains that captures the implicit graph structure among the entities. |
| Outcome: | The proposed framework outperforms existing methods for pre-training KEPLMs in closed domains significantly. |
ChemAmp: Amplified Chemistry Tools via Composable Agents (2026.findings-acl)
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Zhucong Li, Powei Chang, Jin Xiao, Zhijian Zhou, Qianyu He, Jiaqing Liang, Fenglei Cao, Xu Yinghui, Yuan Qi
| Challenge: | LLM-based agents are powerful tools for automating complex scientific workflows, especially in chemistry, but their single-task performance is limited by tool constraints. |
| Approach: | They propose a framework that optimizes the collective capabilities of specialized tools by dynamic coordination within individual tasks. |
| Outcome: | The proposed framework outperforms chemistry-specialized models, generalist LLMs, and agent systems with tool orchestration. |
ALLSH: Active Learning Guided by Local Sensitivity and Hardness (2022.findings-naacl)
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| Challenge: | Existing studies show that labeling in crowdsourcing annotations is not an annotation artifact but rather a core linguistic phenomenon. |
| Approach: | They propose to retrieve unlabeled data with a local sensitivity and hardness-aware acquisition function. |
| Outcome: | The proposed method achieves consistent gains over the commonly used active learning strategies in various classification tasks. |
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation (2022.naacl-main)
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Guangyi Liu, Zichao Yang, Tianhua Tao, Xiaodan Liang, Junwei Bao, Zhen Li, Xiaodong He, Shuguang Cui, Zhiting Hu
| Challenge: | Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss. |
| Approach: | They propose an Edit-Invariant Sequence Loss method which computes the matching loss of a target sequence with all n-grams in the generated sequence. |
| Outcome: | The proposed method outperforms the common CE loss and strong baselines on a wide range of tasks. |
Rethinking-based Code Summarization with Chain of Comments (2025.coling-main)
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| Challenge: | Existing methods focus on learning a direct mapping from pure code to summaries, overlooking the heterogeneity gap between code and summary. |
| Approach: | They propose a framework that uses chain of comments as auxiliary intermediate information to bridge the gap between code and summaries. |
| Outcome: | The proposed framework outperforms baseline models and multiple code Large Language Models by a large margin. |
CHESS: Optimizing LLM Inference via Channel-Wise Thresholding and Selective Sparsification (2024.emnlp-main)
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| Challenge: | Existing methods for activation sparsification do not capture the relationship between activation and model performance. |
| Approach: | They propose a general activation sparsification approach using channel-wise thresholding and selective sparsifying to capture the relationship between activation and model performance. |
| Outcome: | The proposed approach reduces the number of activated neurons during inference by 1.27x over eight downstream tasks while activating fewer parameters than existing methods. |
Automatic Construction of Enterprise Knowledge Base (2021.emnlp-demo)
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| Challenge: | Existing knowledge bases are often based on bootstrapping entities from human-curated sources such as Wikipedia. |
| Approach: | They propose to build a knowledge base from enterprise documents with minimal human intervention by using deep learning models and classical machine learning techniques. |
| Outcome: | The proposed system is currently serving as part of a Microsoft 365 service. |
Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts (2022.coling-1)
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| Challenge: | Existing methods for knowledge Graph Completion (KGC) fail in unseen relation representations. |
| Approach: | They propose to use three kinds of graphs to obtain unseen relation representations . they propose to decouple mixture-of-graph experts (DMoG) for unseened relations learning . |
| Outcome: | The proposed method outperforms the state-of-the-art methods on unseen relation representations. |
Empowering Reliable Visual-Centric Instruction Following in MLLMs (2026.findings-acl)
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| Challenge: | Existing benchmarks for evaluating instruction-following capabilities focus on verbal instructions in the textual modality. |
| Approach: | They propose to incorporate vision-dependent constraints into instruction design to enable a more rigorous assessment of how well MLLMs align their outputs with both visual input and textual instructions. |
| Outcome: | The proposed benchmark incorporates vision-dependent constraints into instruction design, enabling a more rigorous and fine-grained assessment of how well MLLMs align their outputs with both visual input and textual instructions. |
Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy (2020.acl-main)
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| Challenge: | Knowledge-driven conversation approaches have attracted considerable research attention in recent years. |
| Approach: | They propose a method that integrates recurrent knowledge interaction among response decoding steps to incorporate appropriate knowledge. |
| Outcome: | The proposed method improves on two datasets Wizard-of-Wikipedia and DuConv with different knowledge formats and different languages. |
DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)
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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability. |
| Approach: | They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. |
| Outcome: | The proposed model outperforms baselines on three real-world datasets. |
TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation (P18-1)
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Alexander Fabbri, Irene Li, Prawat Trairatvorakul, Yijiao He, Weitai Ting, Robert Tung, Caitlin Westerfield, Dragomir Radev
| Challenge: | TutorialBank is a publicly available dataset that aims to facilitate NLP education and research . a google search of "Natural Language Processing" returns over 100 million hits with papers, tutorials, 1 http://aan.how blog posts, codebases and other related online resources. |
| Approach: | They have manually collected and categorized over 5,600 resources on NLP . they have created a search engine and command-line tool to search the corpus . |
| Outcome: | The tutorial bank dataset is the largest manually-picked corpus of resources intended for NLP education . it includes lists of research topics, relevant resources for each topic, prerequisite relations among topics . |
PreAct: Prediction Enhances Agent’s Planning Ability (2025.coling-main)
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| Challenge: | Existing methods to analyze Markov decision processes (MDPs) are based on chain-of-thought (COT) and historical thought, action, and observation. |
| Approach: | They propose a model that integrates prediction, reasoning, and action with other models to provide a wider range of reasoning and more efficient actions. |
| Outcome: | The proposed model outperforms the ReAct method in completing complex tasks and is more efficient when paired with other memory or selection strategy techniques. |
Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction (2021.naacl-main)
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| Challenge: | Recent studies on ABSA focus on Target-oriented Opinion Words (or Terms) Extraction . Experimental results indicate that TSMSA outperforms the benchmark methods on TOWE significantly . |
| Approach: | They propose to use a pre-trained language model with multi-head self-attention to integrate TOWE with AOPE to extract aspects and opinion terms in pairs. |
| Outcome: | The proposed structure outperforms the benchmark methods on TOWE significantly . the proposed structure is similar or even better than state-of-the-art AOPE models . |
Negative-Aware Diffusion Process for Temporal Knowledge Graph Extrapolation (2026.findings-eacl)
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| Challenge: | Temporal Knowledge Graphs (TKGs) are dynamic structures representing entities and their evolving relationships through time. |
| Approach: | They propose a non-parametric model that encodes subject-centric histories into sequential embeddings. |
| Outcome: | The proposed model encodes subject-centric histories of entities, relations and temporal intervals into sequential embeddings. |
NaturalGAIA: A Verifiable Benchmark and Hierarchical Framework for Long-Horizon GUI Tasks (2026.acl-long)
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| Challenge: | Current research faces an "Evaluation-Realism Dilemma" due to unstable MLLM judges or manual verification. |
| Approach: | They propose a verifiable evaluation dataset grounded in real-world human GUI intents. |
| Outcome: | The proposed framework outperforms the state-of-the-art framework in achieving a weighted pathway success rate of 45.6% while reducing token consumption and execution time by 76%. |
Jailbreak Large Vision-Language Models Through Multi-Modal Linkage (2025.acl-long)
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| Challenge: | Existing methods to jailbreak large vision-language models fail against cutting-edge models such as GPT-4o, despite having undergone safety alignment training. |
| Approach: | They propose a new framework for jailbreaking large vision-language models that uses an encryption-decryption process to mitigate the over-exposure of harmful information. |
| Outcome: | The proposed framework jailbreaks GPT-4o with 99.40% success rates on SafeBench, 98.81% on MM-SafeBench and 99.07% on HADES-Dataset. |
Partial Or Complete, That’s The Question (N19-1)
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| Challenge: | Existing annotation schemes aim at acquiring completely annotated structures, but partial annotations can be costly and hinder learning. |
| Approach: | They propose a method to find out that learning from partial structures can sometimes outperform learning from complete ones. |
| Outcome: | The proposed method outperforms existing methods in three different structured learning tasks. |
Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization? (2024.emnlp-main)
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| Challenge: | Text summarization is a key natural language generation task, but the high cost of inaccurate summaries raises concerns about the reliability of uncertainty estimation on text summarisation (UE-TS) evaluation methods. |
| Approach: | They propose a UE-TS benchmark that evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets. |
| Outcome: | The proposed benchmark evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets, with human-annotation analysis incorporated where applicable. |
Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework (2026.findings-acl)
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Jiaqi Weng, Han Zheng, Hanyu Zhang, Ej Zhou, Qinqin He, Jialing Tao, Hui Xue, Zhixuan Chu, Xiting Wang
| Challenge: | Existing studies on how SAEs derive most fine-grained latent features for safety remain unexplored. |
| Approach: | They propose a framework for interpreting SAE features in safety-critical domains . they train a suite of SAEs with human-readable explanations and systematic evaluations based on pornography, politics, violence, and terror . |
| Outcome: | The proposed framework reduces interpretation cost by 55% and improves safety-critical features. |
Vocabulary Pyramid Network: Multi-Pass Encoding and Decoding with Multi-Level Vocabularies for Response Generation (P19-1)
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| Challenge: | Conventional methods employ a fixed vocabulary and one-pass decoding, which make them prone to safe and general responses and lack further refinement to the first generated raw sequence. |
| Approach: | They propose a Vocabulary Pyramid Network which integrates multi-pass encoding and decoding with multi-level vocabularies into response generation. |
| Outcome: | The proposed system outperforms strong baselines on English Twitter and Chinese Weibo datasets. |
Better Language Model with Hypernym Class Prediction (2022.acl-long)
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| Challenge: | Class-based language models (LMs) have been devised to address context sparsity in n-gram LMs for decades. |
| Approach: | They propose to use class-based prediction to improve generalization for rare words by annealing from predicting the class to token prediction during training. |
| Outcome: | The proposed model improves perplexity without sacrificing performance on rare words. |
KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals (2021.findings-emnlp)
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| Challenge: | Existing frameworks for multi-subgoal dialogs require a system to build a social bond with users to gain trust and develop affinity. |
| Approach: | They propose a framework for common knowledge-based multi-subgoal dialogs that divides up conversations with multiple subgoals and propose mechanisms to filter noisy knowledge and to include cleaned knowledge in the dialog response generation process. |
| Outcome: | The proposed framework obtains state-of-the-art results on a DuRecDial dataset in both automatic and human evaluation. |
Demonstration Retrieval-Augmented Generative Event Argument Extraction (2024.lrec-main)
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| Challenge: | Experimental results show that our method outperforms all strong baselines and can be generalized to various datasets. |
| Approach: | They propose a generative EAE that uses event knowledge-injected generator and demonstration retriever to generate event arguments from training data. |
| Outcome: | The proposed method outperforms baselines and can be generalized to various datasets. |
XMC-Agent : Dynamic Navigation over Scalable Hierarchical Index for Incremental Extreme Multi-label Classification (2024.findings-acl)
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Yanjiang Liu, Tianyun Zhong, Yaojie Lu, Hongyu Lin, Ben He, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing methods for XMC struggle with the growing set of labels due to their static label assumptions, and embedding-based methods struggle with complex mapping relationships due to late interaction paradigm. |
| Approach: | They propose a large language model (LLM) powered agent framework for extreme multi-label classification, XMC-Agent, which can effectively learn, manage and predict the extremely large and dynamically increasing set of labels. |
| Outcome: | The proposed framework can learn, manage and predict the extremely large and dynamically growing set of labels and achieves state-of-the-art performance on three standard datasets. |
The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning (2025.findings-acl)
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Bingxiang He, Ning Ding, Cheng Qian, Jia Deng, Ganqu Cui, Lifan Yuan, Haiwen Hong, Huan-ang Gao, Longtao Huang, Hui Xue, Huimin Chen, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing work on instruction tuning has focused on task level, without considering that tasks are artificially defined and, to LLMs, merely consist of tokens and representations. |
| Approach: | They propose a training data arrangement framework that allows for continual learning and loss reduction. |
| Outcome: | The proposed framework promotes continual learning and loss reduction on unseen tasks. |
Discourse-Aware Neural Rewards for Coherent Text Generation (N18-1)
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| Challenge: | Existing approaches to train text generation models using cross-entropy loss do not always correlate well with achieving high scores on commonly used evaluation measures. |
| Approach: | They propose to use discourse-aware rewards to model cross-sentence ordering to approximate desired discourse structure to train a model of long, coherent text. |
| Outcome: | The proposed model produces more coherent and less repetitive text than models trained with cross-entropy or with commonly used scores as rewards. |
Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards (2026.acl-long)
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Youliang Yuan, Qiuyang Mang, Jingbang Chen, Hong Wan, Xiaoyuan Liu, Junjielong Xu, Jen-tse Huang, Wenxuan Wang, Wenxiang Jiao, Pinjia He
| Challenge: | Existing models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning ability. |
| Approach: | They propose a Rubric Reward Model that rewards the entire reasoning trajectory against problem-specific rubrics. |
| Outcome: | The proposed model outperforms outcome-only supervision on four math benchmarks and boosts Verified Pass@1024 from 26.7% to 62.6% and reduces the incidence of Miracle Steps by 71%. |
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains (2025.naacl-long)
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Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He
| Challenge: | Retrieval-augmented generation (RAG) enhances the question answering abilities of large language models (LLMs) however, adapting general-purpose RAG systems to specialized fields poses unique challenges due to distribution shifts and limited access to domain-specific data. |
| Approach: | They propose a method that equips large language models with joint capabilities of question answering and question generation for domain adaptation. |
| Outcome: | Experiments on 11 datasets across three different domains verify the efficacy of SimRAG over baselines by 1.2%–8.6%. |
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks (2021.emnlp-main)
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Qingbin Liu, Pengfei Cao, Cao Liu, Jiansong Chen, Xunliang Cai, Fan Yang, Shizhu He, Kang Liu, Jun Zhao
| Challenge: | Existing offline DST models require a fixed dataset to train . Existing domain-lifelong learning methods are impractical in real-world applications . |
| Approach: | They propose a domain-lifelong learning method to continuously train a DST model on new data to learn incessantly emerging new domains while avoiding catastrophically forgetting old learned domains. |
| Outcome: | The proposed method outperforms state-of-the-art lifelong learning methods by 4.25% and 8.27% on the MultiWOZ and the SGD benchmarks. |
English-to-Chinese Transliteration with Phonetic Auxiliary Task (2020.aacl-main)
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| Challenge: | Several NMT techniques have been used to enhance machine transliteration models, but few focus on the linguistic features specific to the relevant languages. |
| Approach: | They propose a phonetic auxiliary task that integrates phonetic features into a model to improve generalization performance of the main transliteration task. |
| Outcome: | The proposed model achieves similar performance to the current state of the art with a much smaller size. |
Does Large Language Model Contain Task-Specific Neurons? (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities in comprehensively handling various types of natural language processing (NLP) tasks. |
| Approach: | They propose a method for task-specific neuron localization based on Causal Gradient Variation with Special Tokens (CGVST) this method identifies task- specific neurons by concentrating on the most significant tokens during task processing, eliminating redundant tokens and minimizing interference from non-essential neurons. |
| Outcome: | The proposed method can locate task-specific neurons across eight public tasks. |
Multi-perspective Preference Alignment of LLMs for Programming-Community Question Answering (2025.coling-main)
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| Challenge: | Extensive experiments on a high-quality, real-world PCQA dataset validate its accuracy and preference. |
| Approach: | They propose a multi-perspective preference alignment for programming-community question answering to generate user-centric responses. |
| Outcome: | Experiments on a high-quality, real-world PCQA dataset validate the proposed model's accuracy and preference. |
MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension (2024.findings-emnlp)
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| Challenge: | Existing models generate erroneous information and evaluations fail to assess factual correctness of models. |
| Approach: | They propose to use MoleculeQA to evaluate molecular factual correctness in large language models by organizing molecules into a taxonomy and building QA pairs through human and LLM efforts. |
| Outcome: | The proposed model improves the factual correctness of generated information and enables the development of new models. |
Teaching Small Language Models to Reason for Knowledge-Intensive Multi-Hop Question Answering (2024.findings-acl)
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| Challenge: | Large language models can teach small language models to solve complex reasoning tasks by Chain-of-thought Distillation (CoTD) e.g., mathematical question answering. |
| Approach: | They propose a method that distills two student models to solve a multi-hop question . they use chain-of-thought distillation to generate step-by-step reasoning paths . |
| Outcome: | The proposed method surpasses existing methods on knowledge-intensive multi-hop questions. |
AMR-TST: Abstract Meaning Representation-based Text Style Transfer (2023.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input. |
| Approach: | They propose an AMR-based text style transfer technique that converts source text to an AML graph and generates transferred text based on the AMR graph modified by a TST policy named style rewriting. |
| Outcome: | The proposed method achieves state-of-the-art results compared with baseline models in automatic and human evaluations. |
GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? (2025.emnlp-main)
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Yiyang Zhou, Linjie Li, Shi Qiu, Zhengyuan Yang, Yuyang Zhao, Siwei Han, Yangfan He, Kangqi Li, Haonian Ji, Zihao Zhao, Haibo Tong, Lijuan Wang, Huaxiu Yao
| Challenge: | Existing video benchmarks often resemble image-based questions with scans of only a few key frames, without deep temporal reasoning. |
| Approach: | They propose a video benchmark to assess whether large vision-language models can genuinely think with videos rather than perform superficial frame-level analysis. |
| Outcome: | The proposed benchmark consists of 3,269 videos and over 4,342 highly visual-centric questions across 11 categories, including Trajectory Analysis, Temporal Reasoning, and Forensics Detection. |
Rethinking Round-Trip Translation for Machine Translation Evaluation (2023.findings-acl)
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| Challenge: | Automatic evaluation methods for translation often require model training and the availability of parallel corpora limits their applicability to low-resource settings. |
| Approach: | They revisit the statistical machine translation technique and use it to improve translation quality. |
| Outcome: | The proposed method improves translation quality estimation models and identifies adversarial competitors in shared tasks via cross-system verification. |
Event Temporal Relation Extraction with Bayesian Translational Model (2023.eacl-main)
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| Challenge: | Existing methods to extract temporal relations between events lack a principled method to incorporate external knowledge. |
| Approach: | They propose a Bayesian-based method that models the temporal relation representations as latent variables and infers their values via Bayessian inference and translational functions. |
| Outcome: | The proposed method outperforms existing methods for event temporal relation extraction on three widely used datasets. |
AIR: Complex Instruction Generation via Automatic Iterative Refinement (2025.emnlp-main)
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| Challenge: | Existing methods for generating complex instructions are resource-intensive and lack diversity. |
| Approach: | They propose a framework to generate complex instructions with constraints using a document-generated initial instruction and an iterative refinement framework to incorporate LLM-as-judge guidance. |
| Outcome: | The proposed framework significantly outperforms existing methods for generating complex instructions, and outperformed existing methods. |
Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation Perspective (2024.emnlp-main)
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| Challenge: | Recent studies focus on monosemanticity on its basic units. |
| Approach: | They propose to revisit monosemanticity from the feature decorrelation perspective and advocate for its encouragement. |
| Outcome: | The proposed method improves representation diversity and activation sparsity and improves preference alignment performance. |
S-RAG: A Novel Audit Framework for Detecting Unauthorized Use of Personal Data in RAG Systems (2025.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) systems rely on external data for accurate and context-specific responses. |
| Approach: | They propose a framework that enables users to determine whether their textual data has been utilized in RAG systems even in black-box settings with no prior system knowledge. |
| Outcome: | The proposed framework achieves an improvement in Accuracy by 19.9% while maintaining strong performance under adversarial defenses. |
Accelerating Prefilling via Decoding-time Contribution Sparsity (2026.findings-acl)
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| Challenge: | Existing acceleration methods exploit attention score sparsity by estimating blocks with high attention scores and applying dynamic sparse attention. |
| Approach: | They propose a method which replaces dense attention with Triangle attention in a subset of layers to reduce the time needed to decode. |
| Outcome: | Experiments show that TriangleMix achieves near-lossless performance on long-context and long-constrast reasoning benchmarks while significantly improving efficiency. |
Explicit vs. Implicit: Investigating Social Bias in Large Language Models through Self-Reflection (2025.findings-acl)
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| Challenge: | Existing methods to quantify and quantify social biases in Large Language Models (LLMs) focus on explicit bias, with little attention to implicit bias. |
| Approach: | They propose a self-reflection-based evaluation framework that measures implicit bias and evaluates explicit bias by prompting LLMs to analyze their own generated content. |
| Outcome: | The proposed framework compares explicit and implicit biases in large language models . it demonstrates that explicit bias manifests as mild stereotypes, while implicit bias exhibits strong stereotypes. |
CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection (2025.findings-acl)
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| Challenge: | Multimodal large language models have demonstrated impressive capabilities in visual reasoning and text generation. |
| Approach: | They propose a multimodal large language model that captures deeper relationships between images and text . they propose CMIE, which uses a Coexistence Relationship Generation strategy and an AS mechanism to detect misinformation. |
| Outcome: | The proposed framework outperforms existing methods in detecting out-of-context misinformation. |
Revisit Self-Debugging with Self-Generated Tests for Code Generation (2025.acl-long)
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Xiancai Chen, Zhengwei Tao, Kechi Zhang, Changzhi Zhou, Xinyu Zhang, Wanli Gu, Yuanpeng He, Mengdi Zhang, Xunliang Cai, Haiyan Zhao, Zhi Jin
| Challenge: | Large language models (LLMs) have made significant advances in code generation, but they still face challenges when tackling complex programming tasks beyond their basic capabilities. |
| Approach: | They propose to integrate self-generated tests into the code generation process . they propose to use post-execution and in-exection self-debugging to mitigate test bias . |
| Outcome: | The proposed method improves the performance of large language models in code generation tasks by leveraging execution feedback from tests. |
MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.findings-acl)
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Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context. |
| Approach: | They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts. |
| Outcome: | The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context. |
From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language Models (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) follow instructions with elaborate requirements, yet it remains under-explored how to enhance their ability to follow complex instructions with multiple constraints. |
| Approach: | They propose a method to obtain and utilize effective training data to enhance LLMs' ability to follow complex instructions with multiple constraints. |
| Outcome: | The proposed framework improves models' ability to follow instructions generally and generalize effectively across out-of-domain, in domain, and adversarial settings while maintaining general capabilities. |
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations (2023.acl-long)
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| Challenge: | In-context learning is an important paradigm for adapting large language models to new tasks . but the generalization behavior of ICL remains poorly understood . |
| Approach: | They characterize the feature biases of large language models by constructing underspecified demonstrations . they find that LLMs exhibit clear feature bias, and they evaluate interventions . |
| Outcome: | The proposed model prefers the "default" task features over distractor features more often than the base model. |
UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity Mixture-of-Experts (2026.acl-long)
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Zhenyu Liu, Yunxin li, Xuanyu Zhang, Qixun Teng, Shenyuan Jiang, Xinyu Chen, Haoyuan Shi, Haolan Chen, Fanbo Meng, Mingjun Zhao, Yu Xu, Yancheng He, Baotian Hu, Haizhou Li, Min Zhang
| Challenge: | Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation. |
| Approach: | They propose a unified speech and music generation model built upon a novel framework . they propose specialized MoE architectures and curated training strategies to tackle data imbalances . |
| Outcome: | The proposed model achieves state-of-the-art performance on major speech and music generation benchmarks. |
Cognitive Bias in Decision-Making with LLMs (2024.findings-emnlp)
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| Challenge: | Large language models inherit societal biases against protected groups and can be subject to functionally resembling cognitive bias. |
| Approach: | They propose a framework to uncover, evaluate, and mitigate cognitive bias in large language models by using a dataset containing 13,465 prompts to evaluate LLM decisions on different cognitive biases. |
| Outcome: | The proposed framework uncovers, evaluates, and mitigates cognitive bias in large language models, particularly in high-stakes decision-making tasks. |
Visual Question Decomposition on Multimodal Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing methods for question decomposition focus on unimodal language models, but question decomposing capability of Multimodal Large Language Models (MLLMs) has yet to be explored. |
| Approach: | They propose a finetuning dataset and a training objective for selective decomposition to enhance the model's question decomposing capability. |
| Outcome: | The proposed dataset shows that existing models struggle to produce high-quality sub-questions. |
A2ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization (2025.findings-acl)
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Junhui He, Junna Xing, Nan Wang, Rui Xu, Shangyu Wu, Peng Zhou, Qiang Liu, Chun Jason Xue, Qingan Li
| Challenge: | Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache. |
| Approach: | They propose a retrieval-based method to reduce the memory footprint of LLMs . they propose Windowed Rotary Position Embedding and query-aware vector quantization . |
| Outcome: | The proposed method can achieve lower performance degradation with lower overhead compared to existing methods . it can reduce the memory footprint and access overhead of long context large language models . |
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems (2020.coling-main)
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| Challenge: | Existing task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. |
| Approach: | They propose a dual dynamic memory network for multi-turn dialog generation . the model dynamically expands the dialog memory turn by turn and keeps track of dialog history . |
| Outcome: | The proposed model outperforms baseline models on three benchmark datasets on human evaluation and automatic evaluation. |
Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification (2023.findings-emnlp)
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| Challenge: | Existing approaches to hierarchical multi-label text classification (HMTC) ignore the correlation between similar samples and introduce noise . |
| Approach: | They propose a semi-supervised method that uses a label hierarchy to bring text and label embeddings closer to each other by supervised contrastive learning. |
| Outcome: | The proposed method bridges the gap between supervised contrastive learning and HMTC by bringing text and label embeddings closer. |
A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation (2020.coling-main)
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| Challenge: | Paraphrase generation is a longstanding problem in natural language processing (NLP) Neural network-based methods have shown great progress on paraphrase generation. |
| Approach: | They propose a framework that integrates variational inference on a target-related latent variable to introduce the diversity. |
| Outcome: | The proposed framework outperforms baseline models on the metrics based on n-gram matching and semantic similarity, and it can generate multiple different paraphrases by assembling different syntactic variables. |
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings (2020.tacl-1)
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| Challenge: | Experimental results show that the proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
| Approach: | They propose a generative model that explores local and global context for joint learning topics and topic-specific word embeddings. |
| Outcome: | The proposed model outperforms word-level embedding methods in word similarity evaluation and word sense disambiguation. |
How Far are We from Robust Long Abstractive Summarization? (2022.emnlp-main)
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| Challenge: | Abstractive summarization has made tremendous progress in recent years . however, even under a short document setting, abstractive models often generate summaries that are repetitive, ungrammatical, and factually inconsistent with the source. |
| Approach: | They perform fine-grained human annotations to evaluate long document abstractive summarization systems and develop factual consistency metrics. |
| Outcome: | The proposed model can generate more relevant summaries but not factual ones. |
ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents (2026.acl-long)
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| Challenge: | Existing approaches to measuring and optimizing proactive task-oriented agents lack generalizable end-to-end solutions. |
| Approach: | They propose a framework for conversational task scheduling that integrates proactiveness reinforcement learning with a domain-agnostic annotation methodology. |
| Outcome: | The proposed framework enables scalable proactiveness reinforcement learning (RL) Experiments on two newly auto-annotated datasets demonstrate significant improvements in proactive timing while maintaining action consistency comparable to state-of-the-art baselines. |
PromptKeeper: Safeguarding System Prompts for LLMs (2025.findings-emnlp)
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| Challenge: | PromptKeeper is a defense mechanism designed to safeguard system prompts . adversarial and regular queries can exploit LLM vulnerabilities to expose hidden prompts. |
| Approach: | PromptKeeper is a defense mechanism designed to safeguard system prompts . it detects both explicit and subtle leakage and regenerates responses using a dummy prompt . |
| Outcome: | PromptKeeper detects and mitigates side-channel vulnerabilities when prompts are exposed . it regenerates responses using a dummy prompt, ensuring outputs remain indistinguishable from typical interactions . |
Making Pre-trained Language Models Better Learn Few-Shot Spoken Language Understanding in More Practical Scenarios (2023.findings-acl)
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| Challenge: | Existing few-shot Spoken Language Understanding models need to be trained on a set of data-rich source domains and adapt to the target domain with a few examples. |
| Approach: | They propose a scenario where only a pre-trained language model and a few labeled examples are used to train few-shot SLU models. |
| Outcome: | The proposed model outperforms existing models on few-shot settings by reducing the number of slot labels and reducing training complexity. |
WebWalker: Benchmarking LLMs in Web Traversal (2025.acl-long)
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Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, Fei Huang
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks. |
| Approach: | They propose a benchmark to assess the ability of LLMs to perform web traversal by using an explore-critic paradigm. |
| Outcome: | The proposed framework mimics human-like web navigation through an explore-critic paradigm and demonstrates the effectiveness of RAG combined with WebWalker in real-world scenarios. |
Not All Voices Are Rewarded Equally: Probing and Repairing Reward Models across Human Diversity (2025.findings-emnlp)
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Zihao Li, Feihao Fang, Xitong Zhang, Jiaru Zou, Zhining Liu, Wei Xiong, Ziwei Wu, Baoyu Jing, Jingrui He
| Challenge: | Using real-world datasets, we conduct the most comprehensive study to date, auditing various state-of-the-art reward models across nine sensitive attributes, including age, gender, ethnicity, etc. |
| Approach: | They propose a method to mitigate group disparities in reward modeling by using real-world data. |
| Outcome: | The proposed method is based on a population-based dataset with nine demographic attributes, including gender, ethnicity, age, gender, and ethnicity. |
OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems (2024.acl-long)
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Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, Maosong Sun
| Challenge: | Large Language Models (LLMs) and Large Multimodal Models have exceeded general human capabilities in various tasks. |
| Approach: | They present an Olympiad-level bilingual multimodal scientific benchmark featuring 8,476 problems from Olympiad level mathematics and physics competitions. |
| Outcome: | The best performing model, GPT-4V, attains an average score of 17.97% on OlympiadBench, with a mere 10.74% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning. |
Shuttle Between Symbolic Instructions and Neural Parameters of Large Language Models (2026.acl-long)
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| Challenge: | Despite their distinct external representations, a deeper analysis reveals their intrinsic nature: instructions serve as a natural language compression devised by humans for data governing specific mapping patterns, whereas parameters act as 'neuro compression' of the same task data. |
| Approach: | They propose a neural network framework to model and learn the bi-directional mappings between instructions and parameters of large language models by evaluating it on the tasks of instruction deduction and induction. |
| Outcome: | The proposed framework can map one of the instructions/parameters to the other by evaluating it on the tasks of instruction deduction and induction. |
MTGP: Multi-turn Target-oriented Dialogue Guided by Generative Global Path with Flexible Turns (2023.findings-acl)
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| Challenge: | Existing approaches focus on global planning, which plans toward the target before the conversation. |
| Approach: | They propose to generate a global path as a natural language sentence instead of a sequence of nodes. |
| Outcome: | The proposed method has fewer turns, more coherent semantics, and higher success rate than baselines. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
A Strategic Coordination Framework of Small LMs Matches Large LMs in Data Synthesis (2025.acl-long)
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| Challenge: | Large Language Models suffer from high computational costs and environmental inefficiency . smaller LMs are more accessible and sustainable, but their individual capabilities often fall short . a collaborative framework for small LM combines specialized roles to iterative refinement and quality control . |
| Approach: | They propose a framework that aggregates specialized roles across small LMs to iterative refinement and quality control typically achieved by a single large LM. |
| Outcome: | The proposed framework aggregates specialized roles across small LMs to iterative refinement and quality control typically achieved by large LM. |
O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning (2026.findings-acl)
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Haotian Luo, Haiying He, Yibo Wang, Shiwei Liu, Wei Li, Xiaochun Cao, Dacheng Tao, Naiqiang Tan, Li Shen
| Challenge: | Recent long-thought reasoning models adopt extended reasoning processes similar to how humans ponder over complex problems. |
| Approach: | They propose a model that uses RL-style fine-tuning to reduce inference overhead while maintaining accuracy. |
| Outcome: | The proposed model reduces inference overhead while maintaining accuracy. |
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks (2022.findings-naacl)
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Bill Yuchen Lin, Chaoyang He, Zihang Ze, Hulin Wang, Yufen Hua, Christophe Dupuy, Rahul Gupta, Mahdi Soltanolkotabi, Xiang Ren, Salman Avestimehr
| Challenge: | Increasing concerns and regulations about data privacy necessitate the study of privacy-preserving, decentralized learning methods for natural language processing tasks. |
| Approach: | They propose a framework for evaluating federated learning methods on four different tasks . they propose federation between Transformer-based language models and FL methods . |
| Outcome: | The proposed framework compares FL methods on four different tasks under non-IID partitioning strategies. |
MLeVLM: Improve Multi-level Progressive Capabilities based on Multimodal Large Language Model for Medical Visual Question Answering (2024.findings-acl)
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Dexuan Xu, Yanyuan Chen, Jieyi Wang, Yue Huang, Hanpin Wang, Zhi Jin, Hongxing Wang, Weihua Yue, Jing He, Hang Li, Yu Huang
| Challenge: | Existing MVQA models ignore multi-level progressive capabilities due to unspecific data and plain architecture. |
| Approach: | They propose a multi-level visual language model for medical visual question answering (MVQA) which covers multi- level questions and answers as well as reasoning processes from visual clues to semantic cognition. |
| Outcome: | The proposed model outperforms existing medical multimodal large language models on a multi-level instruction dataset and a feature alignment module. |
Aligning as Debiasing: Causality-Aware Alignment via Reinforcement Learning with Interventional Feedback (2024.naacl-long)
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| Challenge: | Existing methods to reduce LLMs' biased outputs rely on reward signals from current model outputs without considering the source of biases. |
| Approach: | They propose to leverage the reward model in RL alignment as an instrumental variable to perform causal intervention on LLMs. |
| Outcome: | The proposed method reduces biases by using human feedback to fine tune LLMs to human values. |
StereoRel: Relational Triple Extraction from a Stereoscopic Perspective (2021.acl-long)
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| Challenge: | Existing methods for relational triple extraction still face challenges, including information loss and error propagation. |
| Approach: | They propose a model which maps relational triples to a three-dimensional space and leverages three decoders to extract them. |
| Outcome: | The proposed model outperforms the baselines on five public datasets. |
Breaking the Reasoning Barrier A Survey on LLM Complex Reasoning through the Lens of Self-Evolution (2025.findings-acl)
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Tao He, Hao Li, Jingchang Chen, Runxuan Liu, Yixin Cao, Lizi Liao, Zihao Zheng, Zheng Chu, Jiafeng Liang, Ming Liu, Bing Qin
| Challenge: | OpenAI's O1 and subsequent projects like DeepSeek R1 have significantly advanced research on complex reasoning in LLMs. |
| Approach: | They analyze existing reasoning studies from the perspective of self-evolution and summarize O1-like works from open-source projects like DeepSeek R1 and Kimi-k1.5. |
| Outcome: | The proposed models are based on open-source models and pioneer advanced methodologies like Scaling Reinforcement Learning (RL). |
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization (2026.findings-acl)
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| Challenge: | Existing methods for designing and optimizing multi-agent systems are static and do not learn from experience. |
| Approach: | They propose a framework that enables a multi-agent system to learn to evolve . they use "textual gradients" to pinpoint failures and suggest granular modifications . |
| Outcome: | a new framework enables a multi-agent system to learn to evolve . it learns from historical optimization experiences to improve its performance . |
FiNE: Filtering and Improving Noisy Data Elaborately with Large Language Models (2025.naacl-long)
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| Challenge: | Currently, there are two mainstream methods for improving data integrity: data filtering and data augmentation. |
| Approach: | They propose a method to improve data integrity by combining data filtering and data augmentation with LLMs. |
| Outcome: | The proposed method surpasses the open-source chat version on HalluQA by 8.45 on the open source version. |
k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text (2024.findings-acl)
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| Challenge: | Recent watermarked generation algorithms inject detectable signatures during language generation to facilitate post-hoc detection. |
| Approach: | They propose a watermark which assigns signatures to each watermarked sentence according to locality-sensitive hashing (LSH) they propose k-SemStamp, which uses kmeans clustering to partition the semantic space with awareness of inherent semantic structure. |
| Outcome: | The proposed watermark improves its robustness and sampling efficiency while preserving the generation quality, making it more effective for machine-generated text detection. |
ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations (2025.findings-acl)
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Brihi Joshi, Keyu He, Sahana Ramnath, Sadra Sabouri, Kaitlyn Zhou, Souti Chattopadhyay, Swabha Swayamdipta, Xiang Ren
| Challenge: | Language models are widely used in education, yet their ability to tailor responses to learners with varied informational needs and knowledge backgrounds remains under-explored. |
| Approach: | They conduct two extensive human studies to assess the utility of language model-generated explanatory answers (explanations) on a benchmark of 13.4K "Why" questions. |
| Outcome: | The proposed model explanations match learners' educational backgrounds only 50% of the time, compared to 79% for lay explanations. |
Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios. |
| Approach: | They propose a framework that internalizes domain knowledge through internal-external knowledge self-selection and selective supervised fine-tuning. |
| Outcome: | The proposed framework outperforms existing methods and matches domain-pretrained LLMs with significantly lower cost. |
Metaphor Reasoning is Meta-reasoning (2026.acl-long)
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Qianyu He, Junting Lu, Yikai Zhang, Siyu Yuan, Xiaojun Meng, Jiansheng Wei, Jiaqing Liang, Yanghua Xiao
| Challenge: | Existing work on metaphor reasoning's impact on reasoning abilities is limited. |
| Approach: | They propose a system for synthesizing metaphorical riddles that satisfy five quality dimensions: diverse, balanced, reasoning-oriented, challenging, and verifiable. |
| Outcome: | The proposed system improves reasoning abilities across six domains using only thousands of metaphorical riddles. |
GraphMind: Interactive Novelty Assessment System for Accelerating Scientific Discovery (2025.emnlp-demos)
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| Challenge: | Existing approaches to literature analysis lack transparency and information retrieval module. |
| Approach: | GraphMind is an easy-to-use interactive web tool designed to assist users in evaluating novelty of scientific papers or drafted ideas. |
| Outcome: | GraphMind enables users to capture the main structure of a scientific paper, explore related ideas through various perspectives, and assess novelty via providing verifiable contextual insights. |
Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning (2025.emnlp-main)
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| Challenge: | Existing approaches to improve data quality face limitations in static dataset curation that fail to adapt to evolving model capabilities. |
| Approach: | They propose a self-evolving framework that uses model-aware data selection and context-preserving data refinement to improve LLM performance. |
| Outcome: | The proposed framework improves the quality of seed data and boosts LLM’s performance with improving accuracy by 7.15% on average while maintaining the original dataset scale. |
Scheduled Dialog Policy Learning: An Automatic Curriculum Learning Framework for Task-oriented Dialog System (2021.findings-acl)
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| Challenge: | et al., 2013) show that dialog policy learning is an important component of the task-oriented dialogue system. |
| Approach: | They propose a framework that integrates curriculum learning and policy optimization . they propose to train dialog agents from easy dialogues to complex ones . |
| Outcome: | The proposed framework outperforms the state-of-the-art model on multi-task dialogues. |
Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models (2026.findings-acl)
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| Challenge: | a recent study shows that vision-language models have modality gaps that persist even in well-aligned models. |
| Approach: | They propose a modality-dominance score to measure and leverage modality gaps . they propose automatic interpretability metrics to evaluate these features in a scalable manner . |
| Outcome: | The proposed framework allows for training-free probing and editing methods for understanding model perception across genders and generating adversarial examples. |
PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation (2025.acl-long)
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| Challenge: | Personalized large language models (LLMs) aim to tailor outputs to user preferences . however, user data is typically sparse, making it challenging to adapt LLMs to specific user patterns. |
| Approach: | They propose a progressive learning framework that groups users based on preferences and adapts LLMs in stages. |
| Outcome: | The proposed approach outperforms SOTA models across multiple tasks. |
MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on evaluating single-round interactions, neglecting other critical aspects. |
| Approach: | They propose a benchmark to evaluate full-duplex speech language models in multi-round settings . they segment continuous full-dual dialogues into discrete turns for evaluation . |
| Outcome: | The proposed benchmark compared full-duplex speech language models with full-dual speech models . the results show that the models perform better in multi-round settings than standard models compared to benchmarks . |
Towards Imperceptible Document Manipulations against Neural Ranking Models (2023.findings-acl)
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| Challenge: | Current approaches to detect vulnerabilities in neural ranking models often introduce noticeable errors and require a well-imitated surrogate NRM to guarantee the attack effect. |
| Approach: | They propose a framework called Imperceptible DocumEnt Manipulation to produce adversarial documents that are less noticeable to both algorithms and humans. |
| Outcome: | The proposed framework outperforms strong baselines while maintaining fluency and correctness of the target documents. |
Neural Storyline Extraction Model for Storyline Generation from News Articles (N18-1)
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| Challenge: | Existing approaches to storyline generation are domain dependent and cannot deal with unseen event types. |
| Approach: | They propose a neural network-based approach to extract structured representations and evolution patterns of storylines without using annotated data. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on accuracy and efficiency on three news corpora and it is based on supervised models. |
Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding (2024.acl-long)
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| Challenge: | Generating long-term texts using artificial intelligence has always been a challenge . however, the generated novels exhibit poor logical coherence and appeal in their plots and deficiencies in character and event depiction, ultimately compromising the overall narrative quality. |
| Approach: | They propose a method for extracting excelsior and expanding from novel data to generate arbitrarily long novels using large language models. |
| Outcome: | The proposed method produces high-quality long-form novels with a high level of logical coherence and appeal despite the use of large language models. |
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models (2020.tacl-1)
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| Challenge: | Recent work shows that pre-trained language models perform poorly on challenging datasets where spurious correlations do not hold. |
| Approach: | They propose to use multi-task learning to improve generalization from minority examples . they propose to combine MTL with auxiliary tasks to improve performance . |
| Outcome: | The proposed model generalizes from minority examples without hurting in-distribution performance. |
Why and How LLMs Benefit from Knowledge Introspection in Commonsense Reasoning (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) can improve commonsense reasoning by generating intermediate knowledge, but the effectiveness of this knowledge introspection is not always guaranteed. |
| Approach: | They propose a training-free strategy that optimizes introspection via two stages: Knowledge Detection and Knowledge Regeneration. |
| Outcome: | The proposed approach mitigates the limitations of standard introspection and has consistent performance gains across all settings. |
Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning (2026.findings-acl)
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| Challenge: | Existing unified optimization strategies overlook the statistical conflict between these distinct gradient signals. |
| Approach: | They propose a framework to reduce bias-variance trade-offs in Large Language Models . they propose DYPO, which leverages intrinsic group dynamics to significantly reduce RL gradient variance . |
| Outcome: | The proposed framework outperforms traditional pipelines on reasoning benchmarks and out-of-distribution tasks. |
When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback (2025.findings-acl)
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| Challenge: | Existing static benchmarks that measure task performance often rely on a simple input-output configuration. |
| Approach: | They propose an evaluation pipeline that evaluates code models with different feedback types in an interactive setting. |
| Outcome: | The proposed evaluation pipeline compares model-user collaboration with static benchmarks by obfuscating inputs to a simulated user. |
How do Language Models Reshape Entity Alignment? A Survey of LM-Driven EA Methods: Advances, Benchmarks, and Future (2025.emnlp-main)
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| Challenge: | Entity alignment (EA) is critical for knowledge graph (KG) integration. |
| Approach: | They propose a taxonomy that categorizes methods in three stages: data preparation, feature embedding, and alignment. |
| Outcome: | The proposed taxonomy categorizes methods in three key stages: data preparation, feature embedding, and alignment. |
Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots (2020.emnlp-main)
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| Challenge: | Open-vocabulary slots degrade neural-based slot filling models because they can take on unlimited set of values and have no semantic restriction nor length limit. |
| Approach: | They propose a model-agnostic slot filling method that explicitly decouples local semantics inherent in open-vocabulary slot words from the global context. |
| Outcome: | The proposed method outperforms other models on open-vocabulary slots without deteriorating performance. |
Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack (2021.naacl-main)
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| Challenge: | Existing methods to learn representations from text often reflect social biases . previous methods rely on pre-specified direction or suffer from unstable training . |
| Approach: | They propose an adversarial disentangled debiasing model to decouple social bias attributes from intermediate representations trained on the main task. |
| Outcome: | The proposed model decouples social bias attributes from intermediate representations trained on the main task. |
Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech (2020.lrec-1)
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Adriana Guevara-Rukoz, Isin Demirsahin, Fei He, Shan-Hui Cathy Chu, Supheakmungkol Sarin, Knot Pipatsrisawat, Alexander Gutkin, Alena Butryna, Oddur Kjartansson
| Challenge: | Using crowd-sourced datasets, we build a text-to-speech voice for a new dialect in a language with existing resources. |
| Approach: | They propose a multidialectal corpus approach for building a text-to-speech voice for a new dialect in a language with existing resources using crowd-sourcing. |
| Outcome: | The proposed model outperforms baseline models in a “zero-resource” dialect scenario while holding out target dialect recordings from the training data. |
Reading Like HER: Human Reading Inspired Extractive Summarization (D19-1)
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| Challenge: | Existing methods for extracting text summarization are abstractive and extractive. |
| Approach: | They propose a novel approach for extractive summarization by simulating two stages . they adopt a convolutional neural network to encode gist of paragraphs for rough reading . |
| Outcome: | The proposed method significantly outperforms the state-of-the-art extractive methods on CNN and DailyMail datasets. |
Weak Reward Model Transforms Generative Models into Robust Causal Event Extraction Systems (2024.emnlp-main)
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| Challenge: | Existing evaluation metrics that reflect the performance of causal event extraction tasks are poorly reflecting the inherent ambiguity of cause and effect boundaries. |
| Approach: | They propose to use a weak-to-strong supervision method to train an evaluation model while still achieving high performance in training an RL model. |
| Outcome: | The proposed method achieves high agreement with human-annotated data while still achieving high performance in training an RL model. |
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2025.acl-long)
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Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, Ben Kao, Guohao Li, Junxian He, Yu Qiao, Zhiyong Wu
| Challenge: | Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability. |
| Approach: | They propose a GUI data synthesis pipeline that reverse engineers GUI trajectory construction process by executing pre-defined tasks. |
| Outcome: | The proposed GUI data synthesis pipeline overcomes the bottlenecks of previous methods that rely on pre-defined tasks and limited data diversity. |
Scattered Hypothesis Generation for Open-Ended Event Forecasting (2026.findings-acl)
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| Challenge: | Existing methods for event forecasting focus on the most probable outcomes, neglecting the intrinsic uncertainty of real-world events. |
| Approach: | They propose a reinforcement learning framework that optimizes inclusiveness and diversity of the hypothesis by integrating validity-gated score into the overall objective. |
| Outcome: | The proposed framework outperforms baselines on two real-world benchmark datasets. |
Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Models (2025.findings-acl)
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| Challenge: | In real-world scenarios, user instructions often contain soft constraints, which are semantically related and cannot be rule-based verified, posing challenges for large language models. |
| Approach: | They propose a pipeline to construct datasets with high-quality outputs for instructions containing soft constraints automatically and use Direct Preference Optimization (DPO) as the training method. |
| Outcome: | The proposed model improves the LLMs' soft constraint following ability by using direct preference optimization (DPO) and constraint quantity. |
Self-Steering Optimization: Autonomous Preference Optimization for Large Language Models (2025.findings-acl)
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Hao Xiang, Bowen Yu, Hongyu Lin, Keming Lu, Yaojie Lu, Xianpei Han, Ben He, Le Sun, Jingren Zhou, Junyang Lin
| Challenge: | Prior research focused on developing data generation methods, while insufficient attention has been paid to quality control mechanisms and often produces inaccurate and unhelpful data. |
| Approach: | They propose an algorithm that automatically generates high-quality preference data, eliminating manual annotation requirements. |
| Outcome: | The proposed algorithm outperforms baselines in human preference alignment and reward optimization. |
Machine Translation With Weakly Paired Documents (D19-1)
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| Challenge: | Recent studies explore the possibility of unsupervised machine translation with monolingual data only. |
| Approach: | They propose a method to mine bilingual sentences from weakly paired documents . they use word distribution-level alignments to constrain word distributions of two weakly-paired documents. |
| Outcome: | The proposed method outperforms previous results on six translation tasks using weakly paired bilingual documents and a large number of bilingual sentences. |
Batch IS NOT Heavy: Learning Word Representations From All Samples (P18-1)
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| Challenge: | Stochastic Gradient Descent with negative sampling is the most prevalent approach to learn word representations. |
| Approach: | They propose a method that uses batch gradient learning to generate word representations from all training samples. |
| Outcome: | The proposed method outperforms sampling-based methods on several benchmark tasks. |
Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation (2022.findings-emnlp)
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| Challenge: | Existing QA frameworks that use event-centric reasoning are lacking. |
| Approach: | They propose a novel QA model with contrastive learning and invertible event transformation . they use an invertable transformation matrix to project event vectors into a common event embedding space . |
| Outcome: | The proposed model achieves 8.4% gain in token-level F1 score and 3.0% gain in Exact Match score on the ESTER dataset. |
Crafting Adversarial Examples for Neural Machine Translation (2021.acl-long)
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| Challenge: | Effective adversary generation for neural machine translation is crucial for robust systems. |
| Approach: | They propose to leverage round-trip translation technique to build valid metrics for evaluating NMT adversarial attacks. |
| Outcome: | The proposed method could break the state-of-art NMT models with small perturbations. |
TempTabQA: Temporal Question Answering for Semi-Structured Tables (2023.emnlp-main)
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| Challenge: | Semi-structured data often include temporal information about entities, either implicitly or explicitly. |
| Approach: | They present a dataset that includes 11,454 question-answer pairs from Wikipedia Infobox tables spanning more than 90 distinct domains. |
| Outcome: | The proposed dataset can be used as a benchmark to improve models for temporal reasoning on semi-structured tables. |
Community-Cross-Instruct: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities (2024.emnlp-main)
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| Challenge: | Social scientists use surveys to learn opinions and beliefs of populations, but these methods are slow, costly, and prone to biases. |
| Approach: | They propose a framework for aligning large language models to online communities by finetuning instruction-output pairs by an advanced LLM to elicit their beliefs. |
| Outcome: | The proposed framework enables cost-effective and automated surveying of diverse online communities. |
Continual Generalized Intent Discovery: Marching Towards Dynamic and Open-world Intent Recognition (2023.findings-emnlp)
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| Challenge: | Currently, the generalized intent classification system only considers one stage of OOD learning and requires all IND data for joint training. |
| Approach: | They propose a task that detects OOD intents from dynamic OOD data streams . they propose CGID method that bootstraps new intent discovery through class prototypes . |
| Outcome: | The proposed task can detect out-of-domain (OOD) queries and extend them to the in-domain classifier . it can safely and efficiently detect out of-domain queries and avoid wrong operations . |
ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation (2025.coling-main)
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| Challenge: | Empathy improves human-machine dialogue systems by enhancing the user's experience. |
| Approach: | They propose a framework that leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder. |
| Outcome: | Empirical results show that the framework outperforms baseline models and offers a robust solution for empathetic dialogue generation. |
Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection (2024.naacl-long)
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| Challenge: | Semi-supervised dialogue summarization (SSDS) leverages model-generated summaries to reduce reliance on human-labeled data. |
| Approach: | They propose a scoring approach that encapsulates three primary dimensions of summarization model quality. |
| Outcome: | The proposed method reduces reliance on human-labeled data and improves the performance of summarization models. |
Instruct-of-Reflection: Enhancing Large Language Models Iterative Reflection Capabilities via Dynamic-Meta Instruction (2025.naacl-long)
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| Challenge: | Existing approaches involve models iterating and improving their previous responses based on internal reflection ability or external feedback. |
| Approach: | They propose a reflection framework that leverages meta-thoughts and self-consistency to enhance the iterative reflection capability of Large LanguageModels. |
| Outcome: | The proposed framework achieves an average improvement of 10.1% over established baselines in mathematical and commonsense reasoning tasks, highlighting its efficacy and applicability. |
AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation (2026.findings-acl)
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Wentao Shi, Yu Wang, Yuyang Zhao, Yuxin Chen, Fuli Feng, Xueyuan Hao, Xi Su, Qi GU, Hui Su, Xunliang Cai, Xiangnan He
| Challenge: | Existing approaches to verify agent behaviors in complex environments rely on rule-based verifiers or LLM-as-a-Judge models. |
| Approach: | They propose a benchmark to evaluate Agent-as-a-Judge across three domains . the benchmark covers search, data systems, and graphical user interfaces - with 155 tasks and 516 trajectories . |
| Outcome: | The proposed benchmark outperforms existing benchmarks in search, data systems, and GUI domains while revealing open challenges in agent-based verification. |
UnitedQA: A Hybrid Approach for Open Domain Question Answering (2021.acl-long)
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| Challenge: | Recent work on open-domain question answering focuses on either extractive or generative readers exclusively. |
| Approach: | They propose a hybrid approach to extractive and generative readers that leverages both models. |
| Outcome: | The proposed approach outperforms state-of-the-art models on NaturalQuestions and TriviaQA respectively. |
Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations (2024.acl-long)
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| Challenge: | Currently, many benchmarks evaluate the commonsense reasoning of large language models (LLMs), but most are English-based, limiting non-English evaluations. |
| Approach: | They propose to use Chinese commonsense reasoning to evaluate LLMs' commonsensing ability. |
| Outcome: | The proposed benchmark covers both globally known and Chinese-specific commonsense reasoning abilities and can be used as a reference for future research. |
LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation (2025.findings-acl)
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| Challenge: | Existing frameworks for missing data imputation are lacking in a finetuning-free process and mitigating biases and uncertainty in LLM outputs. |
| Approach: | They propose a framework for imputation of large language models with a forest of few-shot learning LLM "trees" they use bipartite information graphs to identify relevant neighboring entries with feature and value granularity. |
| Outcome: | The proposed framework is based on a concept of bipartite information graphs to identify high-quality relevant neighboring entries with both feature and value granularity. |
A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output (2022.lrec-1)
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Vivien Macketanz, Eleftherios Avramidis, Aljoscha Burchardt, He Wang, Renlong Ai, Shushen Manakhimova, Ursula Strohriegel, Sebastian Möller, Hans Uszkoreit
| Challenge: | Using fine-grained evaluation techniques, translation outputs have become better and more fluent. |
| Approach: | They propose a fine-grained test suite for the language pair German–English . they describe the creation and implementation of the test suite in detail . |
| Outcome: | The proposed test suite is based on linguistically motivated categories and phenomena and semi-automatic evaluation is carried out with regular expressions. |
Multi-Modality Expansion and Retention for LLMs through Parameter Merging and Decoupling (2025.acl-long)
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Junlin Li, Guodong Du, Jing Li, Sim Kuan Goh, Wenya Wang, Yequan Wang, Fangming Liu, Ho-Kin Tang, Saleh Alharbi, Daojing He, Min Zhang
| Challenge: | Large Language Models (LLMs) are a cornerstone in artificial intelligence due to their exceptional performance. |
| Approach: | They propose a training-free approach that integrates existing MLLMs for effective multimodal expansion while retaining their original performance. |
| Outcome: | The proposed approach can expand LLMs' multimodal capabilities while retaining original performance. |
CodeRise: Bootstrapping LLMs for Ultra Low-Resource Programming Languages via Progressive Self-Refinement Curriculum (2026.findings-acl)
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| Challenge: | Existing methods for training data generation for low-resource languages suffer from a cold-start problem and lack diversity. |
| Approach: | They propose a two-stage framework that generates a high-quality, diverse, and progressively complex curriculum for Ultra Low-Resource Programming Languages (ULRPLs) they leverage the full formal syntax of the target language as structural guidance and apply a biased sampling strategy over library modules. |
| Outcome: | The proposed framework outperforms training-free and training-based baselines on two ULRPLs, Tengo and Janet. |
Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark (2026.acl-long)
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Kai Zou, Ziqi Huang, Yuhao Dong, Shulin Tian, Dian Zheng, Hongbo Liu, Jingwen He, Bin Liu, Yu Qiao, Ziwei Liu
| Challenge: | Existing evaluations treat visual understanding and generation in isolation or overlook tasks that inherently couple them. |
| Approach: | They propose a benchmark that examines the bidirectional synergy between generation and understanding across eight reasoning-centric domains. |
| Outcome: | The proposed model systematically unfolds the bidirectional synergy between generation and understanding across eight reasoning-centric domains. |
Class Lifelong Learning for Intent Detection via Structure Consolidation Networks (2023.findings-acl)
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Qingbin Liu, Yanchao Hao, Xiaolong Liu, Bo Li, Dianbo Sui, Shizhu He, Kang Liu, Jun Zhao, Xi Chen, Ningyu Zhang, Jiaoyan Chen
| Challenge: | Existing intent detection models can only handle predefined intent classes in the offline environment. |
| Approach: | They propose a method that continually learns new intent classes from new data . structure-based retrospection and contrastive knowledge distillation are used to solve these problems . |
| Outcome: | The proposed method outperforms existing models on three benchmarks. |
InternalInspector I2: Robust Confidence Estimation in LLMs through Internal States (2024.findings-emnlp)
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Mohammad Beigi, Ying Shen, Runing Yang, Zihao Lin, Qifan Wang, Ankith Mohan, Jianfeng He, Ming Jin, Chang-Tien Lu, Lifu Huang
| Challenge: | Large Language Models (LLMs) often struggle with generating reliable outputs, often producing high-confidence inaccuracies known as hallucinations. |
| Approach: | They propose a framework that leverages contrastive learning on internal states including attention states, feed-forward states, and activation states of all layers to enhance confidence estimation in LLMs. |
| Outcome: | The framework outperforms existing methods in the hallucination detection benchmark HaluEval and the previous methods at the same time. |
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation (2021.findings-emnlp)
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| Challenge: | Radiology report generation aims at generating descriptive text from radiology images automatically. |
| Approach: | They propose a weakly supervised contrastive loss method that generates descriptive text from radiology images automatically. |
| Outcome: | The proposed method outperforms previous work on correctness and text generation metrics for two public benchmarks. |
Interpretable Relevant Emotion Ranking with Event-Driven Attention (D19-1)
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| Challenge: | Existing studies ignore the latent event information in documents . Existing methods for detecting emotions are limited to a few words . |
| Approach: | They propose to integrate event information into a deep learning architecture to extract relevant emotion ranking models using corpus-level event embeddings and document-level events. |
| Outcome: | The proposed model performs better than state-of-the-art emotion detection and multi-label approaches on three real-world corpora and interpretable results shed light on the events which trigger certain emotions. |
ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain (2021.eacl-main)
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| Challenge: | Using a novel annotation scheme, we identify anaphoric references in chemical patents and determine the chemical relation between linked entities. |
| Approach: | They propose a neural approach to anaphora resolution based on coreference and bridging links in chemical patents. |
| Outcome: | The proposed framework can be used to identify anaphoric references in chemical patents and determine the chemical relation between linked entities. |
Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection (2024.lrec-main)
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Pei Wang, Keqing He, Yejie Wang, Xiaoshuai Song, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
| Challenge: | Out-of-domain (OOD) intent detection is crucial for task-oriented dialogue systems. |
| Approach: | They conduct a comprehensive evaluation of large language models (LLMs) under various experimental settings and outline their strengths and weaknesses. |
| Outcome: | The proposed models exhibit strong zero-shot and few-shot capabilities, but is still at a disadvantage compared to models fine-tuned with full resource. |
CoCoST: Automatic Complex Code Generation with Online Searching and Correctness Testing (2024.emnlp-main)
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| Challenge: | Existing methods to improve code generation from natural language descriptions are difficult due to complex structure, subtle bugs, and lack of supplementary contents. |
| Approach: | They propose a framework that enhances complex code generation by online searching for more information with planned queries and correctness testing for code refinement. |
| Outcome: | The proposed framework improves the quality of complex code generation on the DS-1000 and ClassEval datasets. |
Machine-in-the-Loop Rewriting for Creative Image Captioning (2022.naacl-main)
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| Challenge: | Prior work has shown that providing users with a machine-written draft or sentence-level continuations has limited success since the generated text tends to deviate from users’ intention. |
| Approach: | They propose to train a rewriting model that modifies specified spans of text within the user’s original draft to introduce descriptive and figurative elements in the text. |
| Outcome: | The proposed model is rated more helpful by users than a baseline infilling language model on a user study through Amazon Mechanical Turk. |
STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing models focus on predictive accuracy over reasoning, a gap exists . time series data are ubiquitous in real-world systems and exhibit complex spatio-temporal structures. |
| Approach: | They propose a time series reasoning model that integrates time series, graph structure, and text for explicit reasoning. |
| Outcome: | The proposed model achieves average accuracy gains between 17% and 135% at 0.004x the cost of proprietary models and generalizes robustly to real-world data. |
Scaling Laws for Multilingual Language Models (2025.findings-acl)
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Yifei He, Alon Benhaim, Barun Patra, Praneetha Vaddamanu, Sanchit Ahuja, Parul Chopra, Vishrav Chaudhary, Han Zhao, Xia Song
| Challenge: | Existing scaling laws for language models are limited to a limited number of languages, but they can be applied to arbitrary number of different languages. |
| Approach: | They propose a scaling law for general-purpose decoder-only language models trained on multilingual data that shifts focus from individual languages to language families. |
| Outcome: | The proposed scaling law can be applied to models trained on multilingual data . it can be used to predict performance across multiple languages and models . |
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database (2024.lrec-main)
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| Challenge: | Existing benchmarks for textual question answering only focus on single-chain or single-hop retrieval . Existing approaches to answer complex questions have limitations . |
| Approach: | They propose to conduct Graph-Hop, a novel multi-chains and multi-hops retrieval paradigm in complex question answering. |
| Outcome: | The proposed model provides explicit and fine-grained evidence graphs for complex question to support comprehensive and detailed reasoning. |
Branch-and-Browse: Efficient and Controllable Web Exploration with Tree-Structured Reasoning and Action Memory (2026.acl-long)
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| Challenge: | Existing methods for embodied reasoning are coarse-grained and expensive . branch-and-browse framework enables fine-grounded, memory-guided, and efficient multi-branch reasoning. |
| Approach: | They propose a framework that unifies structured reasoning-acting, contextual memory, and efficient execution. |
| Outcome: | The proposed framework achieves task success rate of 35.8% and reduces execution time by up to 40.4% relative to state-of-the-art methods. |
Towards Understanding Neural Machine Translation with Word Importance (D19-1)
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| Challenge: | Neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, but the interpretability of NMT remains unsatisfactory. |
| Approach: | They propose to attribute NMT output to every input word using a gradient-based method to measure word importance. |
| Outcome: | The proposed method is superior on identifying input words with higher influence on translation performance. |
QuAC: Question Answering in Context (D18-1)
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Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer
| Challenge: | a dataset for Question Answering in Context contains 14K information-seeking QA dialogs . questions are often more open-ended, unanswerable, or only meaningful within the dialog context . |
| Approach: | They propose a dataset for Question Answering in Context that contains 14K dialogs . they use a student to ask questions about a Wikipedia section and a teacher to answer them . |
| Outcome: | The proposed dataset underperforms humans in a number of reference models . the dataset contains 14K information-seeking dialogs over sections from Wikipedia . |
An Unsupervised Sentence Embedding Method by Mutual Information Maximization (2020.emnlp-main)
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| Challenge: | Sentence BERT is inefficient for sentence-pair tasks as it needs to evaluate combinatorially many sentence pairs which is very time-consuming. |
| Approach: | They propose a lightweight extension on top of BERT and a self-supervised learning objective to derive meaningful sentence embeddings in an unsupervised manner. |
| Outcome: | The proposed method outperforms baselines on common semantic textual similarity tasks and downstream supervised tasks and achieves performance competitive with supervised methods on various tasks. |
InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026.acl-long)
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| Challenge: | Existing data selection methods suffer from severe domain specificity . existing methods for general instruction-following fail on reasoning tasks . |
| Approach: | They propose a framework that operationalizes contrastive entropy as a domain-adaptive selection criterion through warmup calibration, bi-directional NLL filtering, and entropic-based ranking. |
| Outcome: | Experiments show that InstructDiff outperforms baseline training on reasoning tasks while using only 10% of the data. |
Task-aware Contrastive Mixture of Experts for Quadruple Extraction in Conversations with Code-like Replies and Non-opinion Detection (2025.emnlp-main)
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| Challenge: | Applying Large Language Models (LLMs) for this specific task presents two primary challenges: the accurate extraction of multiple elements and the understanding of complex dialogue reply structure. |
| Approach: | They propose a novel LLM-based multi-task approach to extract sentiment quadruples from conversations by integrating expert-level contrastive loss within task-oriented mixture of experts layer. |
| Outcome: | The proposed method outperforms existing fine-tuning techniques in terms of accuracy and computational efficiency. |
Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections (P19-1)
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| Challenge: | Current systems for syntactic analysis tasks rely heavily on large scale annotated data. |
| Approach: | They propose to learn a generative model with a structured prior that uses labeled source and unlabeled target data jointly. |
| Outcome: | The proposed model improves on part-of-speech tagging and dependency parsing tasks on English as the only source corpus and on a wide range of target languages. |
Hypernetwork-Assisted Parameter-Efficient Fine-Tuning with Meta-Knowledge Distillation for Domain Knowledge Disentanglement (2024.findings-naacl)
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| Challenge: | Recent work on domain adaptation for text summarization fails to account for the huge gap between dialogue and general articles. |
| Approach: | They propose a hypernetwork-assisted encoder-decoder architecture with parameter-efficient fine-tuning to disentangle domain-invariant knowledge from source domains while learning specific knowledge of the target domain. |
| Outcome: | The proposed model can disentangle domain-invariant knowledge from source domains while learning specific knowledge of the target domain. |
RoleBreak: Character Hallucination as a Jailbreak Attack in Role-Playing Systems (2025.coling-main)
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| Challenge: | Existing approaches to combat character hallucination are vulnerable to attack . large language models (LLMs) are capable of generating responses inconsistent with intended personas . |
| Approach: | They propose a novel defence strategy that generates supplemental context through narration to mitigate role-query conflicts and improve query generalization. |
| Outcome: | The proposed defence strategy outperforms refusal-based strategies in character hallucinations and query generalization. |
Cross-lingual Aspect-based Sentiment Analysis with Aspect Term Code-Switching (2021.emnlp-main)
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| Challenge: | Existing studies on Aspect-based sentiment analysis (ABSA) focus on English texts, but handling it in resource-poor languages remains a challenge. |
| Approach: | They propose an unsupervised cross-lingual transfer method for the Aspect-based sentiment analysis task . they propose an aspect code-switching mechanism to augment training data with code-linked bilingual sentences . |
| Outcome: | The proposed method preserves task-specific knowledge in the target language. |
DSPM-NLG: A Dual Supervised Pre-trained Model for Few-shot Natural Language Generation in Task-oriented Dialogue System (2023.findings-acl)
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| Challenge: | Existing models for few-shot natural language generation are based on a dual correlation between NLG and SLU from the perspective of probability. |
| Approach: | They propose a dual supervised pre-trained model to regularize the pre-training process . they use a probabilistic approach to learn the dual correlation between NLG and SLU . |
| Outcome: | The proposed model outperforms the previous state-of-the-art models on a few-shot dataset. |
Pre-trained Language Model Based Active Learning for Sentence Matching (2020.coling-main)
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| Challenge: | Existing active learning approaches for natural language processing ignore the characteristics of natural language. |
| Approach: | They propose a pre-trained language model based active learning approach for sentence matching that provides linguistic criteria to measure instances and help select more effective instances for annotation. |
| Outcome: | The proposed approach can achieve greater accuracy with fewer labeled training instances. |
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection (2021.emnlp-main)
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| Challenge: | Existing work on unsupervised domain adaptation of neural machine translation assumes access to monolingual text in either the source or target language in the new domain. |
| Approach: | They propose a method to extract in-domain sentences from a large generic monolingual corpus from 'missing' text. |
| Outcome: | The proposed method outperforms baselines up to +1.5 BLEU score on five diverse domains in three language pairs and a real-world translation scenario. |
Neural Unsupervised Reconstruction of Protolanguage Word Forms (2023.acl-long)
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| Challenge: | Existing methods for reconstructing ancient word forms use expectation-maximization . past work has used this method to predict simple phonological changes . |
| Approach: | They extend expectation-maximization to predict phonological changes between ancient word forms and their cognates in modern languages. |
| Outcome: | The proposed model reduces edit distance from the target word forms compared to previous methods. |
HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization (2021.naacl-main)
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| Challenge: | Existing models for hierarchical text classification do not consider statistical constraint on label representations learned by structure encoder. |
| Approach: | They propose a new hierarchical text classification model called HTCInfoMax which incorporates two modules to improve the model's representations. |
| Outcome: | The proposed model can model the interaction between each text sample and its ground truth labels explicitly which filters out irrelevant information. |
Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter (D18-1)
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| Challenge: | Neural machine translation suffers from exposure bias and error propagation problem. |
| Approach: | They conduct a series of analyses to deeply understand the accuracy drop problem . they find that the left part of the translated sentence is often better than its right part . |
| Outcome: | The results show that the left part of the translated sentence is often better than its right part in left-to-right decoding models. |
A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System (2023.eacl-main)
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| Challenge: | Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes and comparing different models. |
| Approach: | They propose a human-in-the-loop topic modeling system that integrates users’ knowledge into the modelling process, enabling them to refine the model iteratively. |
| Outcome: | The proposed system is based on a series of user studies to assess its performance in progressively more realistic applications. |
Martingale Foresight Sampling: A Principled Approach to Inference-Time LLM Decoding (2026.eacl-long)
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| Challenge: | Standard autoregressive decoding in large language models is short-sighted, often failing to find globally optimal reasoning paths due to token-by-token generation process. |
| Approach: | They propose a principled framework that reformulates LLM decoding as a problem of identifying an optimal stochastic process. |
| Outcome: | The proposed framework surpasses state-of-the-art methods in accuracy while significantly improving computational efficiency. |
Position Engineering: Boosting Large Language Models through Positional Information Manipulation (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated significant strides towards achieving artificial general intelligence. |
| Approach: | They propose a technique termed position engineering which alters the positional information in the prompt without modifying the text itself. |
| Outcome: | The proposed technique significantly improves on the baseline in retrieval-augmented generation and in-context learning scenarios. |
MONTROSE: LLM-driven Monte Carlo Tree Search Self-Refinement for Cross-Domain Rumor Detection (2025.findings-acl)
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| Challenge: | Existing feature alignment methods are susceptible to task interference during training. |
| Approach: | MONTROSE is a cross-domain rumor detection method that generates high-quality synthetic data for the target domain and a domain-sharpness-aware approach to train models with these synthetic data. |
| Outcome: | Experiments show that MONTROSE improves in cross-domain rumor detection. |
Graph-Based Decoding for Task Oriented Semantic Parsing (2021.findings-emnlp)
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| Challenge: | Existing paradigms for semantic parsing are sequence-to-sequence and AMR parsers. |
| Approach: | They propose to formulate parsing as a sequence-to-sequence task using graph-based decoding techniques developed for syntactic parsers. |
| Outcome: | The proposed approach is competitive with sequence decoders on the standard setting and offers significant improvements in data efficiency and data availability. |
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences (2024.acl-long)
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Xiyao Wang, Yuhang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon, Taixi Lu, Fuxiao Liu, Gedas Bertasius, Mohit Bansal, Huaxiu Yao, Furong Huang
| Challenge: | Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks, but their ability to extrapolate from image sequences has been less investigated. |
| Approach: | They propose a new benchmark to assess MLLMs’ sequential image reasoning abilities. |
| Outcome: | The proposed benchmark features 4,761 diverse image sequences with varying lengths. |
CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation (2022.emnlp-main)
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| Challenge: | a new benchmark for goal-oriented dialog evaluation is needed to address the problem of knowledge sources, noisy user expressions, and the shortage of annotated data. |
| Approach: | They propose a Chinese benchmark for goal-oriented dialog evaluation that uses dialog sessions and 574,949 dialog turns to bridge the gap between academic benchmarks and spoken dialog scenarios. |
| Outcome: | The proposed benchmark contains 96,763 dialog sessions and 574,949 dialog turns totally. |
SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis (2020.coling-main)
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| Challenge: | Pre-trained language models have been widely applied to cross-domain NLP tasks like sentiment analysis, but fine-tuning them on the source domain tends to overfit, leading to inferior results on the target domain. |
| Approach: | They propose to pre-train a sentiment-aware language model (SentiX) via domain-invariant sentiment knowledge from large-scale review datasets and utilize it for cross-domain sentiment analysis tasks without fine-tuning. |
| Outcome: | The proposed model achieves state-of-the-art in all the cross-domain sentiment analysis tasks and can be trained with only 1% samples and better than BERT with 90% samples. |
Federated Data-Efficient Instruction Tuning for Large Language Models (2025.findings-acl)
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| Challenge: | Existing federated learning (FL) uses all local data, causing excessive computational overhead and overfitting to local data. |
| Approach: | They propose a federated data-efficient instruction tuning approach which utilizes a representative subset of edge-side data to tune LLMs. |
| Outcome: | The proposed method improves Rouge-L on unseen tasks by 10.72% over the SOTA full-data instruction tuning methods while using less than 1.5% of the data samples. |
PlanGPT-VL: Enhancing Urban Planning with Domain-Specific Vision-Language Models (2025.emnlp-industry)
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| Challenge: | Existing Vision-Language Models (VLMs) fail to analyze planning maps . specialized visual representations of land use zones, transportation networks, and development policies are needed to interpret complex planning maps. |
| Approach: | They propose a domain-specific VLM tailored for urban planning maps that employs three innovations: PlanAnno-V framework for high-quality VQA data synthesis, Critical Point Thinking (CPT) and PlanBench-V benchmark for systematic evaluation. |
| Outcome: | The new model outperforms general-purpose VLMs on planning map interpretation tasks. |
Curriculum Prompt Learning with Self-Training for Abstractive Dialogue Summarization (2022.emnlp-main)
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| Challenge: | Existing methods to summarize dialogues are difficult due to insufficient training data and low information density. |
| Approach: | They propose a curriculum-based prompt learning method with self-training that gradually increases the degree of prompt perturbation, improving dialogue understanding and modeling capabilities. |
| Outcome: | The proposed model outperforms baseline models on the AMI and ICSI datasets and human evaluations show it is superior in the quality of the summary generation. |
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning (2024.acl-long)
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| Challenge: | Earlier studies of instruction tuning on Large Language Models focus on creating large, varied, and high-quality datasets with responses curated by human experts. |
| Approach: | They propose to use a smaller and weaker model to fine tune a larger and stronger model . they find it can largely speed up the data filtering and improve performance . |
| Outcome: | The proposed model can filter instruction data faster and better on benchmarks. |
Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models? (2024.acl-long)
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| Challenge: | Experimental results show that stories outperform rules as the expression for retrieving commonsense from LLMs, exhibiting higher generation confidence and commonsensense accuracy. |
| Approach: | They investigate the commonsense ability of large language models expressed through stories and rules to retrieve commonsensing knowledge from LLMs. |
| Outcome: | The stories outperform rules as commonsense expressions on 28 commonsensense QA datasets, exhibiting higher generation confidence and commonsence accuracy. |
MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking (2023.findings-acl)
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| Challenge: | Experimental results show that MoNET outperforms previous DST methods in alleviating state momentum issues and improving the anti-noise ability. |
| Approach: | They propose to use previous state of each turn in training data as input to learn to predict current state. |
| Outcome: | The proposed model outperforms existing methods on multiWOZ datasets and shows that it can update and correct slot values and improve anti-noise ability. |
ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation (2025.emnlp-main)
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Minghua He, Yue Chen, Fangkai Yang, Pu Zhao, Wenjie Yin, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing code translation models only learn the contextual semantics of code during pre-training, neglecting executability information closely related to the execution state of the code. |
| Approach: | They propose an LLM specifically designed for code translation called ExeCoder . it uses executability representations such as functional semantics and syntax structures to enhance LLMs' capabilities. |
| Outcome: | The proposed model outperforms existing open-source code translation models on two metrics. |
EVGeoQA: Benchmarking LLMs on Dynamic, Multi-Objective Geo-Spatial Exploration (2026.findings-acl)
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| Challenge: | Existing geo-spatial question answering benchmarks focus on static retrieval, failing to capture the complexity of real-world planning that involves dynamic user locations and compound constraints. |
| Approach: | They propose a new benchmark for Large Language Models that integrates location-anchored and dual-objective queries with a user's real-time coordinates. |
| Outcome: | The proposed model can summarize historical exploration trajectories to enhance exploration efficiency. |
HqeKV: Towards Hybrid Quantization and Eviction for KV Cache in Long-Context LLM Inference (2026.findings-acl)
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| Challenge: | autoregressive inference requires repeated computation across transformer layers. |
| Approach: | They propose a hybrid compression framework built on both quantization and eviction . they propose varying importance metric and flexible conversion policies to reduce memory overhead . |
| Outcome: | The proposed framework outperforms state-of-the-art methods under memory constraints. |
On the Generalization of Training-based ChatGPT Detection Methods (2024.findings-emnlp)
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| Challenge: | Existing studies show that training-based methods are ineffective to detect LLM generated texts from unseen tasks or topics which are not collected during training. |
| Approach: | They propose to train classification models to distinguish LLMs from human texts by a distribution shift caused by prompts, text lengths, topics, and language tasks. |
| Outcome: | The proposed methods can detect LLMs from black-box models, but they suffer from distribution shifts due to a wide range of factors, including prompts, text lengths, topics, and language tasks. |
Belief Revision: The Adaptability of Large Language Models Reasoning (2024.emnlp-main)
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| Challenge: | Existing evaluations assume language models operate with consistent information. |
| Approach: | They propose a dataset to test LMs' belief revision ability when presented with new evidence. |
| Outcome: | The proposed framework improves language models’ adaptiveness to changing information, highlighting a critical trade-off. |
SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters (2022.findings-emnlp)
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| Challenge: | Pretrain-finetuned models are increasingly complex and require more parameters to match the performance of full fine-tuning. |
| Approach: | They propose an efficient Adapter Tuning technique that freezes pretrained language models and fine-tunes a few extra modules. |
| Outcome: | The proposed setting outperforms the standard Adapter Tuning by 80% . the proposed setting is easy to use and has a high sparse ratio . |
QuALITY: Question Answering with Long Input Texts, Yes! (2022.naacl-main)
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Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, Samuel Bowman
| Challenge: | Existing models for natural language understanding are limited to processing only a few hundred words at a time. |
| Approach: | They propose a dataset with context passages in English that have an average length of 5,000 tokens. |
| Outcome: | a new dataset with long-text comprehension questions is used to test models on long-document comprehension . the questions are validated by contributors who have read the entire passage, not just excerpts . only half of the questions can be answered by annotators working under tight time constraints . |
MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents (2026.acl-long)
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Pengxiang Zhao, Guangyi Liu, Yaozhen Liang, Weiqing He, Zhengxi Lu, WenHao Wang, Yuehao Huang, Yuxiang Chai, Zhaolu Kang, Yaxuan Guo, Hao Wang, Kexin Zhang, Liang Liu, Yong Liu
| Challenge: | Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, but systematic evaluation of GUI–shortcut hybrid agents remains underexplored. |
| Approach: | They propose a benchmark that evaluates GUI-shortcut hybrid agents with a specific focus on the mobile domain. |
| Outcome: | MAS-Bench evaluates agent's ability to generate shortcuts by discovering and creating reusable, low-cost workflows. |
RD-MCSA: A Multi-Class Sentiment Analysis Approach Integrating In-Context Classification Rationales and Demonstrations (2025.emnlp-main)
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| Challenge: | Existing methods for multi-class sentiment analysis (MCSA) are difficult due to subtle semantic differences between adjacent sentiment levels and the scarcity of high-quality annotated data. |
| Approach: | They propose a framework to integrate classification rationales with adaptively selected demonstrations to enhance MCSA performance under limited supervision. |
| Outcome: | The proposed framework outperforms baseline and standard ICL methods on five benchmark datasets. |
UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models (2025.findings-naacl)
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Yuzhe Yang, Yifei Zhang, Yan Hu, Yilin Guo, Ruoli Gan, Yueru He, Mingcong Lei, Xiao Zhang, Haining Wang, Qianqian Xie, Jimin Huang, Honghai Yu, Benyou Wang
| Challenge: | Recent advances in large language models (LLMs) have expanded their potential applications in finance. |
| Approach: | They propose a framework to evaluate the ability of large language models to handle financial tasks using human expert evaluations and task-specific interactions. |
| Outcome: | The proposed framework evaluates the ability of large language models to handle complex financial tasks and combines human expert evaluations with dynamic, task-specific interactions to simulate the complexities of evolving financial scenarios. |
Incremental Intent Detection for Medical Domain with Contrast Replay Networks (2022.findings-acl)
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| Challenge: | Existing methods to detect medical intents require fixed pre-defined intent categories . however, novel medical intent categories incessantly emerge with new data and intents in the real world . |
| Approach: | They propose to incrementally learn emerged medical intents from continually arriving data of new intents while avoiding catastrophically forgetting old ones. |
| Outcome: | The proposed method outperforms the state-of-the-art model on two benchmarks by 5.7% and 9.1% accuracy. |
Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning (2022.acl-long)
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| Challenge: | Existing NDR models suffer from large performance drop on hypothetical questions, e.g., “what the annualized rate of return would be if the revenue in 2020 was doubled”. |
| Approach: | They propose a learning to imagine module which can be seamlessly incorporated into NDR models to perform the imagination of unseen counterfactual. |
| Outcome: | The proposed model can perform the imagination of unseen counterfactuals on hypothetical questions. |
LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval (2026.acl-long)
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He Cheng, Yifu Wu, Saksham Khatwani, Maya Kruse, Dmitriy Dligach, Timothy A. Miller, Majid Afshar, Yanjun Gao
| Challenge: | Existing systems struggle to balance efficiency, scalability, and interpretability. |
| Approach: | They propose a hardware-aligned framework that enables scalable and interpretable k-hop retrieval on large KGs. |
| Outcome: | The proposed framework scales to billion-edge graphs without loss of retrieval fidelity. |
TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models (2025.acl-long)
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| Challenge: | Tabular data are crucial in many fields and their understanding by large language models (LLMs) under high parameter efficiency paradigm is important. |
| Approach: | They propose a module that uses 2D LoRA to encode low-rank information on cell positions to improve table serialization and representation of two-dimensional structured information within a one-dimensional sequence. |
| Outcome: | Experiments on four tabular-related datasets show that TableLoRA outperforms vanilla LoRA and surpasses table encoding methods tested in control. |
Integrating Transformer and Paraphrase Rules for Sentence Simplification (D18-1)
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| Challenge: | Current models for sentence simplification adopted ideas from machine translation studies and implicitly learned simplification mapping rules from normal-simple sentence pairs. |
| Approach: | They propose a novel model based on a multi-layer and multi-head attention architecture and two innovative approaches to integrate a paraphrase knowledge base for simplification. |
| Outcome: | The proposed model outperforms state-of-the-art models for sentence simplification . it seeks to select more accurate simplification rules, the authors show . |
Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models (2021.eacl-main)
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| Challenge: | a large-scale unsupervised pretraining has been shown to greatly boost the performance of natural language processing models. |
| Approach: | They propose an intuitive finetuning strategy to regularize the finetune process . they propose a mix-review strategy to alleviate the forgetting problem . |
| Outcome: | The proposed strategy regularizes the finetuning process, and the forgetting problem is alleviated . the proposed strategy also improves the performance of the resulting model . |
Seq2seq Dependency Parsing (C18-1)
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| Challenge: | Recent trend for dependency parsing is adopting neural networks due to their significant success in a wide range of applications. |
| Approach: | They propose a sequence to sequence (seq2seque) dependency parser that predicts the relative position of head for each word. |
| Outcome: | The proposed parser achieves 94.11% UAS on PTB and 88.78% UAS . |
Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models (2020.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding models suffer from limited knowledge representation due to sparse and noisy dataset annotations. |
| Approach: | They propose to use pretrained language models to enhance knowledge representation by leveraging world knowledge from pretrained models. |
| Outcome: | Extensive experiments show that the proposed framework can improve results over existing models. |
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)
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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen
| Challenge: | Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges. |
| Approach: | They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task. |
| Outcome: | The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing. |