Papers by He Wang
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). |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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-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%. |
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 . |
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. |
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. |
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. |
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. |
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 . |
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. |
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%. |
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. |
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. |
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. |
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. |
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%). |
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. |
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. |
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 . |
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. |
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. |
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/ . |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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%. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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. |
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. |
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. |
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%. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 . |
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. |
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 . |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
Towards Boosting the Open-Domain Chatbot with Human Feedback (2023.acl-long)
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| Challenge: | Existing frameworks for pre-training open-domain dialogue models with social media comments generate coherent replies but have difficulties producing engaging responses. |
| Approach: | They propose a framework to boost the open-domain chatbot by leveraging human feedback and annotating the model's candidate responses. |
| Outcome: | The proposed framework boosts the open-domain chatbot by leveraging human demonstrated responses and leveraging the implicit preference in the data collection process. |
Self-Correction is More than Refinement: A Learning Framework for Visual and Language Reasoning Tasks (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown remarkable abilities, but they invariably generate flawed responses. |
| Approach: | They propose a self-correction approach that instructs VLMs to refine their outputs by allowing them to learn from their self-generated self-reference data without external feedback. |
| Outcome: | The proposed approach enables VLMs to learn from their self-generated self-correction data without relying on external feedback, facilitating self-improvement. |
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems (2025.acl-industry)
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Jianling Wang, Yifan Liu, Yinghao Sun, Xuejian Ma, Yueqi Wang, He Ma, Zhengyang Su, Minmin Chen, Mingyan Gao, Onkar Dalal, Ed H. Chi, Lichan Hong, Ningren Han, Haokai Lu
| Challenge: | Large Language Models (LLMs) can be used to broaden user experiences beyond established preferences and reinforce feedback loops. |
| Approach: | They propose a hierarchical approach that combines hierarchic planning with LLM inference-time scaling to improve recommendation relevancy without compromising novelty. |
| Outcome: | The proposed approach shows significant gains in both user satisfaction and exploration diversity. |
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension (2023.findings-emnlp)
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Nuo Chen, Hongguang Li, Junqing He, Yinan Bao, Xinshi Lin, Qi Yang, Jianfeng Liu, Ruyi Gan, Jiaxing Zhang, Baoyuan Wang, Jia Li
| Challenge: | Existing benchmarks for conversational machine reading comprehension are inconsistent with real scenarios. |
| Approach: | They propose to use a Chinese CMRC benchmark to evaluate model's generalization ability towards diverse domains by using zero-shot/few-shot settings. |
| Outcome: | The proposed benchmarks are based on 831 hot-topic driven conversations with 4,742 turns and cover 33 domains. |
STRICT: Stress-Test of Rendering Image Containing Text (2025.emnlp-main)
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| Challenge: | Despite the advances in diffusion models, the generation of coherent text remains a major bottleneck. |
| Approach: | They propose a benchmark to test the ability of diffusion models to render coherent text in images. |
| Outcome: | The proposed model fails to generate coherent and legible text in images despite its iterative nature . the model fails in both the maximum length of readable text and correctness and legibility of the generated text . |
Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data (2025.findings-naacl)
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Juanhui Li, Sreyashi Nag, Hui Liu, Xianfeng Tang, Sheikh Muhammad Sarwar, Limeng Cui, Hansu Gu, Suhang Wang, Qi He, Jiliang Tang
| Challenge: | Large Language Models (LLMs) have demonstrated superior language understanding abilities in many real-world NLP applications. |
| Approach: | They propose a learning-based sample selection method that incorporates signals from both teacher and student to enhance model performance. |
| Outcome: | The proposed method improves model performance across datasets with higher data efficiency. |
InfiMM-WebMath-40B: Advancing Multimodal Pre-Training for Enhanced Mathematical Reasoning (2025.findings-emnlp)
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Xiaotian Han, Yiren Jian, Xuefeng Hu, Haogeng Liu, Yiqi Wang, Qihang Fan, Yuang Ai, Huaibo Huang, Ran He, Zhenheng Yang, Quanzeng You
| Challenge: | InfiMM-WebMath-40B is a dataset of interleaved image-text documents . it consists of 24 million web pages, 85 million image URLs, and 40 billion text tokens . |
| Approach: | InfiMM-WebMath-40B is a high-quality dataset of interleaved image-text documents . it contains 24 million web pages, 85 million image URLs, and 40 billion text tokens . |
| Outcome: | InfiMM-WebMath-40B is a high-quality dataset of interleaved image-text documents . it consists of 24 million web pages, 85 million image URLs, and 40 billion text tokens . |
STaR-SQL: Self-Taught Reasoner for Text-to-SQL (2025.acl-long)
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| Challenge: | Existing methods for generating step-by-step “chain-of-thought” rationales are limited to text-to-SQL. |
| Approach: | They propose a method that prompts SQL query generation to produce reasoning steps for SQL queries and fine-tunes it on rationales that lead to correct outcomes. |
| Outcome: | The proposed method outperforms agent-like prompting methods on the Spider benchmark. |
SUA: Stealthy Multimodal Large Language Model Unlearning Attack (2025.emnlp-main)
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| Challenge: | Multimodal Large Language Models (MLLMs) trained on massive data may memorize sensitive personal information and photos, posing privacy and copyright concerns. |
| Approach: | They propose a framework that learns a universal noise pattern to recover unlearned information from MLLMs. |
| Outcome: | The proposed framework learns a universal noise pattern and can reveal unlearned content when applied to images. |
BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answering (2025.acl-long)
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| Challenge: | Existing studies on multi-hop question answering employ specific methods regardless of question types . complexity of multihop question answerrs often exceeds knowledge boundaries of LLMs . |
| Approach: | They propose a framework that uses chain-of-thought prompting to prompt LLMs to answer multi-hop questions. |
| Outcome: | The proposed framework outperforms baseline models in multi-hop QA scenarios. |
The CRECIL Corpus: a New Dataset for Extraction of Relations between Characters in Chinese Multi-party Dialogues (2022.lrec-1)
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Yuru Jiang, Yang Xu, Yuhang Zhan, Weikai He, Yilin Wang, Zixuan Xi, Meiyun Wang, Xinyu Li, Yu Li, Yanchao Yu
| Challenge: | Existing datasets focus on relation extraction between two entities in one sentence, and some focus on cross-sentence relationships. |
| Approach: | They propose to use a Chinese multi-party dialogue dataset for automatic extraction of dialogue-based character relationships. |
| Outcome: | The proposed dataset extracts relationships between 140 entities on the CRECIL corpus and another existing relation extraction corpus. |
Can LLM Graph Reasoning Generalize beyond Pattern Memorization? (2024.findings-emnlp)
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| Challenge: | Existing studies seek to enhance the graph reasoning capabilities of Large Language Models (LLMs) by specialized instruction tuning. |
| Approach: | They propose to evaluate LLM graph reasoning generalization using in-distribution settings . they propose to use three strategies to improve LLM generalization . |
| Outcome: | The proposed benchmark evaluates LLM graph reasoning generalization with in-distribution settings only . it shows that LLMs struggle to generalize across reasoning and real-world patterns . |
Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation (2026.findings-acl)
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| Challenge: | Multi-agent systems (MAS) are increasingly used for open-ended idea generation . when and why collective interaction expands the solution space remains unclear . |
| Approach: | They propose to study diversity in multi-agent systems across three bottom-up levels: model intelligence, agent cognition, and system dynamics. |
| Outcome: | The proposed model yields diminishing diversity despite higher quality . the proposed model fails to expand diversity and causes it to collapse . |
Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery (2023.acl-long)
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| Challenge: | Existing methods for generalized intent discovery lack pseudo label disambiguation and representation learning. |
| Approach: | They propose a prototype learning framework to decouple pseudo label disambiguation and representation learning. |
| Outcome: | The proposed method can decouple pseudo label disambiguation and representation learning. |
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding (2020.acl-main)
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| Challenge: | Existing knowledge graph embeddings have improved the knowledge graph link prediction task, but complex relations such as N-to-1, 1-to-N and N- to-N remain challenging to predict. |
| Approach: | They propose to extend RotatE from 2D complex domain to high dimensional space with orthogonal transforms to model relations. |
| Outcome: | The proposed method improves on N-to-1, 1-to-N and N- to-N cases while maintaining the capability for modeling symmetric/anti-symmetric, inverse and compositional relations. |
Semantic-Space Exploration and Exploitation in RLVR for LLM Reasoning (2026.findings-acl)
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Fanding Huang, Guanbo Huang, Xiao Fan, Yi He, Xiao Liang, Xiao Chen, Qinting Jiang, Faisal Nadeem Khan, Jingyan Jiang, Zhi Wang
| Challenge: | Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have substantially improved the reasoning abilities of Large Language Models (LLMs). |
| Approach: | They propose a method that balances exploration and exploitation in the hidden-state space of response trajectories. |
| Outcome: | The proposed model yields consistent improvements across models, algorithms and reasoning benchmarks. |
Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation (2022.naacl-main)
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| Challenge: | BLEU scores of 31.16 for ende and 38.37 for deen on the IWSLT14 dataset, 30.78 for entde, 35.15 for de en and 27.17 for zhen . |
| Approach: | They propose a bidirectional pretraining and unidirectional finetuning procedure to boost NMT performance. |
| Outcome: | The proposed method achieves strong translation performance across five datasets. |
SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection (2026.acl-industry)
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Zhiyong Cao, Dunqiang Liu, Qi Dai, Haojun Xu, Huai Yuen Khor, Hao Wang, Huan He, Yafei Liu, Ke Ma, Ruqian Shi, Sicheng Zhou, Sijia Yao
| Challenge: | High-quality data in training proactive dialogue agents is scarce, despite fine-tuning and reinforcement learning . a recent study has shown that the effectiveness of supervised fine-touring is limited by the lack of high-quality, domain-specific training data. |
| Approach: | They propose a framework for training recruitment proactive dialogue agents using a high-fidelity user simulator and a multi-dimensional evaluation framework based on Chain-of-Intention. |
| Outcome: | The proposed framework outperforms existing simulator-based data selection strategies in a real-world recruitment scenario. |
Fin-STAR: Structure-as-Semantics to Resolve Implicitness in Financial Retrieval (2026.findings-acl)
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Yu Zou, Yan Chen, Lida He, Qi Zhou, Xiaorui Zhou, Aixi Zhong, Yi Wang, Wei Li, Qingyu Wang, Jiatao Li, Wei Gong, Jialei Zeng, Jingmei Zhao, Ke Jiang, Qing Li
| Challenge: | Existing Retrieval-Augmented Generation systems treat structure as a physical navigational skeleton rather than intrinsic semantic knowledge. |
| Approach: | They propose a framework that redefining hierarchy as intrinsic semantics and uses snippets to enrich hierarchical lineage. |
| Outcome: | The proposed framework outperforms state-of-the-art hierarchical and graph-based benchmarks on FinTierQA Gold. |
SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining (2021.acl-long)
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| Challenge: | Existing knowledge-based PLMs are based on linked-entity information, but they only use linked-enemy information as auxiliary information. |
| Approach: | They propose to integrate semantic knowledge from neighbours of linked-entity into a medical PLM that integrates heterogeneous-entities into the homogeneously neighbouring entity structure. |
| Outcome: | Experiments show that SMedBERT outperforms baselines in knowledge-intensive Chinese medical tasks. |
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification (P18-1)
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| Challenge: | Recent years have seen rapid growth in the MRC community . MRC is believed to be a crucial step in building a general intelligent agent . |
| Approach: | They propose an end-to-end neural model that enables multiple passages to verify each other based on their content representations. |
| Outcome: | The proposed model outperforms the baseline on the English MS-MARCO dataset and the Chinese DuReader dataset, and achieves state-of-the-art performance on both datasets. |
SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models (2026.findings-acl)
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Yifu Huo, Chenglong Wang, Ziming Zhu, Shunjie Xing, Peinan Feng, Tongran Liu, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Zhengtao Yu, Tong Xiao
| Challenge: | Reinforcement learning (RL) training typically improves single-sample success rates but limited exploration of diverse reasoning trajectories. |
| Approach: | They propose a training paradigm that interleaves conventional RL with inverse reinforcement learning (IRL) they propose 'Steering Probability Squeezing' to enhance exploration without external supervision . |
| Outcome: | The proposed training paradigm improves Pass@k and improves exploration of diverse reasoning trajectories without external supervision. |
Continuous Relational Diffusion Driven Topic Model with Multi-grained Text for Microblog (2024.lrec-main)
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| Challenge: | Existing topic models assume that there are only 0/1-state relationships between the two parties in social networks, but the relationship status in real life is more complicated. |
| Approach: | They propose a topic model that leverages unsupervised learning to mine hidden topics in document collections using multi-grained text. |
| Outcome: | The proposed model can be applied to microblog with multi-grained text to realize the representation of the relationship state and make up for the context and structural information lost by previous representation methods. |
Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training (2024.acl-long)
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Junqing He, Kunhao Pan, Xiaoqun Dong, Zhuoyang Song, LiuYiBo LiuYiBo, Qianguosun Qianguosun, Yuxin Liang, Hao Wang, Enming Zhang, Jiaxing Zhang
| Challenge: | Large language models suffer from severe hallucinations, compromising performance in knowledge-oriented QA, dialogue, and writing. |
| Approach: | They propose to enhance the information searching and reflection ability of large language models by training them in position-agnostic multi-step QA tasks to improve their model's accuracy. |
| Outcome: | The proposed model improves in multi-doc QA and other benchmarks by 13.7% absolute gain in shuffled settings, by 21.5% in passage retrieval task. |
Correcting Chinese Spelling Errors with Phonetic Pre-training (2021.findings-acl)
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Ruiqing Zhang, Chao Pang, Chuanqiang Zhang, Shuohuan Wang, Zhongjun He, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Existing methods for Chinese spelling correction only use pre-trained language model or incorporate phonological information as external knowledge. |
| Approach: | They propose a phonetic Chinese spelling correction model that integrates phonetic features into language model by leveraging pre-training and fine-tuning methods. |
| Outcome: | The proposed model outperforms existing methods on SIGHAN datasets and improves on other datasets. |
NILE: Internal Consistency Alignment in Large Language Models (2025.emnlp-main)
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Minda Hu, Qiyuan Zhang, Yufei Wang, Bowei He, Hongru Wang, Jingyan Zhou, Liangyou Li, Yasheng Wang, Chen Ma, Irwin King
| Challenge: | Recent advances show that the world knowledge in the Instruction Fine-Tuning (IFT) dataset, which is incompatible with LLMs’ internal knowledge, can greatly hurt the IFT performance. |
| Approach: | They propose a framework to optimize the effectiveness of IFT by carefully aligning the world and internal knowledge of LLMs. |
| Outcome: | The proposed framework can significantly improve performance across multiple LLM ability evaluation datasets. |
APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection (2023.findings-emnlp)
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Pei Wang, Keqing He, Yutao Mou, Xiaoshuai Song, Yanan Wu, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
| Challenge: | Existing methods for detecting out-of-domain (OOD) intents are hard to label . previous studies use labeled in-domain data to learn intent representations . |
| Approach: | They propose a prototypical pseudo-labeling method for few-shot OOD detection . they propose 'protoOOD' framework and adaptive pseudo-labeled method . |
| Outcome: | The proposed method is able to detect out-of-domain (OOD) intents from user queries. |
Hint-Based Training for Non-Autoregressive Machine Translation (D19-1)
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| Challenge: | AutoRegressive Translation models have to generate tokens sequentially during decoding and thus suffer from high inference latency. |
| Approach: | They propose to use hidden states and word alignments to help train NART models. |
| Outcome: | The proposed model improves on the WMT14 En-De and De-En datasets but is faster in inference than the current models. |
VisCRA: A Visual Chain Reasoning Attack for Jailbreaking Multimodal Large Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in Large Reasoning Models (LRMs) have enabled sophisticated visual reasoning capabilities by integrating reinforcement learning and Chain-of-Thought (CoT) supervision. |
| Approach: | They propose a jailbreak framework that exploits visual reasoning chains to bypass safety mechanisms. |
| Outcome: | The proposed framework achieves high attack success rates on leading closed-source MLRMs. |
Multi-Scale Distribution Deep Variational Autoencoder for Explanation Generation (2022.findings-acl)
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| Challenge: | Existing methods for generating explanations for recommender systems produce generic explanations that fail to incorporate user and item specific details. |
| Approach: | They propose a multi-scale distribution deepvariational autoencoder with a prior network that eliminates noise while retaining meaningful signals in the input. |
| Outcome: | The proposed models can generate explanations with concrete input-specific contents. |
EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)
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Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo, Sheikh Muhammad Sarwar, Yang Li, Hansu Gu, Hui Liu, Changlong Yu, Jiaxin Bai, Yifan Gao, Haiyang Zhang, Qi He, Shuiwang Ji, Yangqiu Song
| Challenge: | Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges . |
| Approach: | They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions. |
| Outcome: | The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset . |
Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation (2026.acl-long)
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| Challenge: | Recent advances in large vision-language models produce hallucinations that compromise output reliability. |
| Approach: | They propose a dual-stage framework for mitigating hallucinations without performance degradation . they propose semantic-aware component disentanglement and interpretable parameter updates . |
| Outcome: | The proposed model reduces hallucinations by 23.4% while maintaining 97.4% of general generative capability. |
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents (2021.acl-short)
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| Challenge: | Faceted summarization provides briefings of a document from different perspectives. |
| Approach: | They propose a faceted summarization benchmark built on Emerald journal articles . they propose faceted models that bring structure into faceted documents . |
| Outcome: | The proposed benchmark is based on Emerald journal articles and covers a diverse range of domains. |
FocalOrder: Focal Preference Optimization for Reading Order Detection (2026.acl-long)
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Fuyuan Liu, Dianyu Yu, He Ren, Nayu Liu, Xiaomian Kang, Delai Qiu, Fa Zhang, Genpeng Zhen, Shengping Liu, Liang Jiaen, null Weihuang, Yining Wang, Junnan Zhu
| Challenge: | Existing methods for document comprehension rely on uniform supervision, resulting in a performance degradation in the intermediate sections. |
| Approach: | They propose a framework driven by Focal Preference Optimization to detect reading order in document layouts. |
| Outcome: | The proposed framework outperforms competing baselines and surpasses large-scale general VLMs. |
EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety (2025.emnlp-main)
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Jiahao Qiu, Yinghui He, Xinzhe Juan, Yimin Wang, Yuhan Liu, Zixin Yao, Yue Wu, Xun Jiang, Ling Yang, Mengdi Wang
| Challenge: | EmoAgent evaluates and mitigates mental health hazards in human-AI interactions, especially for vulnerable human users with psychological disorders. |
| Approach: | EmoAgent is a multi-agent AI framework designed to evaluate and mitigate mental health hazards in human-AI interactions. |
| Outcome: | EmoAgent evaluates and mitigates mental health hazards in human-AI interactions. |
Language Models over Large-Scale Knowledge Base: on Capacity, Flexibility and Reasoning for New Facts (2025.coling-main)
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| Challenge: | Existing studies on LMs lack systematic studies on their structured reasoning capabilities over the infused knowledge. |
| Approach: | They investigate how LMs of different sizes can store world knowledge of different frequencies in a large-scale KB after training on the abundant world knowledge triplets. |
| Outcome: | The proposed models can store and respond to natural language queries with flexibility and reasoning abilities, but they need to be enhanced to fully realize their potential. |
On the Representation Geometry of LoRA Model Merging (2026.findings-acl)
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| Challenge: | Existing methods for low-rank Adaptation (LoRA) fine-tuning focus on globally shared structure . combining SVD with CUR improves performance of LoRA model merging . |
| Approach: | They propose a training-free method that combines SVD and CUR decomposition to improve LoRA merging performance. |
| Outcome: | The proposed procedure improves on vision and language benchmarks. |
R-Judge: Benchmarking Safety Risk Awareness for LLM Agents (2024.findings-emnlp)
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Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, Rui Wang, Gongshen Liu
| Challenge: | Large language models (LLMs) have shown compelling abilities in reasoning, decision-making, and instruction following. |
| Approach: | They propose a benchmark to evaluate the proficiency of large language models (LLMs) in judging and identifying safety risks given agent interaction records. |
| Outcome: | The proposed model outperforms the best-performing model, GPT-4o, while no other models significantly exceed the random. |
Learning Multilingual Sentence Representations with Cross-lingual Consistency Regularization (2023.emnlp-industry)
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| Challenge: | Experimental results on multilingual similarity search and bitext mining tasks show the effectiveness of our approach. |
| Approach: | They propose a multilingual sentence representation model that aligns different languages in a shared representation space. |
| Outcome: | The proposed model performs better than LASER3 on similarity searches and bitext mining tasks. |
Safeguarding LLM Fine-tuning via Push-Pull Distributional Alignment (2026.acl-long)
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| Challenge: | Existing safety defenses for large language models fail to explicitly repel harmful patterns . Optimal transport (SOT) allows for safe fine-tuning without sacrificing safety . |
| Approach: | They propose a framework that reframes safe fine-tuning from instance-level filtering challenge to distribution-level alignment task grounded in Optimal Transport. |
| Outcome: | a new framework improves safety of large language models while maintaining competitive performance . the proposed framework reduces the risk of errors and improves model performance compared to baselines . |
Mirror: Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning (2024.acl-long)
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| Challenge: | Large language models (LLMs) struggle with knowledge-rich problems without external resources. |
| Approach: | They propose a Multiple-perspective self-reflection method that allows LLMs to reflect from multiple-perceptive clues, achieved through a heuristic interaction between a Navigator and a Reasoner. |
| Outcome: | The proposed method is superior to other self-reflection methods on five reasoning datasets. |
QDTSynth: Quality-Driven Formal Theorem Synthesis for Enhancing Proving Performance of LLMs (2025.acl-long)
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| Challenge: | Existing formal languages such as Lean, Coq and Metamath are proving to be useful in formal theorem proving . however, there is a scarcity of high-quality supervised fine-tuning data for formal proofs . |
| Approach: | They propose a Q**uality-**D**riven **T**heorem **S**ynthesis method in Lean4 . they propose diversity screening and the self-assessment method to select theoremas that exhibit diversity and high quality from the initial synthetic statements. |
| Outcome: | The proposed method significantly improves performance of open-source LLMs in theorem proving tasks. |
Ranking Human and LLM Texts Using Locality Statistics (2026.findings-eacl)
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| Challenge: | The paper extends the Data Movement Distance (DMD) metric defined to measure the locality in computer memory to text by defining a new term designed to better characterize low-frequency tokens. |
| Approach: | They propose to define a normalized version of the Data Movement Distance (nDMD) term is designed to better characterize low-frequency tokens. |
| Outcome: | The proposed normalized version outperforms baselines and improves performance on the English subset of the M4 dataset and the GenAI detection shared task. |
Exploratory Neural Relation Classification for Domain Knowledge Acquisition (C18-1)
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| Challenge: | Existing methods for relation classification are limited and lack of low-frequency relations in specific domains. |
| Approach: | They propose a method to learn a classifier on pre-defined relations and discover new relations expressed in texts. |
| Outcome: | The proposed method can classify entities into a finite set of relations and discover relations with high precision and recall. |
ACE-M3: Automatic Capability Evaluator for Multimodal Medical Models (2025.coling-main)
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| Challenge: | Existing metrics for multimodal large language models only focus on token overlap and may not align with human judgment. |
| Approach: | They propose an open-source model that assesses the question answering abilities of multimodal large language models. |
| Outcome: | Experiments show that the ACE-M3 model performs better than existing models and is more reliable than existing metrics. |
Text-like Encoding of Collaborative Information in Large Language Models for Recommendation (2024.acl-long)
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| Challenge: | Existing methods to adapt Large Language Models for Recommendation (LLMRec) do not represent collaborative information in a text-like format, which may not align optimally with LLMs. |
| Approach: | They propose a novel LLMRec method that integrates collaborative information through text-like encoding. |
| Outcome: | Extensive experiments show that BinLLM integrates collaborative information better with LLMs. |
A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges (2025.findings-acl)
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Yibo Yan, Jiamin Su, Jianxiang He, Fangteng Fu, Xu Zheng, Yuanhuiyi Lyu, Kun Wang, Shen Wang, Qingsong Wen, Xuming Hu
| Challenge: | This survey provides **the first comprehensive analysis of mathematical reasoning in the era of multimodal large language models** . integrating large language model with mathematical reasoning tasks is becoming significant as AI advances . |
| Approach: | They review over 200 studies published since 2021 and examine the state-of-the-art developments in Math-LLMs . they identify five major challenges hindering the realization of AGI in this domain . |
| Outcome: | The authors examine the state-of-the-art developments in Math-LLMs with a focus on multimodal settings. |
DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models (2025.naacl-long)
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Jianyu Liu, Hangyu Guo, Ranjie Duan, Xingyuan Bu, Yancheng He, Shilong Li, Hui Huang, Jiaheng Liu, Yucheng Wang, Chenchen Jing, Xingwei Qu, Xiao Zhang, Pei Wang, Yanan Wu, Jihao Gu, Yangguang Li, Jianke Zhu
| Challenge: | Multimodal Large Language Models (MLLMs) pose unique safety challenges due to their integration of visual and textual data. |
| Approach: | They propose a method to disentangle risks through step-by-step reasoning within multimodal inputs. |
| Outcome: | The proposed approach improves safety alignment in MLLMs by fine-tuning and iterative Reinforcement Learning from AI feedback. |
Mathematical Proof as a Litmus Test: Revealing Failure Modes of Advanced Large Reasoning Models (2026.acl-long)
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| Challenge: | Large reasoning models have demonstrated remarkable mathematical problem-solving abilities, but their true reasoning shortcomings are often hidden. |
| Approach: | They propose to leverage the rigor and methodological complexity of mathematical proofs as a diagnostic tool to expose hidden failures. |
| Outcome: | The proposed model evaluation exploits the rigor and complexity of proof problems to uncover 10 fine-grained errors. |
On the Blind Spots of Model-Based Evaluation Metrics for Text Generation (2023.acl-long)
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| Challenge: | Existing methods for text generation evaluation metrics are lacking in robustness analysis. |
| Approach: | They propose to use stress tests to test for errors in text generation evaluation metrics . they find that BERTScore is confused by truncation errors in summarization . |
| Outcome: | The proposed stress tests show that they are insensitive to errors in open-ended generation, translation, and summarization. |
PairRE: Knowledge Graph Embeddings via Paired Relation Vectors (2021.acl-long)
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| Challenge: | Existing knowledge graph embedding methods fail to solve two major problems at the same time, leading to unsatisfactory results. |
| Approach: | They propose a model with paired vectors for each relation representation that can be adaptively adjusted to fit for different complex relations. |
| Outcome: | Experiments on two knowledge graph datasets show the proposed model can handle complex relations and encode relation patterns. |
Unveiling Privacy Risks in LLM Agent Memory (2025.acl-long)
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| Challenge: | Large Language Model (LLM) agents store private user-agent interactions in memory for demonstrations, introducing new privacy risks for LLM agents. |
| Approach: | They propose an attack that extracts private information from memory under a black-box setting and propose a method that can be used to attack the agent. |
| Outcome: | The proposed attack is effective under a black-box setting and it is demonstrated on two representative agents. |
CoRAG: Enhancing Hybrid Retrieval-Augmented Generation through a Cooperative Retriever Architecture (2025.findings-emnlp)
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| Challenge: | Existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base, which often miss relevant information located further away from a global view. |
| Approach: | Hybrid-RAG combines textual documents and graph-structured relational information for RAG . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base . |
| Outcome: | Hybrid-RAG combines textual documents and graph-structured relational information . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base . |
JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse (2025.findings-acl)
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| Challenge: | Visual Language Action models have shown promise in decision-making tasks, but have been neglected in previous work . |
| Approach: | They propose a new paradigm for visual language action models that enhances the foundation model prior to action-specific tuning by first post-training it on a curated set of visual and linguistic tasks using self-supervised learning. |
| Outcome: | The proposed model outperforms the best agent baseline on a diverse set of atomic tasks and surpasses imitation learning-based policies in Minecraft. |
Foiling Training-Time Attacks on Neural Machine Translation Systems (2022.findings-emnlp)
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| Challenge: | Neural machine translation systems are vulnerable to backdoor attacks . successful backdoors can cause slander, hate speech, phishing, etc. attacks can target very short trigger phrases, which can be challenging to detect even when included verbatim in poisoned instances. |
| Approach: | They propose a method that exploits asymmetry between source and target sentences to detect outlier tokens. |
| Outcome: | The proposed method reduces the success of attacks by up to 89.0% while not affecting predictive accuracy. |
BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis (2022.findings-acl)
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| Challenge: | Aspect-based sentiment analysis is challenging because a sentence may contain multiple aspects or complicated relationships. |
| Approach: | They propose a bi-syntax aware Graph Attention Network to model the context of every aspect and sentiment relations across aspects for learning. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on four benchmark datasets. |
Backdoor Attacks on Multilingual Machine Translation (2024.naacl-long)
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| Challenge: | Recent studies have shown that multilingual machine translation systems are vulnerable to backdoor attacks through data poisoning. |
| Approach: | They propose to investigate the security of multilingual machine translation systems by exposing poisoned data into low-resource languages to cause malicious translations. |
| Outcome: | The proposed method achieves an average of 20% success rate in attacking high-resource languages. |
Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual (D19-61)
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| Challenge: | Statistical natural language inference models are susceptible to learning dataset bias. |
| Approach: | They propose a debiasing algorithm that debiases models that use only known dataset biases . they use two benchmark datasets to train three high-performing NLI models . |
| Outcome: | The proposed learning objective improves model performance on challenge datasets while maintaining reasonable performance on original datasets. |
DenseSSM: State Space Models with Dense Hidden Connection for Efficient Large Language Models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) face excessive computational and memory requirements due to the commonly used Transformer architecture. |
| Approach: | They propose a method to enhance the flow of hidden information between layers in large language models by selectively integrating shallow-layer hidden states into deeper layers. |
| Outcome: | The proposed method maintains parallelizability and inference efficiency of SSMs while significantly boosting performance on public benchmarks. |
RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services (2025.emnlp-industry)
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Fei Zhao, Chonggang Lu, null Wangyue, Zheyong Xie, Ziyan Liu, Haofu Qian, Jianzhao Huang, Fangcheng Shi, Zijie Meng, Hongcheng Guo, Mingqian He, Xinze Lyu, Zheyu Ye, Weiting Liu, Boyang Wang, Shaosheng Cao
| Challenge: | Social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement. |
| Approach: | They propose a domain-specific LLM to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for social networking services. |
| Outcome: | The proposed model achieves an average improvement of 14.02% across 8 major tasks and 7.56% in bilingual evaluation benchmark, compared with baseline models. |
A Simple and Efficient Learning-Style Prompting for LLM Jailbreaking (2026.findings-eacl)
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| Challenge: | Learning-style queries can reliably elicit harmful responses, highlighting a critical safety blind spot in modern LLMs. |
| Approach: | They propose a new reframing paradigm that hides intention by learning from LLMs and uses 4 conceptual components to construct learning-style queries. |
| Outcome: | The proposed framework achieves top attack success rates on most models and across malicious categories while maintaining high efficiency with concise prompts. |
DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models (2023.acl-long)
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| Challenge: | Existing generative masked language models have a shared training objective, i.e., denoising. |
| Approach: | They propose a noise schedule for the forward diffusion process that controls the degree of noise added at each step based on the information of each token. |
| Outcome: | The proposed model improves on existing models in terms of perplexity and BLEU score. |
RoR: Read-over-Read for Long Document Machine Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Existing models for machine reading comprehension are limited to individual chunks due to encoding length constraint. |
| Approach: | They propose a read-over-read method that expands the reading field from chunk to document by predicting regional answers for each chunk. |
| Outcome: | Extensive experiments on QuAC and TriviaQA show that the proposed model performs well for long document reading. |
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product (2020.emnlp-main)
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| Challenge: | In the real world, product attribute values are incomplete and vary over time, which hinders practical applications. |
| Approach: | They propose a multimodal method to jointly predict product attributes and extract values from product images using multimodal product information. |
| Outcome: | The proposed method can predict product attributes and extract values from product images with the help of product images. |
Affective and Dynamic Beam Search for Story Generation (2023.findings-emnlp)
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| Challenge: | AffGen introduces ‘intriguing twists’ in narratives by employing two novel techniques—Dynamic Beam Sizing and Affective Reranking. |
| Approach: | They propose to use dynamic beam sizing and affective reranking to generate interesting stories using two novel techniques. |
| Outcome: | The proposed method outperforms baseline models in generating affectively charged and interesting narratives. |
PENS: A Dataset and Generic Framework for Personalized News Headline Generation (2021.acl-long)
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| Challenge: | Using a dataset of Microsoft News, we propose a generic framework to personalize a text generator and establish personalized headlines. |
| Approach: | They propose a generic framework to personalize a news headline generator and establish personalized headlines by leveraging user behavioral data. |
| Outcome: | The proposed framework is based on user preference data and user preference injections to personalize a text generator and establish personalized headlines. |
CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution (2026.acl-long)
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Xiangxi Zheng, Kuang He, Jiayi Hu, Ping Yu, Rui Yan, Yuan Yao, Peng Hou, Anxiang Zeng, Alex Jinpeng Wang
| Challenge: | Existing approaches to chart-to-code generation are constrained by data-centric limitations . authors present a new framework that redesigns both training and alignment data . |
| Approach: | They propose a data-centric framework that redesigns both training and alignment data for chart-to-code generation. |
| Outcome: | The proposed framework outperforms open-source baselines and is competitive with GPT-5. |
The Dark Side of Function Calling: Pathways to Jailbreaking Large Language Models (2025.coling-main)
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| Challenge: | Large language models (LLMs) have remarkable capabilities, but their security implications have been overlooked. |
| Approach: | They propose a “jailbreak function” attack method that exploits alignment discrepancies, user coercion, and the absence of rigorous safety filters. |
| Outcome: | The proposed attack exploits alignment discrepancies, user coercion, and the absence of rigorous safety filters on six state-of-the-art LLMs. |
OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)
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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao
| Challenge: | Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability. |
| Approach: | They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability. |
| Outcome: | Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA. |
Unsupervised Sign Language Translation and Generation (2024.findings-acl)
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Zhengsheng Guo, Zhiwei He, Wenxiang Jiao, Xing Wang, Rui Wang, Kehai Chen, Zhaopeng Tu, Yong Xu, Min Zhang
| Challenge: | Experimental results on the BBC-Oxford Sign Language dataset reveal that USLNet achieves competitive results compared to supervised baseline models. |
| Approach: | They propose an unsupervised sign language translation and generation network that learns from abundant single-modality data without parallel sign language data. |
| Outcome: | The proposed model achieves competitive results compared to baseline models on the BBC-Oxford Sign Language dataset and Open-Domain American Sign Language data. |
Tree-CoT-RT: An Explainable Multi-Path Tree-Guided Chain-of-Thought and Reinforcement Learning Framework for Aspect Sentiment Quad Prediction (2026.findings-acl)
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Hao Zhang, Jiahao Wang, Zhenke Duan, Xin Yin, Haichuan Hu, Hualong Chen, null Suyi, Congqing He, Yike Tan, Yu-N Cheah
| Challenge: | Existing methods lack explainability and generalization, making it difficult to justify inference decisions and detect implicit sentiment across domains and varied expression patterns. |
| Approach: | They propose an explainable multi-path tree-guided chain-of-thought framework specifically designed for ASQP. |
| Outcome: | Experiments on benchmark datasets show that Tree-CoT-RT outperforms baselines. |
Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors (2026.acl-long)
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Rui Yin, Tianxu Han, Naen Xu, Changjiang Li, Ping He, Chunyi Zhou, Jun Wang, Zhihui Fu, Tianyu Du, Jinbao Li, Shouling Ji
| Challenge: | Existing methods to inject safety-aligned large language models rely on token-level mappings, which do not guarantee sustained harmful output. |
| Approach: | They propose a method that directly modifies model weights to map a trigger to an attacker-specified response. |
| Outcome: | The proposed method achieves high triggered attack success while maintaining non-triggered safety and general utility. |
Emotion Recognition in Conversation via Dynamic Personality (2024.lrec-main)
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| Challenge: | Existing approaches to ERC focus on conversational contexts, but focus on static personality. |
| Approach: | They propose a model that considers the dynamic personality of speakers during conversations. |
| Outcome: | The proposed model outperforms existing models on three benchmark conversational datasets. |
Customizing In-context Learning for Dynamic Interest Adaption in LLM-based Recommendation (2025.findings-acl)
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| Challenge: | Existing Large Language Model (LLM)-based recommender systems face challenges to adapt to dynamic user interests without any model-level updates. |
| Approach: | They propose a framework that establishes recommendation-oriented in-context learning by structuring recent user interactions and current inputs into ICL formats. |
| Outcome: | The proposed model adapts to dynamic user interests without model updates without any model updates and is available online at https://anonymous.4open.science/r/RecICL-8003. |
CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems (2026.acl-long)
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| Challenge: | LLM-based multi-agent systems (MAS) have demonstrated remarkable capabilities in solving complex tasks. |
| Approach: | They propose a communication inference attack that constructs new adversarial queries to induce intermediate agents’ reasoning outputs and models their semantic correlations through the global bias disentanglement and LLM-guided weak supervision. |
| Outcome: | The proposed attack achieves an average AUC of 0.87 and a peak AUC up to 0.99, revealing the privacy risk in MAS. |
Mixup Decoding for Diverse Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing methods for generating multiple translations for source and target languages neglect the one-to-many mapping between the source and the target languages. |
| Approach: | They propose a method to generate different translations for the input sentence by linearly interpolating it with different sentence pairs sampled from the training corpus during decoding. |
| Outcome: | Experiments on WMT’16 en-ro, WMT'14 en de, and WMT ‘17 zh-en show that the proposed method outperforms all previous diverse machine translation methods. |
Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularization (2023.findings-acl)
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| Challenge: | Existing methods to improve zero-shot translation performance by learning language-agnostic representations and maximizing cross-lingual transfer have been proposed. |
| Approach: | They propose a cross-lingual consistency regularization to bridge the representation gap between different languages and boost zero-shot translation performance. |
| Outcome: | The proposed model improves translation performance on low-resource and high-res benchmarks and closes the sentence representation gap and aligns the representation space. |
GLiM: Integrating Graph Transformer and LLM for Document-Level Biomedical Relation Extraction with Incomplete Labeling (2025.findings-acl)
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Hao Fang, Yuejie Zhang, Rui Feng, Yingwen Wang, Qing Wang, Wen He, Xiaobo Zhang, Tao Zhang, Shang Gao
| Challenge: | Document-level relation extraction (DocRE) solves problems of document quality . number of entities and entity-pair relations increases, causing incomplete annotations . |
| Approach: | a framework that reduces the problem space using a graph-enhanced Transformer-based model is proposed . GLiM leverages large language models for reasoning to reduce the problem-space . |
| Outcome: | GLiM boosts average recall and F1 scores on biomedical datasets . compared with existing models, GLim outperforms existing models on biomedicine benchmarks compared to existing models . |
LMR-BENCH: Evaluating LLM Agent’s Ability on Reproducing Language Modeling Research (2025.emnlp-main)
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Shuo Yan, Ruochen Li, Ziming Luo, Zimu Wang, Daoyang Li, Liqiang Jing, Kaiyu He, Peilin Wu, Juntong Ni, George Michalopoulos, Yue Zhang, Ziyang Zhang, Mian Zhang, Zhiyu Chen, Xinya Du
| Challenge: | Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery, but their capability in reproducing code from research papers remains underexplored. |
| Approach: | They propose to evaluate LLM agents' ability to reproduce scientific research papers by analyzing code reproduction tasks from 23 research papers published in top-tier NLP venues. |
| Outcome: | The proposed benchmark systematically evaluates the capability of large language model (LLM) agents on code reproduction from Language Modeling Research. |
AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)
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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu
| Challenge: | Significant concerns emerge when addressing cultural sensitivity and local values. |
| Approach: | They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. |
| Outcome: | The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks. |
Counterfactual Active Learning for Out-of-Distribution Generalization (2023.acl-long)
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| Challenge: | Existing studies on active learning methods focus on the out-of-distribution generalization of out- of-distortion samples. |
| Approach: | They propose a counterfactual active learning approach that empowers active learning with counterfact thinking to bridge the seen samples with unseen cases. |
| Outcome: | The proposed approach outperforms existing active learning methods on public datasets with comparable IID performance. |
A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)
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Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang
| Challenge: | Inductive reasoning is an important task for large language models (LLMs). |
| Approach: | They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation. |
| Outcome: | The proposed method improves inductive reasoning in large language models. |
Can Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models (2024.acl-long)
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| Challenge: | Existing text watermarking technologies lack consistency when texts are translated into different languages. |
| Approach: | They propose a cross-lingual watermark removal attack to bypass watermarking by first obtaining a response from an LLM in a pivot language and then translating it into the target language. |
| Outcome: | The proposed method can remove watermarks without performance loss by obtaining a response from an LLM in a pivot language and then translating it into the target language. |
Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense (2022.emnlp-main)
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| Challenge: | Existing models that understand image and text but also cross-reference in-between are lacking in evaluation data resources. |
| Approach: | They propose a multimodal evaluation pipeline to automatically generate question-answer pairs to test models’ understanding of the visual scene, text, and related knowledge. |
| Outcome: | The proposed model can answer the highly semantic VCR question correctly but fails to answer related visual question (Q2), textual question (q3), and background knowledge question ( Q4) as shallow mappings with language priors and unbalanced utilization of information between modalities. |
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems (2021.naacl-industry)
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Tong Wang, Jiangning Chen, Mohsen Malmir, Shuyan Dong, Xin He, Han Wang, Chengwei Su, Yue Liu, Yang Liu
| Challenge: | In dialog systems, the Natural Language Understanding component makes the interpretation decision before the mentioned entities are resolved. |
| Approach: | They propose to leverage Entity Resolution (ER) features in NLU reranking to learn model weights . they propose a score distribution matching method to ensure the models are calibrated . |
| Outcome: | The proposed approach outperforms the baseline model on multiple domain evaluations. |
IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce (2024.findings-emnlp)
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Wenxuan Ding, Weiqi Wang, Sze Kwok, Minghao Liu, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Junxian He, Yangqiu Song
| Challenge: | Existing approaches that distill intentions from LMs fail to generate meaningful and human-centric intentions applicable in real-world E-commerce contexts. |
| Approach: | They propose a double-task multiple-choice question answering benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce. |
| Outcome: | The proposed benchmark consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline to ensure scalability on large E-commerce platforms. |
Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (2024.acl-long)
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Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu
| Challenge: | Recent studies have revealed that chain-of-thought prompting significantly enhances LLM’s reasoning capabilities, which attracts widespread attention from both academics and industry. |
| Approach: | They propose to summarize advanced methods through a taxonomy that offers novel perspectives. |
| Outcome: | The proposed method delineates the challenges and future directions, thereby shedding light on future research. |
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)
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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si
| Challenge: | Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment. |
| Approach: | They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. |
| Outcome: | The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering. |
EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)
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Xiyuan Zhou, Xinlei Wang, Yirui He, Ruixi Zou, Yang Wu, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao
| Challenge: | Existing benchmarks focus on well-defined or abstract reasoning and fail to capture real-world engineering problems. |
| Approach: | They propose a hierarchical benchmark to evaluate large language models on engineering problems. |
| Outcome: | The proposed model performs well under well-defined conditions and is based on three levels of difficulty and covers diverse engineering subfields. |
UCS-SQL: Uniting Content and Structure for Enhanced Semantic Bridging In Text-to-SQL (2025.findings-acl)
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Zhenhe Wu, Zhongqiu Li, JieZhangChinaTele JieZhangChinaTele, Zhongjiang He, Jian Yang, Yu Zhao, Ruiyu Fang, Bing Wang, Hongyan Xie, Shuangyong Song, Zhoujun Li
| Challenge: | Existing methods overlook the challenge of effectively transforming structure information from NL to SQL. |
| Approach: | They propose a text-to-SQL framework that unites content and structure pipes to bridge the gap between NL and SQL. |
| Outcome: | The proposed framework bridges the gap between natural language questions and SQL by combining content and structure pipes. |
SpiderFlow: Efficient Topology-Aware Scheduling for LLM Training Across Decentralized GPU Clusters (2026.acl-long)
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| Challenge: | Existing approaches to training large language models lack topologyaware task scheduling mechanisms and model parallelization strategies. |
| Approach: | They propose a topology-aware scheduling system specifically designed for decentralized GPU clusters . they propose heuristic methods at the inter-cluster level with ILP-based optimization within clusters. |
| Outcome: | The proposed system reduces job completion time by 1.2-1.3 and improves throughput by 1.12-1.25 . it also reduces scheduling overhead by 20-90 on average compared to state-of-the-art scheduling systems. |
Massive End-to-end Speech Recognition Models with Time Reduction (2024.naacl-long)
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Weiran Wang, Rohit Prabhavalkar, Haozhe Shan, Zhong Meng, Dongseong Hwang, Qiujia Li, Khe Chai Sim, Bo Li, James Qin, Xingyu Cai, Adam Stooke, Chengjian Zheng, Yanzhang He, Tara Sainath, Pedro Moreno Mengibar
| Challenge: | Using the neural architecture of Google’s universal speech model, we reduce the frame rate and speed up training and inference. |
| Approach: | They propose to use the neural architecture of Google’s universal speech model with additional funnel pooling layers to significantly reduce the frame rate and speed up training and inference. |
| Outcome: | The proposed methods work with both connectionist temporal classification (CTC) and RNN-Transducer (RNN-T) and over two domains. |
Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization (2021.emnlp-main)
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| Challenge: | Existing methods for abstractive summarization use encoder-decoder attention, but this leads to incomplete copying. |
| Approach: | They propose a copying scheme that takes advantage of prior copying distributions and explicitly encourages the model to copy the input word that is relevant to the previously copied one. |
| Outcome: | The proposed scheme achieves state-of-the-art on summarization benchmarks . it takes advantage of prior copying distributions and explicitly encourages copying . |
Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence (2025.acl-long)
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Jinghan He, Kuan Zhu, Haiyun Guo, Junfeng Fang, Zhenglin Hua, Yuheng Jia, Ming Tang, Tat-Seng Chua, Jinqiao Wang
| Challenge: | Existing methods focus on alignment training or decoding refinements but address symptoms at the generation stage without probing the underlying causes. |
| Approach: | They propose a training-free approach to mitigate hallucination by enhancing the role of vision-aware attention heads. |
| Outcome: | The proposed method achieves superior performance compared to state-of-the-art approaches in mitigating hallucinations while maintaining high efficiency with negligible additional time overhead. |
Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning (2026.findings-acl)
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| Challenge: | a framework to mitigate spurious optimization signals is proposed for test-time reinforcement learning (TTRL) Reinforcement learning with verifiable rewards (RLVR) is an effective paradigm for improving large language models on structured challenging reasoning tasks. |
| Approach: | They propose a framework to mitigate spurious optimization signals from label noise . they propose to use a frequency-based sampling strategy to exclude ambiguous samples . |
| Outcome: | The proposed framework outperforms existing TTRL baselines on three large language models across multiple mathematical reasoning benchmarks. |
New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs (2026.acl-long)
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Shiyao Cui, QingLin Zhang, Di Wang, Yida Lu, Zhexin Zhang, Jinhua Gao, Jinglin Yang, Min He, Han Qiu, Minlie Huang
| Challenge: | Neologisms can foster new linguistic consensus by stabilizing shared meanings and usage in common communicative norms. |
| Approach: | They propose a taxonomy that captures the origins and consensus-verification criteria of toxic neologisms . they propose 'SeTox' framework that integrates real-time web context for naeologim detection . |
| Outcome: | The proposed framework outperforms large-scale models in detecting neologism toxicity. |
Enhancing Generalization in Natural Language Inference by Syntax (2020.findings-emnlp)
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| Challenge: | Pre-trained language models such as BERT have the state-of-the-art performance on natural language inference (NLI). |
| Approach: | They propose to use dependency trees to enhance generalization of BERT in a natural language inference task by leveraging on a graph convolutional network to represent a syntax-based matching graph with heterogeneous matching patterns. |
| Outcome: | The proposed method makes BERT more robust on syntactic changes. |
Variational Autoregressive Decoder for Neural Response Generation (D18-1)
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| Challenge: | Existing variational Bayesian models generate responses from a single latent variable, which is not sufficient to model high variability in responses. |
| Approach: | They propose a conditional variable auto-encoder that sequentially introduces latent variables to condition the generation of each word in the response sequence. |
| Outcome: | Empirical results show that the proposed model improves on state-of-the-art models on Opensubtitle and Reddit datasets. |
Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models (2025.acl-long)
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Yancheng He, Shilong Li, Jiaheng Liu, Yingshui Tan, Weixun Wang, Hui Huang, Xingyuan Bu, Hangyu Guo, Chengwei Hu, Boren Zheng, Zhuoran Lin, Dekai Sun, Zhicheng Zheng, Wenbo Su, Bo Zheng
| Challenge: | Current frontier models sometimes generate false outputs or answers that are not substantiated by evidence. |
| Approach: | They propose Chinese SimpleQA, a Chinese benchmark to evaluate LLMs' factuality . they focus on Chinese language over 6 major topics with 99 diverse subtopics . |
| Outcome: | The Chinese SimpleQA benchmark evaluates the factuality ability of LLMs . the questions and answers are short and easy-to-evaluate . |
An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text Translation (2024.naacl-long)
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| Challenge: | Existing methods for speech-to-text translation (ST) have achieved impressive supervised and zero-shot performance. |
| Approach: | They propose to use consistency regularization methods to boost end-to-end (E2E) speech-totext translation (ST) by regularizing the intra-modal consistency instead of the modality gap. |
| Outcome: | The proposed training strategies achieve state-of-the-art (SOTA) performance in most translation directions. |
Exploring Layer Activation Dynamic of CoT via Knowledge Probe (2026.acl-long)
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| Challenge: | Chain-of-thought reasoning has emerged as a crucial paradigm for multi-step reasoning tasks. |
| Approach: | They propose a multi-stage probing framework that enforces structured reasoning with three explicit stages: keyword extraction, theorem generation, and computation execution. |
| Outcome: | The proposed framework enforces structured reasoning with three explicit stages: keyword extraction, theorem generation, and computation execution. |
Semantic-Eval : A Semantic Comprehension Evaluation Framework for Large Language Models Generation without Training (2025.acl-long)
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| Challenge: | Large language models (LLMs) have emerged as key drivers of progress in the field of natural language processing. |
| Approach: | They propose a framework that assesses LLM-generated text based on semantic understanding. |
| Outcome: | The proposed framework surpasses traditional evaluation metrics and lags behind GPT-4. |
P3LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training (2022.findings-emnlp)
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| Challenge: | Existing autoregressive left-to-right (L2R) models are limited to unidirectional information and constrained on strong local dependencies. |
| Approach: | They propose a probabilistically permuted prophet language model which strengthens the modeling of bidirectional information and long token dependencies for sequence generation. |
| Outcome: | Experiments on GLGE dataset show that P3LM improves on natural language generation tasks. |
BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses (2024.lrec-main)
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| Challenge: | Existing pre-trained language models lack diversity and linguistic challenges in task-oriented dialogues. |
| Approach: | They propose a self-bootstrapping dialogue pre-training model called BootTOD . it learns task-oriented dialogue representations via a framework . |
| Outcome: | The proposed model outperforms strong TOD baselines on diverse dialogue tasks. |
Light Up the Shadows: Enhance Long-Tailed Entity Grounding with Concept-Guided Vision-Language Models (2024.findings-acl)
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| Challenge: | Multi-Modal Knowledge Graphs (MMKGs) are knowledge graphs that integrate and align information from diverse modalities (e.g., text and images). |
| Approach: | They propose a framework that integrates image-text pairs of long-tailed entities and a concept guidance module that offers explainability and enables human verification. |
| Outcome: | The proposed framework improves the accuracy of recognizing long-tailed image-text pairs compared to baselines and also offers flexibility and explainability. |
LMGQS: A Large-scale Dataset for Query-focused Summarization (2023.findings-emnlp)
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Ruochen Xu, Song Wang, Yang Liu, Shuohang Wang, Yichong Xu, Dan Iter, Pengcheng He, Chenguang Zhu, Michael Zeng
| Challenge: | Lack of large-scale datasets for query-focused summarization hinders model development . lack of data limits the ability of QFS models to train robust neural models . |
| Approach: | They propose to generate a query for each summary sentence in a generic summarization annotation using a pretrained language model. |
| Outcome: | The proposed model achieves state-of-the-art zero-shot and supervised performance on multiple existing QFS benchmarks. |
CryptoTrade: A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have been used for financial decision-making and stock market prediction for years. |
| Approach: | They propose to use Large Language Models to analyze on-chain and off-chain data to provide a comprehensive overview of the cryptocurrency market. |
| Outcome: | The proposed trading agent leverages the transparency and immutability of on-chain data, as well as the timeliness and influence of off-chain signals, providing a comprehensive overview of the cryptocurrency market. |
Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond (2023.findings-emnlp)
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Zhecan Wang, Long Chen, Haoxuan You, Keyang Xu, Yicheng He, Wenhao Li, Noel Codella, Kai-Wei Chang, Shih-Fu Chang
| Challenge: | Existing studies have examined dataset biases in VQA benchmarks with short-phrase answers Multiple-choice Question with the LONG Answers (VCR, VLEP, etc.) |
| Approach: | They propose to use Adversarial Data Synthesis (ADS) to generate synthetic training and debiased evaluation data and introduce Intra-sample Counterfactual Training (ICT) to assist models in utilizing synthesized training data. |
| Outcome: | The proposed approach improves model performance even in domain-shifted scenarios. |
LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking (2022.naacl-main)
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| Challenge: | Existing methods exploit the utterances of all dialogue turns to assign value to slots . this can lead to suboptimal results due to information introduced from irrelevant utterrances . |
| Approach: | They propose a SLot-TUrN Alignment enhanced approach to assign slot value . they explicitly align each slot with its most relevant utterance and then predict the corresponding value based on this aligned utteration. |
| Outcome: | The proposed approach achieves state-of-the-art on three multi-domain task-oriented dialogue datasets. |
MicroEdit: Neuron-level Knowledge Disentanglement and Localization in Lifelong Model Editing (2025.emnlp-main)
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| Challenge: | Existing methods for enhancing large language models are designed for single or limited edits, lacking the capacity to support long-term, multi-round knowledge updates. |
| Approach: | They propose a neuron-level editing method that performs minimal interventions within large language models (LLMs) by leveraging a sparse autoencoder, MicroEdit disentangles knowledge representations and activates only a minimal set of necessary neurons for precise parameter updates. |
| Outcome: | Extensive experiments show that MicroEdit outperforms prior methods and robustly handles lifelong knowledge editing across QA and Hallucination settings on LLaM and Mistral. |
MRT: Multi-modal Short- and Long-range Temporal Convolutional Network for Time-sync Comment Video Behavior Prediction (2024.lrec-main)
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| Challenge: | Using time-sync comments, it is difficult to understand user behavior due to complexity of interactions between users, videos, and comments. |
| Approach: | They propose a novel time-sync comment behavior prediction model that takes historical behavior into account and optimizes it on the basis of user preferences. |
| Outcome: | The proposed model improves the performance of time-sync comments on visual frames and textual comments on two cats playing simultaneously. |
SAIR-Comb : A Structure-Aware Iterative Refinement Framework for Combinatorics Autoformalization (2026.acl-long)
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| Challenge: | Large language models (LLMs) have catalyzed advances in mathematical reasoning, propelling the development of automated theorem proving (ATP). |
| Approach: | They propose a Structure-Aware Iterative Refinement framework for Combinatorics powered by Lean 4 and LLMs. |
| Outcome: | The proposed framework achieves strong performance on the specialized CombiBench while remaining highly competitive on general-domain benchmarks. |
TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack (2022.findings-emnlp)
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| Challenge: | Existing textual adversarial attacks use gradient or prediction confidence to generate adversarials, making it hard to be deployed in real-world applications. |
| Approach: | They propose a textual adversarial attack that randomly perturbs lots of words to craft an adversarial example. |
| Outcome: | The proposed attack outperforms existing hard-label attacks in terms of attack performance and adversary quality. |
FiDeLiS: Faithful Reasoning in Large Language Models for Knowledge Graph Question Answering (2025.findings-acl)
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| Challenge: | Existing retrieval-based or agent-based methods are prone to generating erroneous or hallucinated outputs. |
| Approach: | They propose a framework to leverage knowledge graphs as external knowledge sources to improve the factuality of LLM responses by anchoring answers to verifiable reasoning steps retrieved from KGs. |
| Outcome: | The proposed framework improves factuality and interpretability across benchmarks and reduces computational costs. |
A Reward-Guided Dual-Phase Framework for Adaptive Inference-Time Reasoning (2026.findings-acl)
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Yingqian Cui, Zhenwei Dai, Pengfei He, Bing He, Hui Liu, Zhan Shi, Xianfeng Tang, Jingying Zeng, Suhang Wang, Yue Xing, Jiliang Tang, Benoit Dumoulin
| Challenge: | Large Language Models (LLMs) have made strong progress in reasoning. |
| Approach: | They propose a dual-phase test-time scaling framework that separates planning and execution and performs search over each phase independently. |
| Outcome: | Experiments on math reasoning and code generation benchmarks show that the proposed approach improves accuracy while reducing redundant computation. |
CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs (2021.emnlp-main)
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| Challenge: | Existing conversational recommender systems (CRS) do not track the deep shift of user interest in conversations due to the complex of high-order and incomplete paths. |
| Approach: | They propose a conversational context-based reinforcement learning model which does explicit multi-hop reasoning on KGs with a contextual context-driven reinforcement learning framework. |
| Outcome: | Extensive experiments show that CRFR improves on paths of interest shift in knowledge graphs (KGs) . |
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond (2024.findings-acl)
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| Challenge: | Existing task embedding methods rely on fine-tuned, task-specific language models, which hinders their adaptability to prompt-guided Large Language Models (LLMs). |
| Approach: | They propose a framework for unified task embedding that harmonizes task embeds from various models within a single vector space. |
| Outcome: | The proposed framework harmonizes task embeddings from various models within a single vector space. |
ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research (2025.acl-industry)
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| Challenge: | Large Language Models (LLMs) have shown promising results in various domains, but their practical application in industry-relevant operations research presents significant challenges and opportunities. |
| Approach: | They propose a cognitive-inspired framework that enhances optimization through counterfactual reasoning . they use a workflow that transforms requirements into mathematical models and executable solver code . |
| Outcome: | Experiments show that ORMind outperforms existing methods in the NL4Opt dataset and ComplexOR dataset. |
IDEA: An Interpretable and Editable Decision-Making Framework for LLMs via Verbal-to-Numeric Calibration (2026.findings-acl)
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| Challenge: | Existing approaches fail to integrate domain expert insights beyond simple prompting. |
| Approach: | They propose a framework that extracts LLM decision knowledge into an interpretable parametric model over semantically meaningful factors. |
| Outcome: | Experiments show that IDEA outperforms DeepSeek R1 and GPT-5.2 in accuracy and accuracy. |
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL (2022.emnlp-main)
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Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, Zhouhan Lin
| Challenge: | Experimental results show RASAT can leverage a variety of relational structures while inheriting the pretrained parameters from the T5 model. |
| Approach: | They propose a Transformer seq2seq architecture augmented with relation-aware self-attention that leverages relational structures while inheriting pretrained parameters from the T5 model. |
| Outcome: | The proposed model can leverage relational structures while inheriting pretrained parameters from the T5 model effectively. |
OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)
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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou
| Challenge: | a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say . |
| Approach: | They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible . |
| Outcome: | The proposed framework achieves state-of-the-art performance among open-source projects. |
KEPL: Knowledge Enhanced Prompt Learning for Chinese Hypernym-Hyponym Extraction (2023.emnlp-main)
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| Challenge: | Existing work on hypernym-hyponym (“is-a”) relations is mostly in the English language. |
| Approach: | They propose a Knowledge Enhanced Prompt Learning method for Chinese hypernym-hyponym relation extraction using Hearst-like patterns as the prior knowledge. |
| Outcome: | The proposed method is able to extract hypernym-hyponym relations from Chinese unstructured texts using Hearst-like patterns and embed patterns and text simultaneously. |
Global and Local Hierarchical Prompt Tuning Framework for Multi-level Implicit Discourse Relation Recognition (2024.lrec-main)
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| Challenge: | Recent methods to recognize hierarchical discourse relations without explicit connectives are inefficient and ignore the utilization of the output probability distribution information of the verbalizer. |
| Approach: | They propose a global and local hierarchical prompt tuning framework which leverages top-up propagated probability as the global hierarchy to inject it into multi-level verbalizer. |
| Outcome: | The proposed framework achieves competitive results on two benchmacks. |
K-GIP: Diagnosing Logical Fractures in Large Vision-Language Models via Verification Scene Graphs and Sequential Pruning (2026.findings-acl)
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| Challenge: | Existing benchmarks that treat hallucinations as isolated errors neglect causal dependencies between visual perception and textual reasoning. |
| Approach: | They propose a Knowledge-Guided In-Context Probing framework that constructs a dual-perception ground truth to transform abstract priors into multi-granularity queries. |
| Outcome: | The proposed framework isolates deep reasoning failures from simple perceptual misses. |
LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios (2025.acl-long)
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| Challenge: | Existing benchmarks rarely focus on instruction-following in long-context scenarios or stability on different inputs. |
| Approach: | They propose a scalable dataset to evaluate LLMs’ instruction-following capabilities and stability across long contexts. |
| Outcome: | The proposed method evaluates LLMs’ instruction-following capabilities and stability across long contexts. |
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate (2024.emnlp-main)
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Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Shuming Shi, Zhaopeng Tu
| Challenge: | Modern large language models (LLMs) have shown remarkable performance on general language tasks but struggle on complex reasoning tasks. |
| Approach: | They propose a multi-agent debate framework that encourages divergent thinking in LLMs . they propose to break debate and use a judge to obtain a final solution . |
| Outcome: | The proposed framework encourages divergent thinking in large language models . it is able to generate novel thoughts even if initial stance is incorrect . |
LayAlign: Enhancing Multilingual Reasoning in Large Language Models via Layer-Wise Adaptive Fusion and Alignment Strategy (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) are pretrained on multilingual corpora but exhibit suboptimal performance on low-resource languages. |
| Approach: | They propose a framework that integrates representations from all encoder layers and an adaptive fusion-enhanced attention mechanism to enable layer-wise interaction between the LLM and the multilingual encoder. |
| Outcome: | Experiments on multilingual reasoning tasks show that the proposed framework outperforms baselines. |
APEX: Learning Adaptive Priorities for Multi-Objective Alignment in Vision-Language Generation (2026.findings-acl)
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Dongliang Chen, Xinlin Zhuang, Junjie Xu, Luojian Xie, Zehui Wang, Jiaxi Zhuang, Haolin Yang, Liang Dou, Xiao He, Xingjiao Wu, Ying Qian
| Challenge: | APEX optimizes for text-to-image generation by combining learning potential, conflict penalty, and progress need. |
| Approach: | They propose an algorithm that stabilizes heterogeneous rewards and dynamically schedules objectives . they propose a method that achieves better Pareto trade-offs across four heterogenous objectives based on P3 Adaptive Priorities . |
| Outcome: | The proposed algorithm achieves better pareto trade-offs across four heterogeneous objectives while maintaining competitive OCR accuracy. |
Scaling Laws Across Model Architectures: A Comparative Analysis of Dense and MoE Models in Large Language Models (2024.emnlp-main)
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| Challenge: | a study of large language models (LLMs) reveals the transferability and discrepancies of scaling laws between Dense and MoE models. |
| Approach: | They investigate the transferability and discrepancies of scaling laws between Dense Models and Mixture of Experts models. |
| Outcome: | The results show that the power-law scaling framework also applies to MoE Models . |
MAXS: Meta-Adaptive Exploration with LLM Agents (2026.findings-acl)
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Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Yu He, Haoran Luo, li Yuan, Lingling Zhang, Rui Mao, Qika Lin, Jun Liu
| Challenge: | Existing methods for inference are often myopic and have divergent reasoning paths . a meta-adaptive reasoning framework is proposed to improve the efficiency of LLM agents . |
| Approach: | They propose a meta-adaptive reasoning framework that integrates tool execution and reasoning planning. |
| Outcome: | The proposed framework outperforms existing methods in performance and inference efficiency. |
XLM-D: Decorate Cross-lingual Pre-training Model as Non-Autoregressive Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing pre-training language models have been successful in natural language understanding and autoregressive generation tasks, but non-autoregressive models have not been sufficiently successful. |
| Approach: | They propose a pre-trained masked language model (MLM) and a non-autoregressive generation model with a lightweight decorator. |
| Outcome: | The proposed model outperforms the previous mask-predict model on translation datasets by 19.9x. |
Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning (2024.findings-emnlp)
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| Challenge: | Fine-tuning and in-context learning are two prevalent methods in imbuing large language models with task-specific knowledge. |
| Approach: | They propose to use a circuit shift theory to explain why in-context learning is superior to fine-tuning for tasks with implicit patterns. |
| Outcome: | The proposed method can grasp deep patterns and significantly improve accuracy on implicit patterns, compared with fine-tuning and in-context learning. |
Instance-Level Dynamic LoRAs Composition for Cross-Task Generalization (2024.findings-emnlp)
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| Challenge: | Large language models perform well on tasks that have undergone fine-tuning of instructions, but performance on completely unseen tasks is often less than ideal. |
| Approach: | They propose a task-level LoRAs combination which learns the LoRA modules combination weights based on a small number of samples to form the task model. |
| Outcome: | The proposed method outperforms the typical method, LoraHub, on 16 out of 27 tasks. |
SecDecoding: Steerable Decoding for Safer LLM Generation (2025.findings-emnlp)
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| Challenge: | Existing decoding-time defense methods suffer from limited generalization, high computational overhead, or significant utility degradation. |
| Approach: | They propose a decoding-time defense framework that leverages a pair of small contrastive models to estimate token-level safety signals by measuring divergence in their output distributions. |
| Outcome: | The proposed framework achieves near-zero attack success rates against a wide spectrum of advanced jailbreak attacks while maintaining the model’s helpfulness with minimal degradation. |
Refining and Synthesis: A Simple yet Effective Data Augmentation Framework for Cross-Domain Aspect-based Sentiment Analysis (2024.findings-acl)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) data augmentation has attracted increasing attention in recent years due to data sparsity. |
| Approach: | They propose a framework to augment ABSA data using pseudo labels for target domain . they refine generated labeled data using a natural language inference filter . |
| Outcome: | The proposed framework outperforms 7 strong baselines on 4 kinds of ABSA tasks. |
Guiding Dialogue Agents to Complex Semantic Targets by Dynamically Completing Knowledge Graph (2023.findings-acl)
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| Challenge: | Existing knowledge graphs are incomplete in tracking complex semantic relations of the target-oriented dialogue. |
| Approach: | They combine methods of knowledge retrieval and relationship prediction to construct a context-related dynamic KG and a metric to evaluate the tracked path automatically. |
| Outcome: | The proposed method can control the agent more logically and smoothly toward the complex target. |
M2RC-EVAL: Massively Multilingual Repository-level Code Completion Evaluation (2025.acl-long)
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Jiaheng Liu, Ken Deng, Congnan Liu, Jian Yang, Shukai Liu, He Zhu, Peng Zhao, Linzheng Chai, Yanan Wu, JinKe JinKe, Ge Zhang, Zekun Moore Wang, Guoan Zhang, Yingshui Tan, Bangyu Xiang, Zhaoxiang Zhang, Wenbo Su, Bo Zheng
| Challenge: | Existing repository-level code completion benchmarks focus on a limited number of languages . existing benchmarks report overall average scores of different languages ignoring fine-grained abilities . |
| Approach: | They propose to use repository-level code completion benchmarks to evaluate general code intelligence abilities across languages for existing code Large Language Models. |
| Outcome: | The proposed benchmarks improve the code completion abilities of existing LLMs by using two types of annotations on the parsed syntax tree. |
CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models (2025.naacl-long)
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Ying Nie, Binwei Yan, Tianyu Guo, Hao Liu, Haoyu Wang, Wei He, Binfan Zheng, Weihao Wang, Qiang Li, Weijian Sun, Yunhe Wang, Dacheng Tao
| Challenge: | Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging task like finance, has not been fully explored. |
| Approach: | They propose a benchmark to assess the financial knowledge of large language models (LLMs) in China. |
| Outcome: | The proposed benchmark is the most comprehensive evaluation benchmark to date for LLMs in finance. |
Router-Tuning: A Simple and Effective Approach for Dynamic Depth (2025.emnlp-main)
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| Challenge: | Existing methods to improve computational efficiency are under-explored and face several critical challenges. |
| Approach: | They propose a method that selectively activates only a subset of the model's layers, skipping those deemed less important. |
| Outcome: | The proposed method significantly improves performance on Attention layers and MoE layers while reducing redundant computation and memory usage. |
Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation (2022.coling-1)
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| Challenge: | Existing methods for OOD detection are overconfident for OD samples . lack of labeled OOD examples leads to poor prior knowledge about these unknown intents, making it challenging to detect OOD samples. |
| Approach: | They propose a Bayesian OOD detection framework to calibrate distribution uncertainty using Monte-Carlo Dropout. |
| Outcome: | The proposed framework gains 33.33% OOD F1 improvements with increasing only 0.41% inference time compared to previous methods. |
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable (2020.acl-main)
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| Challenge: | Existing pre-training models for dialogue generation have been proven effective for a wide range of tasks. |
| Approach: | They propose a dialogue generation pre-training framework that leverages bi-directional context and uni-directional characteristic of language generation. |
| Outcome: | The proposed framework is superior to existing models on three publicly available datasets. |
AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation (2024.findings-emnlp)
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Zhitao He, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Jiexin Xu, Huaijun Li, Kang Liu, Jun Zhao
| Challenge: | Recent advances in deep learning have significantly impacted the legal domain. |
| Approach: | They propose a multi-agent framework for judicial decision-making that simulates the court trial process . they propose 420 Chinese judgment documents to support their framework and build a large-scale legal knowledge base . |
| Outcome: | The proposed framework outperforms existing methods in various aspects, especially in generating legal articles. |
AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following (2026.acl-long)
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Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuanhao Xiong, Nanshu Wang, Xiaoliang Peng, Beibin Li, Shengjie Bi, Shishir G Patil, Qi Qi, Shengyu Feng, Julian Katz-Samuels, Richard Yuanzhe Pang, Sujan Kumar Gonugondla, Hunter Lang, Yue Yu, Yundi Qian, Maryam Fazel-Zarandi, Licheng Yu, Amine Benhalloum, Hany Hassan Awadalla, Manaal Faruqui
| Challenge: | Recent advances in large language models (LLMs) have shown impressive performance on a range of tasks, yet advanced instruction following (IF) remains a significant challenge. |
| Approach: | They propose a benchmark that features over 1,600 prompts and expert-curated rubrics that assess LLMs’ ability to follow complex, multi-turn, and system-level instructions. |
| Outcome: | The proposed framework improves instruction-following abilities of large language models, achieving a 6.7% gain on AdvancedIF and strong results on public benchmarks. |
Your Inference Request Will Become a Black Box: Confidential Inference for Cloud-based Large Language Models (2026.acl-long)
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Chung-ju Huang, Huiqiang Zhao, Yuanpeng He, Lijian Li, Wenpin Jiao, Zhi Jin, Peixuan Chen, Leye Wang
| Challenge: | Existing approaches fail to ensure privacy, maintain model performance, and preserve computational efficiency simultaneously. |
| Approach: | They propose a confidential inference framework that partitions the LLM pipeline between a client-verified Confidential Virtual Machine (CVM) and the public cloud to protect client data without compromising the cloud’s model intellectual property or inference quality. |
| Outcome: | The proposed framework can defend against state-of-the-art token inference attacks while preserving model privacy, performance, and efficiency. |
Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations (2026.findings-acl)
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Haitong Luo, Fali Wang, Weiyao Zhang, Xianren Zhang, Zhiwei Zhang, Tianxiang Zhao, Minhua Lin, Jiahao Zhang, Hui Liu, Xianfeng Tang, Qi He, Suhang Wang, Xuying Meng, Yujun Zhang
| Challenge: | Large language models exhibit intrinsic limitations such as knowledge cutoff, single-threaded reasoning that hinders finer-grained branch and aggregation, and rigid collaboration mechanisms that struggle to coordinate specialized capabilities. |
| Approach: | They propose a taxonomy spanning *Graph-Assisted Knowledge Augmentation*, *Graph Assisted Reasoning and Planning*, and *Graphed LLM Collaboration*. |
| Outcome: | The proposed models show that graphs can augment and correct LLMs and support dynamic coordination among experts and agents in collaborative settings. |
AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering (2026.acl-long)
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Taolin Zhang, Dongyang Li, Chen Chen, Qizhou Chen, Jiuheng Wan, Xiaofeng He, Chengyu Wang, Richang Hong
| Challenge: | Empirical results show that AMATA outperforms baseline approaches, knowledge-augmented frameworks, and LLMs on knowledge-intensive QA benchmarks. |
| Approach: | They propose an Adaptive Multi-Agent Trajectory Alignment framework that integrates external knowledge to improve response interpretability and factual grounding. |
| Outcome: | The proposed framework outperforms baseline approaches, knowledge-augmented frameworks, and LLM-based trajectory systems on five established knowledge-intensive QA benchmarks. |
SLICEFORMER: Static Program Slicing Using Language Models With Dataflow-Aware Pretraining and Constrained Decoding (2026.acl-long)
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| Challenge: | Static program slicing is a software engineering technique for isolating code relevant to specific variables. |
| Approach: | They propose a new approach that reformulates static program slicing as a sequence-to-sequence task using small language models such as CodeT5+. |
| Outcome: | The proposed approach improves on Java and Python program slicing benchmarks with up to 22% gain in ExactMatch. |
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models (2024.lrec-main)
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Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Ece Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan C. Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea
| Challenge: | Recent advances in large language models have led to misleading public discourse that “it’s all been solved.” |
| Approach: | They identify 14 research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. |
| Outcome: | The research areas identified are 45 research directions that require new research and are not directly solvable by LLMs. |
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases (2020.acl-main)
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| Challenge: | Existing models for keyphrase generation do not provide a desideratum for the number of keyphrases in texts. |
| Approach: | They propose a recurrent generative model that generates multiple keyphrases as delimiter-separated sequences. |
| Outcome: | The proposed model outperforms baseline models on all datasets. |
Rethinking Word-level Adversarial Attack: The Trade-off between Efficiency, Effectiveness, and Imperceptibility (2024.lrec-main)
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| Challenge: | Neural language models have demonstrated impressive performance but remain vulnerable to word-level adversarial attacks. |
| Approach: | They propose two standardized search spaces to address the problem of word-level adversarial attacks. |
| Outcome: | The proposed search spaces improve performance and trade-offs in different scenarios. |
Beyond Correctness: Confidence-Aware Reward Modeling for Enhancing Large Language Model Reasoning (2025.emnlp-main)
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| Challenge: | Recent advances in large language models have shifted the post-training paradigm from instruction tuning and human preference alignment to reinforcement learning (RL) based on rule-based evaluations of answer correctness, these models often receive rewards for speculative answers without generating coherent reasoning chains. |
| Approach: | They propose a confidence-based reward model tailored for enhancing STEM reasoning capabilities. |
| Outcome: | The proposed model outperforms state-of-the-art open-source reward models across diverse STEM benchmarks. |
LLM-KT: Enhancing Large Language Models with Knowledge Tracing via Multi-Level Plug-and-Play Alignment (2026.findings-acl)
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| Challenge: | Existing methods to learn behavioral sequences fail to capture complex behavioral patterns due to a lack of deep reasoning capabilities and world knowledge. |
| Approach: | They propose a framework that integrates the reasoning power of Large Language Models with the sequential modeling strengths of traditional KT methods via multi-level plug-and-play alignment. |
| Outcome: | Extensive experiments on four standard datasets show that the proposed framework outperforms existing methods on state-of-the-art questions. |
Aligning Recommendation and Conversation via Dual Imitation (2022.emnlp-main)
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| Challenge: | Existing conversational recommendation systems ignore the advantage of user interest shift in connecting recommendation and conversation, leading to an ineffective loose coupling structure. |
| Approach: | They propose a dual imitation to explicitly align recommendation and conversation paths . they propose to generate high-quality responses with accurate recommendations and coherent explanations . |
| Outcome: | The proposed model outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics. |
Hierarchical Visual Agent: Managing Contexts in Joint Image-Text Space for Advanced Chart Reasoning (2026.findings-acl)
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| Challenge: | Existing MLLMs are strong at understanding single plots, but struggle with multi-step reasoning . Existing approaches to manage context in chart reasoning include text-based chain-of-thought prompting . |
| Approach: | They propose a hierarchical visual agent framework that iteratively constructs a working context in an image–text space. |
| Outcome: | The proposed framework improves on strong multimodal baselines. |
Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs (2026.findings-acl)
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Zhiyuan Hu, Yucheng Wang, Yufei He, Jiaying Wu, Yilun Zhao, See-Kiong Ng, Cynthia Breazeal, Anh Tuan Luu, Hae Won Park, Bryan Hooi
| Challenge: | Reinforcement learning (RL) is a paradigm for post-training large language models, but it suffers from exploration collapse . a new study finds that RL fails to reward correct solutions that exhibit rare high-level strategies . |
| Approach: | They propose a method that rewards correct solutions that exhibit rare high-level strategies by clustering rollouts according to their high- level solution strategies. |
| Outcome: | The proposed approach improves pass@k across large sampling budgets and increases area under the pass@K curve (AUC@K) without sacrificing pass@1. |
Graph-Based Chain-of-Thought Pruning for Reducing Redundant Reflections in Reasoning LLMs (2026.findings-acl)
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Hongyuan Yuan, Xinran He, Run Shao, Bolei He, Xianwei Xue, Mengke Chen, Qiutong Pan, Haiwei Wang, Haifeng Li
| Challenge: | Extending CoT through RL can induce undesirable thinking patterns such as overthinking . prior work has focused on inefficient reflection, which manifests in two problematic patterns: Indiscriminate Reflection and Repetitive Reflectione . |
| Approach: | They propose a graph-based approach to optimize CoT by pruning each linear CoT into a directed acyclic graph with explicit dependency edges. |
| Outcome: | The proposed approach reduces the average reasoning tokens by 42% while maintaining or improving accuracy. |
Evaluating and Mitigating Object Hallucination in Large Vision-Language Models: Can They Still See Removed Objects? (2025.naacl-long)
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| Challenge: | LVLMs often mistakenly determine objects as present in images where they do not exist . authors propose a new benchmark to evaluate object hallucinations by removing objects from images and asking the model whether it can still see the removed objects. |
| Approach: | They propose a benchmark to evaluate object hallucinations by removing objects from images . they propose oDPO, a direct preference optimization objective based on visual objects . |
| Outcome: | The proposed benchmark reduces the likelihood of object hallucinations by removing objects from images and asking the model whether it can still see the removed objects. |
Large Language Models are Complex Table Parsers (2023.emnlp-main)
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| Challenge: | Extensive experiments and results on Complex Table QA datasets, i.e., the open-domain dataset HiTAB and the aviation domain dataset AIT-QA show that our approach significantly outperforms previous work on both datasets. |
| Approach: | They propose to incorporate Generative Pre-trained Transformer 3.5 to address the specific challenges posed by Complex Table QA by reconstructing tables into tuples and using prompt templates to create dialogues. |
| Outcome: | The proposed approach outperforms previous work on complex table parsing datasets and leads to state-of-the-art (SOTA) performance. |
TUBA: Cross-Lingual Transferability of Backdoor Attacks in LLMs with Instruction Tuning (2025.findings-acl)
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Xuanli He, Jun Wang, Qiongkai Xu, Pasquale Minervini, Pontus Stenetorp, Benjamin I. P. Rubinstein, Trevor Cohn
| Challenge: | Despite the increasing support for multilingual capabilities, the impact of backdoor attacks on LLMs remains under-explored. |
| Approach: | They propose to use poisoned instructiontuning data to attack multilingual LLMs . their results show that more powerful models show increased susceptibility to transferable cross-lingual backdoor attacks . |
| Outcome: | The proposed attack is effective in models like BLOOM and GPT-4o with high success rates in more than 7 out of 12 languages. |
Improving Entity Linking through Semantic Reinforced Entity Embeddings (2020.acl-main)
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| Challenge: | Existing entity embeddings are effective, but too distinctive for linking models to learn contextual commonality. |
| Approach: | They propose a method to inject fine-grained semantic information into entity embeddings . they use word embedds of type words to generate semantic embeddngs based on existing embeddables a sample of semantic information is injected into the embedded entities . |
| Outcome: | The proposed method reduces the distinctiveness of existing embeddings and improves performance. |
Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models (2025.findings-naacl)
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| Challenge: | Existing defense methods struggle with two key issues: inadequate defense capabilities and over-defensiveness. |
| Approach: | They propose a multi-agents-based framework that leverages accurate external information to provide an unbiased summary of user intentions and safety response guidance. |
| Outcome: | Experiments on popular jailbreak attacks and benign datasets show that the proposed framework can enhance LLM's robustness against jailbreaks without compromising its general functionality. |
Route Sparse Autoencoder to Interpret Large Language Models (2025.emnlp-main)
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| Challenge: | Sparse autoencoders (SAEs) extract interpretable and monosemantic features in large language models . prior work focused on feature extraction from a single layer, failing to capture activations that span multiple layers. |
| Approach: | They propose a framework that integrates a routing mechanism with a shared SAE to efficiently extract features from multiple layers. |
| Outcome: | The proposed framework extracts features from multiple layers while incurring minimal parameter overhead while achieving high interpretability and flexibility. |
Spectral Disentanglement: Rank-Aware Task Adaptation for Rehearsal-free Continual Learning in LLMs (2026.acl-long)
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| Challenge: | Continual Learning (CL) for Large Language Models faces a fundamental Stability-Plasticity Dilemma . Rank-Blindness enforces a single rank constraint across diverse tasks, leading to catastrophic forgetting of earlier tasks and underfitting on complex new ones. |
| Approach: | They propose a rank-spectrum-based rehearsal-free framework that explicitly disentangles knowledge into two orthogonal subspaces. |
| Outcome: | The proposed framework achieves a superior stability-plasticity balance compared to single-rank baselines. |
Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment (P19-1)
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| Challenge: | Existing approaches to generate informative responses based on external knowledge are limited to singleround settings. |
| Approach: | They propose a framework for multi-turn conversations with two dialogue agents . they propose to evaluate dialogues on informativeness and coherence . |
| Outcome: | The proposed framework outperforms state-of-the-art approaches significantly on the publicly available dataset. |
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)
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Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Shan Xia, Hongfei Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, Jennifer Neville
| Challenge: | Traditional alignment methods rely on human annotations and are subjective and misalignment with real-world user preferences. |
| Approach: | They propose a framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically. |
| Outcome: | The proposed framework identifies and classifies user feedback to LLM responses between conversation turns and creates examples of preferred and dispreferred responses according to user preferences. |
Opinions Are Not Always Positive: Debiasing Opinion Summarization with Model-Specific and Model-Agnostic Methods (2024.lrec-main)
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| Challenge: | Existing opinion summarization frameworks are reluctant to generate negative summaries given input of negative opinions. |
| Approach: | They propose to disentangle input into sentiment-relevant and sentiment-irrelevant components through adversarial loss. |
| Outcome: | The proposed approaches reduce sentiment bias in the existing opinion summarization dataset . the proposed approaches generate better summaries with a more balanced emotional polarity distribution . |
A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)
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Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang
| Challenge: | Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows . |
| Approach: | They propose a repository-level evaluation benchmark to assess security of AI-generated code. |
| Outcome: | The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation. |
Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting (2023.findings-acl)
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| Challenge: | Existing approaches to slot filling only learn surface mapping of slot types between D S and D T and get poor generalization capability or robustness. |
| Approach: | They propose a generative zero-shot prompt learning framework for cross-domain slot filling which improves generalization and robustness than previous work. |
| Outcome: | The proposed framework improves generalization and robustness on unseen slots and an efficient prompt tuning strategy boosts performance. |
BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)
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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang
| Challenge: | Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS). |
| Approach: | They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features. |
| Outcome: | The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization. |
A Comparative Study of Explicit and Implicit Gender Biases in Large Language Models via Self-evaluation (2024.lrec-main)
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| Challenge: | Existing studies on the explicit and implicit biases in large language models (LLMs) focus on either explicit or implicit bias. |
| Approach: | They propose a self-evaluation-based two-stage measurement of explicit and implicit biases within large language models grounded in social psychology. |
| Outcome: | The proposed model is based on two stages of self-evaluation on state-of-the-art LLMs to measure explicit bias toward social targets, where bias is less likely to be self-recognized by the LLM. |
Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling (2023.acl-long)
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| Challenge: | Existing methods to retrieve knowledge-intensive conversations are based on external resources such as Wikipedia databases or search engine results. |
| Approach: | They propose an unsupervised query enhanced approach for knowledge-intensive conversations . they conduct experiments on three knowledge- intensive conversation datasets . |
| Outcome: | The proposed approach performs better than all unsupervised methods across three datasets and achieves competitive performance compared to supervised methods. |
SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation (2024.naacl-long)
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Abe Hou, Jingyu Zhang, Tianxing He, Yichen Wang, Yung-Sung Chuang, Hongwei Wang, Lingfeng Shen, Benjamin Van Durme, Daniel Khashabi, Yulia Tsvetkov
| Challenge: | Existing watermarked generation algorithms employ token-level designs and are vulnerable to paraphrase attacks. |
| Approach: | They propose a sentence-level watermarking algorithm that uses locality-sensitive hashing to partition the semantic space of sentences. |
| Outcome: | The proposed algorithm is more robust than the existing state-of-the-art method on paraphrasers and domains, while posing only minor degradations to SemStamp. |
Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning? (2025.acl-long)
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Yancheng He, Shilong Li, Jiaheng Liu, Weixun Wang, Xingyuan Bu, Ge Zhang, Z.y. Peng, Zhaoxiang Zhang, Zhicheng Zheng, Wenbo Su, Bo Zheng
| Challenge: | Recent advances in o1-like models have generated long Chain-of-Thought reasoning steps to improve the reasoning abilities of existing Large Language Models (LLMs). |
| Approach: | They propose a DeltaBench to analyze the quality and effectiveness of o1-like models and measure their ability to detect errors in long COT reasoning. |
| Outcome: | The proposed model can detect errors in long COT reasoning. |
EMO-RL: Emotion-Rule-Based Reinforcement Learning Enhanced Audio-Language Model for Generalized Speech Emotion Recognition (2025.findings-emnlp)
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| Challenge: | Recent advances in reinforcement learning (RL) have shown promise in improving LALMs’ reasoning abilities, but their performance in affective computing tasks remains suboptimal. |
| Approach: | They propose a framework incorporating reinforcement learning with two key innovations: Emotion Similarity-Weighted Reward (ESWR) and Explicit Structured Reasoning (ESR). |
| Outcome: | The proposed framework improves LALMs' reasoning abilities on MELD and IEMOCAP datasets and shows strong generalization. |
TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles (2026.acl-long)
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Yirong Zeng, Yufei Liu, Xiao Ding, Yutai Hou, Yuxian Wang, Wu Ning, Haonan Song, Dandan Tu, Qixun Zhang, Yuxiang He, Bibo Cai, Ting Liu
| Challenge: | Instruction Following (IF) is a core capability of LLMs, requiring strict adherence to diverse constraints. |
| Approach: | They propose a framework that uses tiny language models to evaluate instruction following . they propose to use a set of specialized tiny language model to provide rewards for soft constraints. |
| Outcome: | The proposed framework outperforms baseline models by 12% and speeds up training time by 3. |
How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs (2025.findings-acl)
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Guhao Feng, Kai Yang, Yuntian Gu, Xinyue Ai, Shengjie Luo, Jiacheng Sun, Di He, Zhenguo Li, Liwei Wang
| Challenge: | Despite the success of transformer-based large language models, understanding and enhancing their mathematical capabilities remains a significant challenge. |
| Approach: | They propose to use numerical precision as a key factor that influences LLMs' effectiveness in arithmetical tasks to determine their effectiveness. |
| Outcome: | The proposed models perform better in arithmetic tasks than transformer-based models with standard numerical precision. |
PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning (2021.findings-acl)
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| Challenge: | PLATO-2 is a high-quality open-domain chatbot that can generate one-to-many mappings and improve response quality. |
| Approach: | They propose a curriculum learning process to build a high-quality open-domain chatbot . they use a coarse-grained generation model and latent variables to train a generative model . |
| Outcome: | The proposed model improves on Chinese and English data and can generate diverse responses and select the best response. |
Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios (2026.findings-acl)
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Xiaomin Li, Tala Wang, Zichen Zhong, Ying Zhang, Zirui Zheng, Takashi Isobe, Dezhuang Li, Huchuan Lu, You He, Xu Jia
| Challenge: | Existing benchmarks focus on evaluating MLLMs’ pre-existing knowledge or perceptual understanding, often neglecting the critical capability of reasoning. |
| Approach: | They propose a benchmark designed for visual clue-driven reasoning in daily scenarios that combines rigorous grounding in authentic daily activities and challenging query design that necessitates more than surface-level perception. |
| Outcome: | The proposed benchmark identifies visual clues and their ability to provide robust reasoning in daily scenarios. |
General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation (2023.findings-acl)
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| Challenge: | Large distribution shifts among different domains hinder transferability of keyphrase generation models. |
| Approach: | They propose a pipeline which guides KPG models’ learning focus from general syntactical features to domain-related semantics in a data-efficient manner. |
| Outcome: | The proposed pipeline can produce good quality keyphrases in new domains and achieve consistent improvements after adaptation with limited in-domain annotated data. |
Position Paper: Data-Centric AI in the Age of Large Language Models (2024.findings-emnlp)
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Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low
| Challenge: | a paper proposes a data-centric perspective of AI research, focusing on large language models. |
| Approach: | They propose a data-centric viewpoint of AI research, focusing on large language models . they propose four scenarios centered around data, including data curation, attribution, knowledge transfer . |
| Outcome: | The proposed research focuses on large language models with data centric benchmarks . the proposed benchmarks can be used to develop new data curation methods . |
Crab: A Novel Configurable Role-Playing LLM with Assessing Benchmark (2025.acl-long)
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Kai He, Yucheng Huang, Wenqing Wang, Delong Ran, Dongming Sheng, Junxuan Huang, Qika Lin, Jiaxing Xu, Wenqiang Liu, Mengling Feng
| Challenge: | Existing RP-LLMs employ only a single role with numerous dialogues, but Crab enables dynamic configuration of desired roles, thereby enhancing related flexibility and adaptability. |
| Approach: | They propose a Configurable Role-Playing LLM with Assessing Benchmark that combines a Role dataset curation, persona-emodying Llm construction, and comprehensive benchmark creation for RP dialogue generation. |
| Outcome: | The proposed model outperforms existing LLMs in performing fine-grained evaluations of RP while keeping dialogue per role minimal. |
UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understanding (2024.lrec-main)
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| Challenge: | Existing studies rely on shallow unsupervised data generated by token surface matching regardless of global context-aware semantics of the surrounding text tokens. |
| Approach: | They propose an Unsupervised Pseudo Semantic Data Augmentation mechanism to enrich training data without human intervention. |
| Outcome: | The proposed model improves on general zero-shot cross-lingual understanding tasks on different languages without human intervention. |
CoDA: Restoring Contextual Dominance via Copy-Encouraged Attention Intervention for Mitigating RAG Hallucinations (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation reduces hallucination by grounding outputs in external evidence. |
| Approach: | They propose a lightweight inference-time attention intervention that amplifies evidence-aligned value states to enhance contextual faithfulness and reduce hallucination. |
| Outcome: | The proposed model reduces hallucination by grounding model outputs in external evidence. |
Can LLMs Clarify? Investigation and Enhancement of Large Language Models on Argument Claim Optimization (2025.coling-main)
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| Challenge: | While Large Language Models (LLMs) have demonstrated proficiency in text rewriting tasks such as style transfer and query rewrite, their application to claim optimization remains unexplored. |
| Approach: | They propose to use a sliding window mechanism to evaluate the performance of large language models in claim clarification tasks under different settings. |
| Outcome: | The proposed model improves the performance of three LLMs on the claim clarification task under zero-shot, few-shot and supervised fine-tuning settings. |
SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings (P19-1)
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| Challenge: | Lexical relations are relations between terms in lexicons. |
| Approach: | They propose a neural representation learning model to distinguish lexical relations among term pairs based on hyperspherical relation embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models on several benchmarks. |
ContextCheck: Sentence-Level Faithfulness Verification with Context-Aware Disambiguation (2026.findings-acl)
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Yueqin Yin, Yaxi Li, Xin Liu, Xun Wang, Kaiqiang Song, Simin Ma, Shujian Liu, Sathish Reddy Indurthi, Haoyun Deng, Pengcheng He, Mingyuan Zhou, Song Wang
| Challenge: | Large language models often hallucinate, producing content that is factually incorrect or not grounded in the sources. |
| Approach: | They propose a framework for sentence-level faithfulness verification with context-aware disambiguation. |
| Outcome: | The proposed framework improves Macro F1 by over 10 points compared to baselines on three context-dependent datasets. |
CliMedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models in Clinical Scenarios (2024.emnlp-main)
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| Challenge: | Chinese medical large language models (LLMs) are underperforming on this benchmark, especially where medical reasoning and factual consistency are vital. |
| Approach: | They propose a benchmark with 14 expert-guided clinical scenarios to assess the medical ability of large language models across 7 pivot dimensions. |
| Outcome: | The proposed benchmark has been validated in several ways. |
Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model (2024.emnlp-main)
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Wenqi Zhang, Zhenglin Cheng, Yuanyu He, Mengna Wang, Yongliang Shen, Zeqi Tan, Guiyang Hou, Mingqian He, Yanna Ma, Weiming Lu, Yueting Zhuang
| Challenge: | Using large language models, large multimodal models struggle with basic tasks like reading time from a clock and planning a route using a road map. |
| Approach: | They propose a multimodal self-instruct that synthesizes massive abstract images and visual reasoning instructions. |
| Outcome: | The proposed model synthesizes 11,193 abstract images and reasoning instructions across eight visual scenarios. |
Neural Topic Modeling with Bidirectional Adversarial Training (2020.acl-main)
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| Challenge: | Recent studies have shown that neural topic models for automatic topic extraction avoid complicated mathematical derivations for model inference. |
| Approach: | They propose a bidirectional adversarial topic model which uses a generator and an encoder to infer topic distribution. |
| Outcome: | The proposed model outperforms baselines and competitive models in three benchmark corpora. |
One-Dimensional Object Detection for Streaming Text Segmentation of Meeting Dialogue (2025.findings-acl)
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Rui He, Zhongqing Wang, Minjie Qiang, Hongling Wang, Yifan.zhang Yifan.zhang, Hua Xu, Shuai Fan, Guodong Zhou
| Challenge: | Current text segmentation models exhibit numerous limitations, such as imbalances in labels that affect the stability of model training and discrepancies between the model’s training tasks (sentence classification) and the actual text segmenting. |
| Approach: | They implement a sliding window-based segmentation method and employ two different levels of sliding window based balanced label strategies to stabilize the training process of the streaming segmentation model. |
| Outcome: | The proposed method is robust, controllable, and achieves state-of-the-art performance. |
ModularMoE: Fast LLM Customization with Parameter-Sharing Mixture-of-Experts for Low-Resource Settings (2026.findings-acl)
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Jiaxing Liu, Qi Qi, Haifeng Sun, Dunjun Li, Zirui Zhuang, Bo He, Xiang Yang, Cong Liu, Jianxin Liao, Jingyu Wang
| Challenge: | Large Language Models impose significant computational and storage burdens on personal devices . existing customization approaches incur excessive computational costs or lead to suboptimal performance . |
| Approach: | They propose a training framework that converts pre-trained LLMs into parameter-sharing MoE models for lightweight deployment. |
| Outcome: | The proposed training framework outperforms state-of-the-art training frameworks at the same sparsity level while delivering up to 2.71 inference speedup. |