Papers by Cheng Cheng
Enabling Self-Improving Agents to Learn at Test Time With Human-In-The-Loop Guidance (2025.emnlp-industry)
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Yufei He, Ruoyu Li, Alex Chen, Yue Liu, Yulin Chen, Yuan Sui, Cheng Chen, Yi Zhu, Luca Luo, Frank Yang, Bryan Hooi
| Challenge: | Existing large language model (LLM) agents are unable to adapt to changing domain knowledge and rules. |
| Approach: | They propose an LLM agent framework that continuously learns updated domain knowledge at test time. |
| Outcome: | The proposed agent improves on a customer due diligence name screening task on . the agent learns updated domain knowledge at test time. |
Multi-Frequency Contrastive Decoding: Alleviating Hallucinations for Large Vision-Language Models (2025.emnlp-main)
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| Challenge: | Existing studies attribute object hallucinations to linguistic priors and data biases . MFCD method removes hallucinian distribution in the original output distribution . |
| Approach: | They propose a method that removes the hallucination distribution in the original output distribution . they propose MFCD to mitigate hallucinism in large visual-language models . |
| Outcome: | The proposed method reduces hallucination distributions without training or external tools . the proposed method can be applied to various LVLMs without modifying model architecture or training . |
MR-P: A Parallel Decoding Algorithm for Iterative Refinement Non-Autoregressive Translation (2022.findings-acl)
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| Challenge: | Non-autoregressive neural machine translation models remove dependency between tokens in the target sentence and generate all tokens on parallel . |
| Approach: | They propose a non-autoregressive neural machine translation model that decodes with the Mask-Predict algorithm which iteratively refines the output. |
| Outcome: | The proposed algorithm increases the performance of the WMT’14 translation task by 1.39 points. |
To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models (2024.findings-emnlp)
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Bozhong Tian, Xiaozhuan Liang, Siyuan Cheng, Qingbin Liu, Mengru Wang, Dianbo Sui, Xi Chen, Huajun Chen, Ningyu Zhang
| Challenge: | Existing unlearning paradigms are mired in vague forgetting boundaries, erasing knowledge indiscriminately. |
| Approach: | They propose a benchmark to evaluate if unlearning erases essential knowledge . they propose 'knowUnDo' which uses copyrighted content and privacy domains . |
| Outcome: | The proposed method is superior to existing methods in both precise knowledge unlearning and general knowledge retaining of LLMs. |
Reasoning Like a Doctor: Improving Medical Dialogue Systems via Diagnostic Reasoning Process Alignment (2024.findings-acl)
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| Challenge: | Medical dialogue systems have attracted significant attention for their potential to act as medical assistants. |
| Approach: | They propose a framework that emulates clinicians' diagnostic reasoning processes and aligns with clinician preferences through thought process modeling. |
| Outcome: | The proposed framework generates appropriate responses that relies on abductive and deductive diagnostic reasoning analyses and aligns with clinician preferences through thought process modeling. |
Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge (2023.acl-long)
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| Challenge: | Large language models (LLMs) have been studied for their ability to store and utilize positive knowledge. |
| Approach: | They propose to use a constrained keywords-to-sentence generation task and a Boolean question answering task to probe large language models on negative commonsense knowledge. |
| Outcome: | The proposed tasks show that LLMs fail to generate valid sentences grounded in negative commonsense knowledge, yet they can correctly answer yes-or-no questions. |
Recyclable Tuning for Continual Pre-training (2023.findings-acl)
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Yujia Qin, Cheng Qian, Xu Han, Yankai Lin, Huadong Wang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Continual pre-training is the paradigm where pre-trained language models acquire fresh knowledge and gradually get upgraded. |
| Approach: | They propose to use adapted weights to recycle old PLMs for continual pre-training . they propose to combine initialization and distillation methods to achieve better performance . |
| Outcome: | The proposed method improves the convergence and performance of the upgraded PLM. |
IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions (2023.emnlp-main)
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| Challenge: | Prior work on IE comprehension has focused on detecting idiomaticity, but this fails to account for IEs' non-compositionality. |
| Approach: | They construct a commonsense knowledge graph for figurative interpretations of IEs that can be used to convert PTLMs into knowledge models that encode and infer commonsensical knowledge related to IE use. |
| Outcome: | The proposed model can generalize to IEs unseen during training. |
Snapshot-Guided Domain Adaptation for ELECTRA (2022.findings-emnlp)
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| Challenge: | Existing domain-specific knowledge of domain-related tasks is lacking in pre-trained language models. |
| Approach: | They propose a domain-adaptation method which can dynamically select domain-specific tokens and guide the discriminator to emphasize them, without introducing new training parameters. |
| Outcome: | The proposed method can capture domain-specific knowledge of domain-related tasks without introducing new training parameters. |
Emotion–Cause Pair Extraction in Conversations via Semantic Decoupling and Alignment (2026.findings-acl)
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| Challenge: | Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify the set of causal relations between emotion utterances and their triggering causes within a dialogue. |
| Approach: | They propose a framework for Emotion-Cause Pair Extraction in Conversations that decouples emotion-oriented semantics from cause-oriented ones and employs optimal transport to enable many-to-many and globally consistent emotion-cause matching. |
| Outcome: | The proposed framework achieves state-of-the-art on several benchmark datasets. |
Can Pre-trained Language Models Interpret Similes as Smart as Human? (2022.acl-long)
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| Challenge: | Simile interpretation is a crucial task in natural language processing. |
| Approach: | They propose a task to let PLMs infer the shared properties of similes by probing textual corpora and human-designed questions. |
| Outcome: | The proposed task outperforms pre-trained language models on simile interpretation tasks while still underperforming humans. |
Towards Explainable Computerized Adaptive Testing with Large Language Model (2024.findings-emnlp)
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| Challenge: | Existing methods focus on minimizing the number of questions required to assess ability, lacking clear and reliable explanations for the question selection process. |
| Approach: | They propose to use large language models to enhance computer adaptive testing (CAT) by providing human-like interpretability and explanations. |
| Outcome: | The proposed agent-based CAT performs comparably or superior to traditional CAT methods in accuracy and significantly improves student trust and satisfaction. |
MRRL: Modifying the Reference via Reinforcement Learning for Non-Autoregressive Joint Multiple Intent Detection and Slot Filling (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive models for multiple intent detection and slot filling have limited overall accuracy due to multi-modality problem and lack of alignment between correct predictions. |
| Approach: | They propose a method for multiple intent detection and slot filling that introduces a modifier and employs reinforcement learning to modify the reference. |
| Outcome: | The proposed method outperforms the previous best approach by 3.6 overall accuracy on MixATIS dataset. |
Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight (2020.acl-main)
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| Challenge: | Current state-of-the-art neural dialogue models learn from human conversations . however, due to the open-ended nature of human conversations, the quality of training data varies . |
| Approach: | They propose a data manipulation framework to augment and highlight effective training samples . they also propose to increase its manipulation skills through gradient descent with validation samples a reshaping framework to proactively restructure the data distribution towards reliable samples is also proposed . |
| Outcome: | The proposed framework improves the performance of open-domain neural dialogue models with respect to evaluation metrics and human judgments. |
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning (2022.acl-short)
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Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length. |
| Approach: | They propose to use a length-aware Convolutional Neural Network to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy. |
| Outcome: | The proposed model improves performance under both offline and online learning strategies. |
Generation-Augmented Retrieval: Rethinking the Role of Large Language Models in Zero-Shot Relation Extraction (2025.findings-emnlp)
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| Challenge: | Recent advances in Relation Extraction (RE) emphasize Zero-Shot methodologies, aiming to recognize unseen relations between entities with no annotated data. |
| Approach: | They propose a plug-in retrieval adjuster that allows rapid fine-tuning without accessing LLMs’ parameters. |
| Outcome: | The proposed model demonstrates comparable performance on multiple benchmarks. |
Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation (2025.emnlp-main)
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Kaikai An, Fangkai Yang, Liqun Li, Junting Lu, Sitao Cheng, Shuzheng Si, Lu Wang, Pu Zhao, Lele Cao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Baobao Chang
| Challenge: | Recent advances in retrieval-augmented generation (RAG) have substantially improved question-answering systems, particularly for factoid ‘5Ws’ questions. |
| Approach: | They propose a data organization paradigm where large language models transform documents into more structured and loosely interconnected LUs. |
| Outcome: | Experiments in open-domain and industrial settings show that the proposed paradigm outperforms existing paradigms and shows high adaptability across diverse document formats. |
SILO-BENCH: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems (2026.acl-long)
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Yuzhe Zhang, Feiran Liu, Yi Shan, Xinyi Huang, Xin Yang, Yueqi Zhu, Xuxin Cheng, Cao Liu, Ke Zeng, Terry Jingchen Zhang, Wenyuan Jiang
| Challenge: | Existing benchmarks conflate coordination ability with role-based priors. |
| Approach: | They propose a role-free benchmark for evaluating free-form collaboration under information silos. |
| Outcome: | The proposed benchmark systematically probes coordination capabilities under information silos using 54 configurations and 3 frontier LLMs. |
VoxpopuliTTS: a large-scale multilingual TTS corpus for zero-shot speech generation (2025.coling-main)
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Wenrui Liu, Jionghao Bai, Xize Cheng, Jialong Zuo, Ziyue Jiang, Shengpeng Ji, Minghui Fang, Xiaoda Yang, Qian Yang, Zhou Zhao
| Challenge: | Existing multilingual TTS datasets are limited in speech generation fields due to lack of quality data. |
| Approach: | They propose to use 30,000 hours of high-quality speech data across 3 languages . they filter out low-quality text-text pairs and concatenate short transcripts . |
| Outcome: | The proposed dataset comprises 30,000 hours of high-quality speech data, across 3 languages with multiple speakers and styles, suitable for various speech tasks such as TTS and ASR. |
Adaptive Spatial and Temporal Redundancy Optimization for Efficient Reasoning in Large Language Models (2026.acl-long)
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Tianle Chen, Pengyu Cheng, Qiyuan Zhu, Jiacheng Wang, Bei Liu, Hao Gu, Ruijie Shen, Xiaofeng Hou, Sirui Han, Jiacheng Liu
| Challenge: | Existing research to improve CoT efficiency falls into three categories, each with distinct limitations. |
| Approach: | They propose a training-free framework that addresses both dimensions of CoT reasoning by applying a progressive precision reduction strategy coupled with an entropy-based confidence mechanism for adaptive termination. |
| Outcome: | Empirical results show that the proposed framework achieves 11.3 efficiency gain without compromising accuracy. |
Neural Machine Translation with Contrastive Translation Memories (2022.emnlp-main)
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| Challenge: | Experimental results show that retrieval-augmented NMT model obtains substantial improvements over strong baselines in the benchmark dataset. |
| Approach: | They propose a retrieval-augmented NMT model that is holistically similar to the source sentence while individually contrastive to each other. |
| Outcome: | The proposed model improves on baselines in the translation task. |
Towards Self-Evolving Agents: Enabling Autonomy through Interactive Experience Refinement (2026.findings-acl)
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Cheng Yang, Xuemeng Yang, Licheng Wen, Daocheng Fu, Jianbiao Mei, Rong Wu, Pinlong Cai, Yufan Shen, Nianchen Deng, Jia Xu, Botian Shi, Yu Qiao, Haifeng Li
| Challenge: | Large Language Models struggle with complex, multi-step operational tasks because they remain static during inference and cannot learn from past experience. |
| Approach: | They propose a framework that organizes cross-domain insights to facilitate orchestration of long-horizon workflows. |
| Outcome: | The proposed framework outperforms existing methods on the TAC productivity benchmark and shows strong cross-task transferability. |
VScript: Controllable Script Generation with Visual Presentation (2022.aacl-demo)
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Ziwei Ji, Yan Xu, I-Tsun Cheng, Samuel Cahyawijaya, Rita Frieske, Etsuko Ishii, Min Zeng, Andrea Madotto, Pascale Fung
| Challenge: | Using a script generation system, scriptwriters can customize their scripts using video retrieval. |
| Approach: | They propose a controllable pipeline that generates complete scripts, including dialogues and scene descriptions, and presents visually using video retrieval. |
| Outcome: | The proposed system outperforms baselines on both automatic and human evaluations, especially in genre control. |
SafeSwitch: Steering Unsafe LLM Behavior via Internal Activation Signals (2025.findings-emnlp)
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| Challenge: | Existing safety mechanisms for large language models (LLMs) are inadequate to fully leverage their internal cognitive processes. |
| Approach: | They propose a framework that regulates unsafe outputs by utilizing the prober-based internal state monitor that actively detects harmful intentions. |
| Outcome: | The proposed framework reduces harmful outputs by approximately 80% while maintaining strong utility. |
Steering Away from Refusal: A Black-box Jailbreak Method Based on First-Token Distribution (2026.findings-acl)
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| Challenge: | Existing methods to analyze black-box jailbreaks lack direct optimization signals to refine adversarial prompts. |
| Approach: | They propose a distribution-jailbreak attack method that selects effective jailbreak templates and iteratively optimizes adversarial suffixes by maximizing the KL divergence from the standard refusal distribution. |
| Outcome: | The proposed method achieves state-of-the-art Attack Success Rate (ASR) on all tested open-source models and delivers over 94% ASR on GPT-4.1. |
CE-DA: Custom Embedding and Dynamic Aggregation for Zero-Shot Relation Extraction (2025.coling-main)
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| Challenge: | Existing methods to predict relationships with given entity pairs are lacking in supervised methods. |
| Approach: | They propose a framework for zero-shot Relation Extraction that includes two modules: Custom Embedding and Dynamic Aggregation. |
| Outcome: | The proposed framework shows competitive performance on two ZSRE datasets. |
Transductive Learning for Unsupervised Text Style Transfer (2021.emnlp-main)
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| Challenge: | Existing methods for style transfer are based on an inductive learning approach, which represents the style as embeddings, decoder parameters, or discriminator parameters and directly applies these general rules to the test cases. |
| Approach: | They propose a retrieval-based context-aware style representation that involves top-K relevant sentences in the target style in the transfer process. |
| Outcome: | The proposed method outperforms several strong baselines and is general and effective to the task of unsupervised style transfer. |
RRHF-V: Ranking Responses to Mitigate Hallucinations in Multimodal Large Language Models with Human Feedback (2025.coling-main)
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| Challenge: | Existing methods to mitigate hallucinations generate erroneous or fabricated information. |
| Approach: | They propose a rank-response-based model that annotates pair-reponses and trains alignment algorithms to improve the correspondence between images and text. |
| Outcome: | The proposed model outperforms the DPO method and outperfies existing methods on two MLLMs of different sizes and four widely used benchmarks. |
Language Models as Inductive Reasoners (2024.eacl-long)
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| Challenge: | Inductive reasoning is a core component of human intelligence. |
| Approach: | They propose a task to induce natural language rules from natural language facts using natural language as representation for knowledge instead of formal language. |
| Outcome: | The proposed task surpasses baselines in both automatic and human evaluations. |
QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory (2025.findings-emnlp)
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Yihang Wang, Xu Huang, Bowen Tian, Yueyang Su, Lei Yu, Huaming Liao, Yixing Fan, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing methods for compressing context by removing redundant tokens are inconsistent with the objective of retaining the most important tokens when conditioning on a given query. |
| Approach: | They propose a method that uses information bottleneck theory to compress context . they propose to remove redundant tokens using metrics such as self-information or perplexity . |
| Outcome: | The proposed method achieves a 25% increase in compression rate compared to the state-of-the-art . |
CART: A Generative Cross-Modal Retrieval Framework With Coarse-To-Fine Semantic Modeling (2025.acl-long)
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Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, Zhenhua Dong, Zhou Zhao
| Challenge: | Cross-modal retrieval tasks are used to retrieve data from one modality or another based on a query from another modality. |
| Approach: | They propose a generative cross-modal retrieval framework based on coarse-to-fine semantic modeling . they propose combining K-Means and RQ-VAE to discretize multimodal data into token sequences that support autoregressive generation. |
| Outcome: | The proposed framework achieves excellent performance and efficiency in multimodal retrieval tasks. |
Lost in Decomposition: Analyzing and Mitigating the Limitations of Long Context Methods via Context Dependency (2026.findings-acl)
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| Challenge: | Existing workflow-based long context methods do not perform well on specific datasets . performance degradation is associated with the indiscriminate application of long context models . |
| Approach: | They propose a training-free adaptive routing strategy to improve long context large language models' robustness. |
| Outcome: | The proposed method can be generalized to all types of datasets, but performance degradation is a concern. |
VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models (2024.emnlp-main)
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| Challenge: | Recent research has focused on pushing weight-only quantization to extremely low-bit due to numerical representation limitations. |
| Approach: | They propose a vector-based quantization approach that pushes LLMs to extremely low-bit . they propose scalar-based weight quantization that reduces memory requirements and optimizes storage costs . |
| Outcome: | The proposed method reduces model quantization perplexity by 0.01-0.34 on LLaMA-2, 0.38-0.68 on mistral-7B, 4.41-7.34, on llaMA-3 on QA tasks on average. |
Question Answering as Programming for Solving Time-Sensitive Questions (2023.emnlp-main)
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| Challenge: | Recent studies show that Large Language Models (LLMs) have shown remarkable intelligence in question answering. |
| Approach: | They propose to reframe the Question Answering task as Programming to overcome this limitation by leveraging LLMs' superior ability in understanding both natural language and programming language. |
| Outcome: | The proposed approach improves on time-sensitive question answering datasets by 14.5% over baselines. |
Partner Personas Generation for Dialogue Response Generation (2022.naacl-main)
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| Challenge: | Existing frameworks that focus on self personas ignore the value of partner persona . experimental results show that our framework generates relevant, interesting, coherent and informative partner personages even compared to ground truth partner personagers. |
| Approach: | They propose a framework that leverages automatic partner personas generation to enhance dialogue response generation. |
| Outcome: | The proposed framework generates relevant, interesting, coherent and informative partner personas even compared to ground truth partner person . it surpasses baselines that condition on ground truth persona . |
RoboFailRing: Retrieval-Augmented and Language Grounding Failure Detection for VLM-enabled Robotic Manipulation (2026.acl-long)
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| Challenge: | RoboFailRing enables timely failure detection during task execution and enhances reasoning accuracy of VLMs. |
| Approach: | They propose a robot-based failure detection system that enables timely failure detection . they evaluate a large-scale simulated dataset and provide a grounded failure report . |
| Outcome: | The proposed method achieves rapid failure detection and returns similarity-based decision on large-scale simulated failures. |
OpenSR: Open-Modality Speech Recognition via Maintaining Multi-Modality Alignment (2023.acl-long)
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| Challenge: | Speech Recognition often gets stuck in the lack of new domain utterances when training a model of new-domain speech. |
| Approach: | They propose a training system Open-modality Speech Recognition that enables zero-shot modality transfer . they use multi-modal alignment in phoneme space to maintain multi-modality alignment . |
| Outcome: | The proposed system achieves zero-shot modality transfer compared to existing methods . it achieves state-of-the-art performance on audio-visual speech recognition and lip-reading with 2.7% and 25.0%, respectively. |
ProMQA: Question Answering Dataset for Multimodal Procedural Activity Understanding (2025.naacl-long)
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Kimihiro Hasegawa, Wiradee Imrattanatrai, Zhi-Qi Cheng, Masaki Asada, Susan Holm, Yuran Wang, Ken Fukuda, Teruko Mitamura
| Challenge: | Existing studies typically provide traditional, but less practical evaluation testbeds for multimodal systems. |
| Approach: | They propose a novel evaluation dataset, ProMQA, to measure the advancement of systems in application-oriented scenarios. |
| Outcome: | The proposed evaluation dataset reveals a significant gap between human and competitive multimodal models. |
CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction (2024.lrec-main)
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| Challenge: | Existing IE tools lack multi-task support and automatic updates for KG and EKG construction. |
| Approach: | They propose a human-machine-cooperative IE toolkit for KG and EKG construction that unifies different IE subtasks and integrates LLMs as the assistant machine. |
| Outcome: | The proposed tool improves annotation quality, efficiency, and stability simultaneously. |
Aligning LLMs with Individual Preferences via Interaction (2025.coling-main)
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| Challenge: | Existing studies on LLMs alignment focus on generalizing their behavior to generalized values such as helpfulness, harmlessness, and honesty. |
| Approach: | They train large language models to "interact to align" to implicitly infer user preferences . they use a multi-turn preference dataset to generate a personalized alignment . |
| Outcome: | The proposed method enables dynamic, personalized alignment via interaction with a multi-turn preference dataset. |
Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction (2024.findings-acl)
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Wanlong Liu, Li Zhou, DingYi Zeng, Yichen Xiao, Shaohuan Cheng, Chen Zhang, Grandee Lee, Malu Zhang, Wenyu Chen
| Challenge: | mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring correlations among multiple events. |
| Approach: | They propose a multi-event argument argument extraction model which extracts arguments from all events simultaneously. |
| Outcome: | The proposed model performs better on four public datasets while saving time. |
REALM: A Dataset of Real-World LLM Use Cases (2025.findings-acl)
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| Challenge: | Existing studies on LLM adoption and their social implications lack empirical grounding, weakening their validity. |
| Approach: | They propose to integrate a dataset of over 94,000 LLM use cases collected from Reddit and news articles to provide insights into LLM adoption across different domains. |
| Outcome: | The proposed dataset includes over 94,000 LLM use cases collected from Reddit and news articles. |
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis (2024.emnlp-main)
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| Challenge: | Existing models consisting of multiple steps of visual and language processing are limited in the visual and visual processing community . a visual reasoner is a plug-and-play approach that can be used to improve VLMs' reasoning abilities. |
| Approach: | They propose a least-to-most visual reasoning paradigm that divides a question into sub-questions and invokes external tools for resolving sub-questions. |
| Outcome: | The proposed method can improve four VLMs on four VQA benchmarks. |
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. |
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation (2021.acl-long)
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Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jiawei Low, Lidong Bing, Luo Si
| Challenge: | Existing studies have shown that adapter-based tuning is more parameter-efficient than fine-tuning. |
| Approach: | They propose to add adapter modules to a pretrained language model and update the parameters of adapter module when learning on a downstream task. |
| Outcome: | The proposed method outperforms fine-tuning on low-resource and cross-lingual tasks and settings. |
HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference (2025.findings-acl)
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Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, Cheng Li
| Challenge: | Existing top-k attention methods struggle to strike a balance between efficiency and accuracy. |
| Approach: | They propose a top-k attention approach that integrates low-overhead techniques into the Top-k Attention process to achieve 7.2 speedup compared to vanilla full attention. |
| Outcome: | The proposed approach achieves 7.2 speedup compared to current top-k attention methods while maintaining model accuracy. |
Text Embedding as Treatment: A Meta Causal Approach for Robust Sentiment Classification (2026.findings-acl)
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Fengxiang Cheng, Chuan Zhou, Xiang Li, Haoxuan Li, Wen-li Wang, Jinkun Chen, Mingming Gong, Kun Zhang
| Challenge: | Existing methods for sentiment classification use binary treatment of words . Existing approaches limit generalizability to novel words and low-frequency words if there is a word in a sentence that is not treated . |
| Approach: | They propose a meta-causal approach that uses a single training task to identify causal words for arbitrary words. |
| Outcome: | The proposed method reduces the spurious correlation between word treatment and sentiment classification by removing words with low treatment effects from a pre-trained language model. |
SelfPrompt: Autonomously Evaluating LLM Robustness via Domain-Constrained Knowledge Guidelines and Refined Adversarial Prompts (2025.coling-main)
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| Challenge: | Existing frameworks for evaluating robustness of large language models rely on standardized benchmarks that can escalate costs and limit evaluations across domains. |
| Approach: | They propose a framework to evaluate the robustness of large language models using adversarial prompts and domain-constrained knowledge guidelines. |
| Outcome: | The proposed framework reduces dependency on conventional benchmarks and provides efficient evaluations in constrained domains. |
Unified Thinker: A General Reasoning Core for Image Generation (2026.acl-long)
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Sashuai Zhou, Qiang Zhou, Jijin Hu, Hanqing Yang, Yue Cao, Junpeng Ma, Yinchao Ma, Jun Song, Tiezheng Ge, Cheng Yu, Bo Zheng, Zhou Zhao
| Challenge: | generative models struggle with logic-intensive instruction following, exposing a persistent reasoning–execution gap. |
| Approach: | They propose a task-agnostic reasoning architecture for general image generation . they propose pixel-level feedback to ground the Thinker's policy in pixel feedback . |
| Outcome: | The proposed system significantly improves image reasoning and generation quality. |
Annotation Process for the Dialog Act Classification of a Taglish E-commerce Q&A Corpus (D19-51)
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| Challenge: | Existing studies on DA classification in general contexts have not addressed this problem. |
| Approach: | They constructed a text-based corpus of 7,265 posts from the question and answer section of products on Lazada Philippines. |
| Outcome: | The text-based corpus of 7,265 posts from the question and answer section of products on Lazada Philippines was constructed using a tagset for DA classification . the corpus was composed dominantly of single-label posts, with 34% of the corpuse having multiple intent tags. |
Cyclical Contrastive Learning Based on Geodesic for Zero-shot Cross-lingual Spoken Language Understanding (2024.findings-acl)
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| Challenge: | zero-shot cross-lingual SLU is a challenging task in low-resource languages . a lack of labeled training data makes it difficult to align representations of similar sentences . |
| Approach: | They propose a framework that uses cyclical contrastive learning to achieve consistency between languages . they propose to use geodesic to measure the similarity to construct positive and negative pairs . |
| Outcome: | The proposed framework achieves state-of-the-art performance on multiATIS++ and MTOP datasets. |
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training (2023.acl-long)
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| Challenge: | Empirical results show that CCLM significantly outperforms the prior state-of-the-art with an average absolute improvement of over 10%. |
| Approach: | They introduce a pre-training framework that unifies cross-lingual and cross-modal pre-trained models with shared architectures and objectives. |
| Outcome: | The proposed framework outperforms the state-of-the-art in two multi-lingual datasets and two multilingual image-text retrieval datasets. |
Logit Space Constrained Fine-Tuning for Mitigating Hallucinations in LLM-Based Recommender Systems (2025.emnlp-main)
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| Challenge: | Existing LLM-based recommender systems rely on standard fine-tuning methodologies, often ignoring hallucination issues during the fine-uning process. |
| Approach: | They propose a logit space constraint-based fine-tuning framework to mitigate hallucination in LLM-based recommenders by incorporating Kullback–Leibler divergence into the training objective. |
| Outcome: | Experiments on two recommendation models with distinct LLM backbones and four real-world datasets show that LCFT reduces hallucination and enhances recommendation performance. |
Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot (2025.findings-emnlp)
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| Challenge: | In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs). |
| Approach: | They introduce CoT to exemplars of ICL to enhance the reasoning capability . however, it remains unclear whether CoT exemplar is still beneficial for recent, stronger models in such tasks. |
| Outcome: | The enhanced exemplars fail to improve the model’s reasoning performance, despite being constructed using answers from advanced models such as Qwen2.5-Max and DeepSeek-R1. |
PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks (2025.emnlp-main)
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Yunuo Liu, Dawei Zhu, Zena Al-Khalili, Dai Cheng, Yanjun Chen, Dietrich Klakow, Wei Zhang, Xiaoyu Shen
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, such as code generation, mathematical problem-solving, and general-purpose human instruction following. |
| Approach: | They propose to use large language models to process questions expressed in natural language to automate tourism-booking prices when multiple, overlapping farerules apply. |
| Outcome: | The proposed model can automate tourism-booking prices when multiple, overlapping farerules apply. |
Neuron-Level Differentiation of Memorization and Generalization in Large Language Models (2025.emnlp-main)
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Ko-Wei Huang, Yi-Fu Fu, Ching-Yu Tsai, Yu-Chieh Tu, Tzu-ling Cheng, Cheng-Yu Lin, Yi-Ting Yang, Heng-Yi Liu, Keng-Te Liao, Da-Cheng Juan, Shou-De Lin
| Challenge: | Existing models exhibit memorization and generalization behaviors in ways that are not easily interpretable or controllable. |
| Approach: | They propose to use a GPT-2 and LLaMA-3.2 model to identify distinct neuron subsets responsible for each behavior to steer the model toward memorization or generalization. |
| Outcome: | The proposed models show that inference-time interventions on these neurons can steer the model’s behavior toward memorization or generalization. |
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation (2020.findings-emnlp)
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| Challenge: | Existing approaches to visual question answering (VQA) are not suitable for real-world applications. |
| Approach: | They propose a supervised multi-modal domain adaptation method for visual question answering in images that exploits supervised domain adaptation. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the benchmark VQA 2.0 and VizWiz datasets. |
Decouple knowledge from paramters for plug-and-play language modeling (2023.findings-acl)
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| Challenge: | Pre-trained language models (PLMs) have made impressive results in a wide range of NLP tasks. |
| Approach: | They propose a pre-training model with editable and scalable key-value memory and leverage knowledge in an explainable manner by knowledge retrieval in the pasted macro ‘MEMORY’. |
| Outcome: | The proposed model decouples the knowledge storage from model parameters with an editable and scalable key-value memory and leverages knowledge in an explainable manner by knowledge retrieval in the pasted macro ‘MEMORY’. |
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
On the Universal Truthfulness Hyperplane Inside LLMs (2024.emnlp-main)
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| Challenge: | Recent studies have explored hallucinations through the lens of internal representations, proposing mechanisms to decipher LLMs’ adherence to facts. |
| Approach: | They propose to train a universal truthfulness hyperplane that distinguishes the model’s factually correct and incorrect outputs on a diverse collection of over 40 datasets and examine its cross-task, cross-domain, and in-domain generalization. |
| Outcome: | The proposed model is able to distinguish factual outputs from incorrect outputs on a diverse collection of over 40 datasets. |
EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models (2025.acl-long)
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| Challenge: | Mixture-of-Experts (MoE) has demonstrated promising potential in scaling LLMs . however, it is hindered by two critical challenges: substantial GPU memory consumption and low activated parameters. |
| Approach: | They propose an Expert-Selection Aware Compressor for Mixture-of-Experts (MoE) that aligns with the characteristics of MoE from the perspectives of quantization and pruning. |
| Outcome: | The proposed approach significantly reduces memory usage and improves inference speed with minimal performance degradation. |
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs (2021.acl-long)
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| Challenge: | Temporal Knowledge Graphs (TKGs) are used in many different areas of research. |
| Approach: | They propose to use a beam search policy to induce multiple clues from historical facts . they propose to adopt a graph convolution network based sequence method to deduce answers from clues . |
| Outcome: | The proposed model can predict future facts in two stages, Clue Searching and Temporal Reasoning. |
Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding (2023.findings-emnlp)
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| Challenge: | Existing work on temporal sentence grounding rely on expensive video-query paired annotations . despite this, there are no ground-truth annotations in the current work . |
| Approach: | They propose to use paired video-query and segment boundary annotations to generate temporal sentence grounding without training. |
| Outcome: | The proposed model outperforms existing unsupervised methods and beats supervised ones on two challenging datasets. |
Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization (2023.findings-emnlp)
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| Challenge: | Existing methods for dialogue summarization only apply to specific scenarios and domains. |
| Approach: | They propose a pre-trained model specifically designed for multi-scenario multi-domain dialogue summarization. |
| Outcome: | The proposed model significantly outperforms state-of-the-art models on three dialogue summarization datasets from different scenarios and domains. |
Semantic-conditioned Dual Adaptation for Cross-domain Query-based Visual Segmentation (2023.findings-acl)
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| Challenge: | Existing approaches to visual segmentation from language queries require expensive labeling and degradation when deployed to an unseen domain. |
| Approach: | They propose a task to adapt a visual segmentation model from a labeled domain to an unseen domain. |
| Outcome: | The proposed framework achieves precise feature- and relation-invariant across domains via universal semantic structure. |
CPT-Agent: A Cognitive Process Theory-driven Framework for Student Simulation in Writing Development (2026.acl-long)
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| Challenge: | Existing LLMs model overly capable learners who over-apply feedback, resulting in pedagogically implausible behavior. |
| Approach: | They propose a framework that decouples cognitive ability from writing proficiency and models their interaction during writing and revision. |
| Outcome: | The proposed model produces distinguishable proficiency levels and is consistent with instructional theories. |
To Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly used for code editing, yet the full-code generation paradigm suffers from severe efficiency bottlenecks. |
| Approach: | They propose to use a structure-aware diff format to train LLMs to choose the most token-efficient format between a given diff format and full code. |
| Outcome: | The proposed approach matches the most token-efficient format with full-code generation while reducing latency and cost by over 30% on long-code editing tasks. |
Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding (2023.findings-emnlp)
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| Challenge: | Pre-trained language models (LMs) have shown effectiveness in literature understanding tasks, especially when tuned via contrastive learning. |
| Approach: | They propose a multi-task contrastive learning framework that enables common knowledge sharing across different scientific literature understanding tasks while preventing task-specific skills from interfering with each other. |
| Outcome: | The proposed framework outperforms state-of-the-art pre-trained language models on a comprehensive dataset. |
RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)
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Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu
| Challenge: | recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability. |
| Approach: | They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. |
| Outcome: | The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models. |
Disentangling Memory and Reasoning Ability in Large Language Models (2025.acl-long)
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Mingyu Jin, Weidi Luo, Sitao Cheng, Xinyi Wang, Wenyue Hua, Ruixiang Tang, William Yang Wang, Yongfeng Zhang
| Challenge: | Existing LLMs operate as an opaque process without explicit separation between knowledge retrieval and reasoning steps, making the decision-making process unclear and disorganized. |
| Approach: | They propose a language model inference paradigm that decomposes the complex inference process into two distinct and clear actions: (1) memory recall: which retrieves relevant knowledge, and (2) reasoning: which performs reasoning steps based on the recalled knowledge. |
| Outcome: | The proposed paradigm decomposes the inference process into two distinct and clear actions, memory and reason, guiding the model to distinguish between steps that require knowledge retrieval and those that involve reasoning. |
CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmented Generation (2025.findings-naacl)
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Yiruo Cheng, Kelong Mao, Ziliang Zhao, Guanting Dong, Hongjin Qian, Yongkang Wu, Tetsuya Sakai, Ji-Rong Wen, Zhicheng Dou
| Challenge: | Existing research focuses on single-turn RAG, leaving a gap in addressing multi-turn conversations . a new benchmark is designed to assess RAG systems in realistic multi-turned conversations based on Wikipedia . |
| Approach: | They propose a large-scale benchmark to assess RAG systems in multi-turn contexts . CORAL includes diverse information-seeking conversations automatically derived from Wikipedia . authors propose unified framework to standardize various conversational RAG methods . |
| Outcome: | The proposed framework supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling. |
The APVA-TURBO Approach To Question Answering in Knowledge Base (C18-1)
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| Challenge: | Existing query languages for question answering over knowledge bases are not capable of processing queries presented in human language directly. |
| Approach: | They advocate a new model architecture that includes a verification mechanism for checking the correctness of predicted relations. |
| Outcome: | The proposed approach dramatically improves the question answering performance. |
Depression Detection on Social Media with Large Language Models (2025.emnlp-industry)
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| Challenge: | Existing methods for analyzing social media data lack a systematic integration of medical knowledge, causing a critical treatment gap. |
| Approach: | They propose a framework that leverages Large Language Models to integrate medical knowledge into social media data. |
| Outcome: | The proposed framework can be used to distinguish depression from transient mood changes. |
Enhancing LLM-as-a-Judge through Active-Sampling-based Prompt Optimization (2025.acl-industry)
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| Challenge: | Suboptimal prompts can introduce biases, inconsistencies, and unreliable evaluations. |
| Approach: | They propose an active-sampling-based framework for automatic prompt optimization . they use a small, diverse subset of samples to guide prompt refinement . |
| Outcome: | The proposed framework outperforms baselines on four popular LLMs and three real-world datasets. |
Tab-Cleaner: Weakly Supervised Tabular Data Cleaning via Pre-training for E-commerce Catalog (2023.acl-industry)
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Kewei Cheng, Xian Li, Zhengyang Wang, Chenwei Zhang, Binxuan Huang, Yifan Ethan Xu, Xin Luna Dong, Yizhou Sun
| Challenge: | Existing methods for analyzing textual attributes in product catalogs are not effective on structured tabular data since they are trained on free-form natural language texts. |
| Approach: | They propose a model to handle error detection over tabular data following a pre-training paradigm. |
| Outcome: | The proposed model improves on a real-world Amazon Product Catalog table by 16% over state-of-the-art methods and by 11% on PR AUC over attribute value validation task. |
Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) driven by In-Context Learning (ICL) have improved performance of text-to-SQL. |
| Approach: | They propose a strategy to mitigate hallucinations in large language models driven by In-Context Learning (ICL) they propose TA-SQL, a text-to-Sql framework that encourages LLMs to take advantage of similar tasks rather than starting from scratch. |
| Outcome: | The proposed framework improves the performance of the GPT-4 model by 21.23% on BIRD dev. |
Velocitune: A Velocity-based Dynamic Domain Reweighting Method for Continual Pre-training (2025.acl-long)
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| Challenge: | Existing methods to optimise pretraining performance have not addressed the complexities of domain-adaptive continual pretraining. |
| Approach: | They propose a framework that dynamically assesses learning velocity and adjusts data proportions accordingly, favouring slower learning domains while de-emphasising faster learning ones. |
| Outcome: | The proposed framework achieves performance gains in math and code reasoning tasks and command-line generation benchmarks. |
InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions (2023.tacl-1)
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Zeqiu Wu, Ryu Parish, Hao Cheng, Sewon Min, Prithviraj Ammanabrolu, Mari Ostendorf, Hannaneh Hajishirzi
| Challenge: | In information-seeking conversations, a user may ask questions that are under-specified or unanswerable. |
| Approach: | They present a dataset for information-seeking conversations with mixed-initiative interactions . they use Wikipedia to search for answers and provide relevant information . |
| Outcome: | The proposed system significantly underperforms humans in two of the most recent studies. |
EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets (2021.acl-long)
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| Challenge: | Large-scale pre-trained language models require enormous computational resources and long training time. |
| Approach: | They propose an algorithm to reduce inference time and train large NLP models by slimming the self-attention and fully-connected sub-layers inside a transformer. |
| Outcome: | The proposed algorithm achieves comparable performance to standard BERT with 35 45% less training time. |
Native Hybrid Attention for Efficient Sequence Modeling (2026.acl-long)
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| Challenge: | Experimental results show that NHA surpasses Transformers on recall-intensive tasks. |
| Approach: | They propose a hybrid architecture of linear and full attention that integrates both into a unified layer design. |
| Outcome: | The proposed architecture surpasses Transformers and other hybrid baselines on recall-intensive and commonsense reasoning tasks. |
G2RPO-A: Guided Group Relative Policy Optimization with Adaptive Guidance (2026.acl-long)
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| Challenge: | Recent advances in reasoning-centric large language models (LLMs) have significantly expanded the performance boundaries of LLMs, showcasing the immense potential of reasoning-enhanced models. |
| Approach: | They propose an adaptive algorithm that injects ground-truth reasoning steps into roll-out trajectories to compensate for SLMs’ inherent weaknesses. |
| Outcome: | Experiments on mathematical reasoning and code-generation benchmarks confirm that G2RPO-A substantially outperforms vanilla GRPO. |
SDBench: A Survey-based Domain-specific LLM Benchmarking and Optimization Framework (2025.acl-long)
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| Challenge: | acquiring domain-specific knowledge often requires professional expert manpower. |
| Approach: | They propose a generic framework for generating evaluation datasets for domain-specific LLMs. |
| Outcome: | The proposed framework reduces the reliance on expert manpower while ensuring that the collected data is uniformly distributed. |
ChemVLR: Prioritizing Reasoning in Perception for Chemical Vision-Language Understanding (2026.findings-acl)
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| Challenge: | Currently, vision-Language Models are optimized for direct visual question-answering tasks. |
| Approach: | They propose a visual-language-based VLM that prioritizes reasoning within the perception process. |
| Outcome: | The proposed model outperforms existing models and domain-specific open-source models in the chemical domain. |
CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations (2023.emnlp-main)
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| Challenge: | Recent work has aimed to capture nuances of human behavior by using LLMs to simulate responses from demographics in social science experiments and public opinion surveys. |
| Approach: | They propose a framework to characterize LLM simulations using four dimensions: Context, Model, Persona, and Topic. |
| Outcome: | The proposed framework measures open-ended LLM simulations’ susceptibility to caricature, defined via two criteria: individuation and exaggeration. |
Learning Slice-Aware Representations with Mixture of Attentions (2021.findings-acl)
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| Challenge: | Real-world machine learning systems are achieving excellent performance in terms of coarse-grained metrics like overall accuracy and F-1 score. |
| Approach: | They extend slice-based learning (SBL) with a mixture of attentions to learn slice-aware dual attentive representations. |
| Outcome: | The proposed approach outperforms the baseline method and the original SBL approach on monitored slices with two natural language understanding tasks. |
Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing approaches to online claim verification rely on prompt engineering or pre-designed reasoning workflows. |
| Approach: | They propose an online reinforcement learning framework that enables an LLM to interact with a search engine and receive reward signals that explicitly shape its planning, retrieval, and reasoning behaviors. |
| Outcome: | Empirical results show that Veri-R1 improves joint accuracy by 30% and doubles evidence score, often surpassing larger-scale model counterparts. |
NarrowBERT: Accelerating Masked Language Model Pretraining and Inference (2023.acl-short)
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| Challenge: | Large-scale language model pretraining is expensive as the models and pretraining corpora have become larger over time. |
| Approach: | They propose a modified transformer encoder that increases throughput for masked language model pretraining by more than 2x. |
| Outcome: | The proposed model increases throughput on IMDB and Amazon reviews classification and CoNLL NER tasks by 3.5x with minimal performance degradation. |
GuideBench: Benchmarking Domain-Oriented Guideline Following for LLM Agents (2025.acl-long)
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| Challenge: | Large language models (LLMs) have been widely deployed as autonomous agents capable of following user instructions and making decisions in real-world applications. |
| Approach: | They propose a benchmark to evaluate LLMs' ability to follow domain-oriented guidelines . they evaluate Lms on three critical aspects: adherence to diverse rules, robustness to rule updates . |
| Outcome: | The proposed benchmark evaluates LLMs on three critical aspects: adherence to diverse rules, robustness to rule updates, and alignment with human preferences. |
PEARL: Self-Evolving Assistant for Time Management with Reinforcement Learning (2026.acl-long)
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| Challenge: | Scheduling logistics associated with meetings can amount to hours each week . a lack of automation can cause a lot of errors and can be costly . |
| Approach: | They propose a benchmark for long-horizon calendar conflict resolution that automates calendar conflict management . they propose 'pearl' that augments the language agent with an external preference memory . |
| Outcome: | a new benchmark for long-horizon calendar conflict resolution shows that existing agents perform poorly with high error rates. |
Schoenfeld’s Anatomy of Mathematical Reasoning by Language Models (2026.acl-long)
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| Challenge: | Large language models expose reasoning traces, yet their underlying cognitive structure and steps remain difficult to identify and analyze beyond surface-level statistics. |
| Approach: | They propose a framework that explicitly abstracts reasoning traces into functional reasoning steps such as Analysis, Explore, Implement, Verify, etc. |
| Outcome: | The proposed framework reveals reproducible thinking dynamics and structural differences between reasoning and non-reasoning models, which are not apparent from token-level views. |
Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning (2020.coling-main)
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| Challenge: | Chinese word segmentation datasets have ambiguous annotation criteria resulting in multi-grained compounds. |
| Approach: | They propose a domain adaptive segmenter to exploit diverse annotation criteria of datasets . they use bidirectional encoder representations from transformers to introduce open-domain knowledge . |
| Outcome: | The proposed model outperforms the state-of-the-art models on 10 Chinese word datasets with superior efficiency. |
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. |
IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization (2025.acl-long)
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| Challenge: | Existing algorithms to improve the ability of LLMs to follow complex instructions are lacking. |
| Approach: | They propose a benchmark to improve the ability to follow complex instructions by using a IOPO alignment method to take input and output preference into consideration. |
| Outcome: | The proposed algorithm shows 8.15%, 2.18% improvements on in-domain data and 5.91%, 2.83% on out-of-domain datasets compared to SFT and DPO respectively. |
Temporal Knowledge Graph Reasoning Based on N-tuple Modeling (2023.findings-emnlp)
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| Challenge: | Existing Temporal Knowledge Graphs (TKGs) only contain their core entities and form them as quadruples. |
| Approach: | They propose to describe a temporal fact more accurately as an n-tuple . they propose to use a neural network to learn evolutional representations of entities . |
| Outcome: | The proposed model oversimplifies and causes information loss on two datasets. |
Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective (2023.emnlp-industry)
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| Challenge: | a study examines how to build meeting summarization systems using large language models . closed-source models are generally better in terms of performance, but open-source ones are more advantageous for industrial use . |
| Approach: | They compare closed-source and open-source meeting summarization models for real-world use . they find that closed-sourced models are generally better in terms of performance . however, smaller open-sourced LLMs could still achieve comparable performance if they are open . |
| Outcome: | The proposed model is more efficient for industrial use than closed-source models due to privacy concerns and high cost. |
Commonsense Subgraph for Inductive Relation Reasoning with Meta-learning (2025.coling-main)
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| Challenge: | Existing subgraph-based models focus on predicting missing relations in knowledge graphs . a new meta-learning model extracts concepts from entities to construct commonsense subgraphs based on semantic information . |
| Approach: | They propose a commonsense subgraph meta-learning model that extracts concepts from entities to construct commonsensible subgraphs. |
| Outcome: | The proposed model outperforms existing models in inductive reasoning tasks and in few-shot scenarios. |
CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data Partitions (2024.emnlp-main)
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| Challenge: | Current studies have focused on fine-tuning, but the use of instruction tuning is not as effective as fine-cuning. |
| Approach: | They propose a commonality-aware instruction tuning strategy to cluster instruction datasets into distinct groups with three proposed metrics Task, Embedding and Length. |
| Outcome: | The proposed strategy boosts an average improvement of 2.1% on the general domain and 5.2% on the special domain. |
NL2Logic: AST-Guided Translation of Natural Language into First-Order Logic with Large Language Models (2026.findings-eacl)
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| Challenge: | Structured reasoning approaches that parse first-order logic rules from natural language lack syntax control and semantic faithfulness. |
| Approach: | They propose a structured reasoning paradigm that parses first-order logic rules from natural language and delegates inference to automated solvers. |
| Outcome: | a proposed framework parses first-order logic rules from natural language and delegates inference to automated solvers. |
Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences (2022.findings-emnlp)
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| Challenge: | Natural language inference (NLI) tasks are difficult to perform on large datasets . a small number of simple sentences can improve model performance, authors say . |
| Approach: | They propose to use syntactically simple sentences to test the inference ability of NLI models. |
| Outcome: | The proposed set of simple sentences shows that the models fine-tuned on MNLI and SNLI perform poorly on Simple Pair. |
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. |
ActPlan-1K: Benchmarking the Procedural Planning Ability of Visual Language Models in Household Activities (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have been adopted to process textual task description and accomplish procedural planning in embodied AI tasks because of their powerful reasoning ability. |
| Approach: | They propose to evaluate the planning ability of large language models and multi-modal counterfactual vision language models (VLMs) using a multi-factual household activity simulator and a chatGPT task description to evaluate their reasoning ability. |
| Outcome: | The proposed benchmark evaluates the planning ability of multi-modal and counterfactual vision language models on a household activity simulator and a chatGPT task description. |
Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards (2026.acl-long)
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| Challenge: | Existing benchmarks fail to reflect real-world communication needs and are limited in their coverage. |
| Approach: | They present a comprehensive index of sign-language datasets, covering 120 resources across 35 sign languages. |
| Outcome: | The proposed index covers 120 resources across 35 sign languages. |
Code-Switching Can be Better Aligners: Advancing Cross-Lingual SLU through Representation-Level and Prediction-Level Alignment (2024.acl-short)
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| Challenge: | Existing code-switching-based cross-lingual spoken language understanding frameworks are limited to low-resource languages. |
| Approach: | They propose a cross-lingual spoken language understanding framework that leverages both code-switched and original sentences to achieve multi-level alignment. |
| Outcome: | The proposed framework can achieve multi-level alignment on two benchmarks across ten languages. |
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective (2025.naacl-long)
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Shenglai Zeng, Jiankun Zhang, Bingheng Li, Yuping Lin, Tianqi Zheng, Dante Everaert, Hanqing Lu, Hui Liu, Hui Liu, Yue Xing, Monica Xiao Cheng, Jiliang Tang
| Challenge: | Existing studies have shown that LLMs struggle to identify the boundaries of their own knowledge and tend to prioritize external information over internal knowledge learned during pre-training. |
| Approach: | They conduct a comprehensive analysis of LLM representation behaviors and demonstrate the significance of using representations in knowledge checking. |
| Outcome: | The proposed classifiers improve performance even when dealing with noisy knowledge databases. |
Knowledge-enhanced Multimodal ECG Representation Learning with Arbitrary-Lead Inputs (2025.findings-emnlp)
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| Challenge: | Current methods for multimodal representation learning for electrocardiograms often result in suboptimal alignment of ECG signals with their corresponding text reports. |
| Approach: | They propose a framework to learn ECG representations by aligning ECG signals with paired free-text reports. |
| Outcome: | The proposed framework outperforms existing methods in zero-shot classification and linear probing tasks using 12 leads. |
MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective (2022.acl-long)
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Xiao Wang, Shihan Dou, Limao Xiong, Yicheng Zou, Qi Zhang, Tao Gui, Liang Qiao, Zhanzhan Cheng, Xuanjing Huang
| Challenge: | Named Entity Recognition models are feature-engineering and machine learning based. |
| Approach: | They propose a new NER learning framework that uses entity mentions to improve model performance. |
| Outcome: | The proposed model achieves better performance on OOV entities on various settings and datasets. |
Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs (2025.findings-emnlp)
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| Challenge: | Sparse Mixture-of-Experts (SMoE) architectures require loading all expert parameters . previous work focused on expert pruning and merging but focused on neuron-level structure . |
| Approach: | They propose a task-agnostic framework for expert pruning and reconstruction . it prunes redundant experts using router statistics, then decomposes them into neuron-level expert segments . |
| Outcome: | The proposed framework reduces the number of experts and memory usage, making it easier to deploy. |
Decoding by Contrasting Knowledge: Enhancing Large Language Model Confidence on Edited Facts (2025.acl-long)
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| Challenge: | In-context knowledge editing (ICE) is currently the most effective method for knowledge editing, but it is constrained by the black-box modeling of LLMs and lacks interpretability. |
| Approach: | They propose a method to decode new knowledge by comparing logits with unedited knowledge to improve the accuracy of LLMs. |
| Outcome: | The proposed method improves the performance of LLaMA3-8B-instruct on MQuAKE by up to 219%. |
AgentBank: Towards Generalized LLM Agents via Fine-Tuning on 50000+ Interaction Trajectories (2024.findings-emnlp)
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Yifan Song, Weimin Xiong, Xiutian Zhao, Dawei Zhu, Wenhao Wu, Ke Wang, Cheng Li, Wei Peng, Sujian Li
| Challenge: | Existing studies focus on specialized agents designed for particular tasks. |
| Approach: | They propose to scale annotated interaction trajectories and fine-tune LLMs on AgentBank to get a series of agent models, Samoyed. |
| Outcome: | The proposed model can scale to get generalized agent capabilities. |
Neutralizing Bias in LLM Reasoning using Entailment Graphs (2025.findings-acl)
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| Challenge: | Natural Language Inference (NLI) is a foundational understanding task in language understanding. |
| Approach: | They propose a framework to construct counterfactual reasoning data and fine-tune LLMs to reduce attestation bias. |
| Outcome: | The proposed framework reduces hallucinations from attestation bias on original and bias-neutralized datasets while keeping hypotheses unchanged. |
Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs (2025.acl-long)
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| Challenge: | Recent studies have focused on prompt engineering to extract sentence embeddings from large language models (LLMs) but these models are mostly decoder-only and the earlier tokens in the sentence cannot attend to the latter, resulting in biased encoding of sentence information and cascading effects on the final decoded token. |
| Approach: | They propose a plug-and-play and training-free technique that prepends each layer’s decoded sentence embedding to the beginning of the sentence in the next layer’ s input. |
| Outcome: | The proposed technique can significantly improve the performance of existing prompt-based sentence embedding methods across different LLMs while incurring negligible additional inference cost. |
Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning (2024.naacl-long)
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| Challenge: | Existing methods for key point analysis rely on semantic similarity instead of measuring the existence of shared key points . |
| Approach: | They propose a key point analysis approach with pairwise generation and graph partitioning to summarize arguments into a concise set of key points. |
| Outcome: | The proposed model surpasses existing models on ArgKP and QAM datasets. |
BSFA: Leveraging the Subspace Dichotomy to Accelerate Neural Network Training (2025.emnlp-main)
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| Challenge: | Recent studies highlight a fundamental dichotomy in deep learning optimization: parameter updates along the top eigendirections of the loss Hessian (Dom-space) capture most of the update magnitude, while updates in the orthogonal component (Bulk-space), have smaller magnitudes but drive most learning progress. |
| Approach: | They propose a plug-and-play framework that scales update components projected onto distinct subspaces and a block-wise strategy that applies this estimation on a per-parameter-block basis. |
| Outcome: | The proposed framework accelerates training by differentially scaling update components projected onto distinct subspaces, while enhancing stability by moderating updates in dominant subspace and boosting convergence speed by amplifying updates in bulk-space. |
LRMM: Learning to Recommend with Missing Modalities (D18-1)
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| Challenge: | Existing methods for content-based recommendation with missing or corrupted modalities are lacking in learning multimodal models. |
| Approach: | They propose a multimodal multimodal autoencoder that learns multimodal representations for complementing and imputing missing modalities. |
| Outcome: | The proposed framework achieves state-of-the-art performance on rating prediction tasks and is more robust to previous methods in alleviating data-sparsity and the cold-start problem. |
Few-Shot Multimodal Named Entity Recognition Based on Mutlimodal Causal Intervention Graph (2024.lrec-main)
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| Challenge: | Existing methods for multimodal named entity recognition are limited due to limited resources. |
| Approach: | They propose a Few-shot Multimodal Named Entity Recognition task to address these relation types by constructing a multimodal graph and a new multimodal causal intervention strategy. |
| Outcome: | The proposed model improves on two multimodal named entity recognition datasets. |
Mitigating Catastrophic Forgetting in Large Language Models with Forgetting-aware Pruning (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have shown impressive capabilities in various downstream tasks but typically face Catastrophic Forgetting (CF) during fine-tuning. |
| Approach: | They propose a pruning-based approach to balance CF and downstream task performance by integrating the ratio of the task vector to pre-trained model parameters into the pruning criteria. |
| Outcome: | The proposed pruning-based approach limits CF to just 0.25% while maintaining 99.67% accuracy on downstream tasks. |
One Cognitive Loop Is Enough: SODA unlocks Pure-Text Spatial Reasoning in Large Language Models (2026.acl-long)
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Shunwen Bai, Jiahuan Zhang, Haoran Huang, Yurun Wang, Jiale Liu, Yanxi Wu, Ningzhe Yu, Yudong Gao, Mingjun Cheng
| Challenge: | Existing large language models (LLMs) lack visual input, leading to errors in basic numerical comparisons. |
| Approach: | They propose a spatial OODA framework that integrates the OODAC cognitive loop into multiple control tasks and integrates it into LLMs. |
| Outcome: | The proposed model significantly improves the spatial reasoning capabilities of large language models across multiple scenarios including SPOD-Bench, SPACE and applications. |
HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications (2021.findings-acl)
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Qiao Cheng, Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, Yanghua Xiao
| Challenge: | Relation extraction (RE) is an essential topic in natural language processing and has attracted extensive attention. |
| Approach: | They propose a case-oriented construction framework to build a hard case relation extraction dataset with 65,225 relational facts annotated from 9,231 documents. |
| Outcome: | The proposed model achieves a high 96% F1 score on data quality and is far lower than humans. |
SDiaReward: Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness (2026.acl-long)
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Jingyu Lu, Yuhan Wang, Fan Zhuo, Xize Cheng, Changhao Pan, Xueyi Pu, Yifu Chen, Chenyuhao Wen, Tianle Liang, Zhou Zhao
| Challenge: | SDiaReward is an end-to-end spoken dialogue system that integrates paralinguistic nuances and spontaneous nature of human conversation. |
| Approach: | They propose an end-to-end multi-turn reward model trained on SDiaReward-Dataset . it is a collection of episode-level preference pairs targeting modality and colloquiality gaps . |
| Outcome: | The proposed model outperforms general-purpose audio LLMs in episode-level evaluation. |
Sources of Hallucination by Large Language Models on Inference Tasks (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI) |
| Approach: | They propose to use LLMs to probe their behavior using controlled experiments. |
| Outcome: | The proposed models perform significantly worse on NLI test samples which do not conform to these biases than those which do. |
Cooperative or Competitive? Understanding the Interaction between Attention Heads From A Game Theory Perspective (2025.acl-long)
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| Challenge: | a number of attention-based large language models (LLMs) focus on individual head contributions, but the precise interaction mechanisms between attention heads remain poorly understood. |
| Approach: | They propose a game-theoretic attention calibration method that uses the Harsanyi dividend . they selectively retain heads demonstrating significant cooperative gains and apply fine-grained adjustments to remaining heads . |
| Outcome: | The proposed framework is based on the Harsanyi dividend, a concept from cooperative game theory. |
Gated Differentiable Working Memory for Long-Context Language Modeling (2026.acl-long)
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Lingrui Mei, Shenghua Liu, Yiwei Wang, Yuyao Ge, Baolong Bi, Jiayu Yao, Jun Wan, Ziling Yin, Jiafeng Guo, Xueqi Cheng
| Challenge: | Long contexts break transformers, attention scores dilute, model cannot adapt to novel patterns at inference time. |
| Approach: | They propose a framework that gates the memory consolidation process by estimating Contextual Utility . they propose GDWM to maintain a form of working memory with constant contexts . |
| Outcome: | The proposed framework achieves comparable or superior performance on sparse-information tasks with 4 fewer gradient steps compared to uniform baselines. |
Exploring the Potential of Large Language Models in Computational Argumentation (2024.acl-long)
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| Challenge: | Argumentation is an essential tool in various domains, including law, public policy, and artificial intelligence. |
| Approach: | They propose to evaluate LLMs on various computational argumentation tasks . they organize existing tasks into six main categories and standardize the format of 14 datasets . |
| Outcome: | The proposed model performs well on argument mining and argument generation tasks. |
ULMR: Unlearning Large Language Models via Negative Response and Model Parameter Average (2024.emnlp-industry)
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| Challenge: | Large language models (LLMs) have attracted significant interest from the research community due to their broad applicability in many language-oriented tasks. |
| Approach: | They propose a framework which uses pre-training datasets to rewrite instructions and generate negative responses to preserve the performance of the original LLM. |
| Outcome: | The proposed framework can erase the pre-training data while maintaining the performance of the original model. |
SafetyQuizzer: Timely and Dynamic Evaluation on the Safety of LLMs (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have been used to evaluate the safety of their users . however, evaluation questions in current benchmarks are too straightforward and difficult to update with practical relevance due to their lack of correlation with real-world events. |
| Approach: | They propose a question-generation framework to evaluate the safety of LLMs in the Chinese context. |
| Outcome: | The proposed framework reduces decline rate while maintaining similar attack success rate. |
Subtle Errors in Reasoning: Preference Learning via Error-injected Self-editing (2025.acl-long)
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Kaishuai Xu, Tiezheng Yu, Wenjun Hou, Yi Cheng, Chak Tou Leong, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li
| Challenge: | Existing studies to improve mathematical ability typically involve applying preference learning to step-wise solution pairs, but they overlook critical subtle errors. |
| Approach: | They propose a preference learning framework that injects predefined subtle errors into pivotal tokens to construct hard pairs for error mitigation. |
| Outcome: | Extensive experiments show that the proposed framework improves on Qwen2-7B-Instruct and MATH with 4.5K training samples. |
Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance (2023.findings-acl)
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| Challenge: | Document-level event argument extraction is a challenging task for cross-sentence inference . previous work focused on document-level EAE, but recent work focused more on documentlevel . |
| Approach: | They propose a document-level event argument extraction model that captures contextual clues and latent role information. |
| Outcome: | The proposed model outperforms existing methods on two public datasets with 1.13 F1 and 2.64 F1 improvements on RAMS and WikiEvents respectively. |
Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception (2025.acl-long)
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| Challenge: | Large language models (LLMs) exhibit impressive performance across diverse tasks but struggle to accurately gauge their knowledge boundaries. |
| Approach: | They propose Consistency-based Confidence Calibration (C3) which assesses confidence consistency through question reformulation to improve LLMs’ ability to recognize their knowledge gaps. |
| Outcome: | The proposed method improves the unknown perception rate by 5.6% on NQ and 4.9% on HotpotQA. |
Cluster-Former: Clustering-based Sparse Transformer for Question Answering (2021.findings-acl)
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| Challenge: | Existing models for encoding long sequences in deep learning suffer from high latency and memory demands. |
| Approach: | They propose a clustering-based sparse Transformer framework to perform attention across chunked sequences. |
| Outcome: | The proposed framework achieves state-of-the-art on several major QA benchmarks. |
Atoxia: Red-teaming Large Language Models with Target Toxic Answers (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) are still vulnerable to generation safety vulnerabilities. |
| Approach: | They propose a method that A**tacks LLMs with target "toxi" given a particular harmful answer, the method generates a user query and a misleading answer opening to examine the internal defects of a given LLM. |
| Outcome: | The proposed method detects safety risks in open-source models and state-of-the-art models such as GPT-4o. |
Exploring Mode Connectivity for Pre-trained Language Models (2022.emnlp-main)
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| Challenge: | Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, starting from which high-performance minima could be found. |
| Approach: | They investigate the geometric connections of different minima through the lens of mode connectivity, which measures whether two minima can be connected with a low-loss path. |
| Outcome: | The proposed model can be used to find low-loss paths between two minima, and to understand how their mode connectivity affects their task knowledge. |
Enhancing Translation Ability of Large Language Models by Leveraging Task-Related Layers (2024.lrec-main)
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| Challenge: | Experimental validation shows that adjusting task-related layers significantly improves performance on translation tasks while maintaining stability and accuracy on other tasks. |
| Approach: | They propose to adjust task-related layers in large models to better harness their machine translation capabilities by revealing the structure and characteristics of attention weights through singular value decomposition. |
| Outcome: | The proposed method reduces computational resource consumption and catastrophic forgetting while maintaining stability and accuracy on other tasks. |
MDR: Model-Specific Demonstration Retrieval at Inference Time for In-Context Learning (2024.naacl-long)
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| Challenge: | Existing methods for retrieval-based in-context learning ignore model biases and fail to retrieve the most appropriate demonstrations for different LLMs. |
| Approach: | They propose a model-specific demonstration retrieval method that considers the biases of different LLMs at inference time. |
| Outcome: | The proposed method improves performance on seen and unseen tasks with multi-scale inference LLMs by up to 41.2%. |
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding (2023.emnlp-main)
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Cheng Jiayang, Lin Qiu, Tsz Chan, Tianqing Fang, Weiqi Wang, Chunkit Chan, Dongyu Ru, Qipeng Guo, Hongming Zhang, Yangqiu Song, Yue Zhang, Zheng Zhang
| Challenge: | Analogy-making between narratives is crucial for human reasoning . despite its importance, there has been limited research on story analogies . |
| Approach: | They construct a large-scale story-level analogy corpus with 24K story pairs . they find that the tasks are incredibly difficult for large language models such as ChatGPT . |
| Outcome: | The proposed corpus contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory. |
A Compressive Memory-based Retrieval Approach for Event Argument Extraction (2025.coling-main)
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| Challenge: | Existing retrieval-based EAE methods have input length constraints and the gap between the retriever and the inference model. |
| Approach: | They propose a retrieval-based retrieval mechanism that overcomes input length constraints . they use compressive memory to cache retrieved information and support continuous updates . |
| Outcome: | The proposed method outperforms retrieval-based methods on three public datasets. |
CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis (D19-1)
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| Challenge: | Existing methods for aspect-specific sentiment classification are noisy and downgraded performance. |
| Approach: | They propose a constrained attention network to regularize attention for multi-aspect sentiment analysis by orthogonal regularization on multiple aspects and sparse regularization for each single aspect. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two public datasets and extends to multi-task settings. |
Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs (2026.acl-long)
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| Challenge: | Recent studies show that large language models fail to challenge users’ harmful beliefs in domains ranging from medical advice to social reasoning. |
| Approach: | They propose to examine whether pragmatic factors influence LLM accommodation and epistemic vigilance in humans. |
| Outcome: | The proposed model can be understood and addressed as having excessive accommodation and insufficient epistemic vigilance. |
Treasures Outside Contexts: Improving Event Detection via Global Statistics (2021.emnlp-main)
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| Challenge: | Existing neural-based ED models are confused by changeable contexts during testing . we propose a system that extracts statistical event features from word-event cooccurrence frequencies . |
| Approach: | They propose to integrate a set of statistical event features from word-event co-occurrence frequencies into the training set to cooperate with contextual features. |
| Outcome: | The proposed model outperforms ten strong baselines on ACE2005 and KBP2015 datasets. |
VisDiaHalBench: A Visual Dialogue Benchmark For Diagnosing Hallucination in Large Vision-Language Models (2024.acl-long)
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| Challenge: | Despite the significant success of large vision-language models, some studies have revealed that LVLMs suffer from the hallucination problem when given long-term misleading textual history. |
| Approach: | They propose a visual dialogue hallucination evaluation benchmark VisDiaHalBench to investigate the halluciation problem of large vision-language models when given long-term misleading textual history. |
| Outcome: | The proposed benchmark consists of samples with five-turn questions about an edited image and its original version. |
TAVT: Towards Transferable Audio-Visual Text Generation (2023.acl-long)
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| Challenge: | Existing transfer learning techniques focus on uni-modal analysis and lack consideration of multi-modal content and cross-modal relation. |
| Approach: | They propose a transferable audio-visual text generation framework that incorporates two components: Audio-Visual Meta-Mapper and Dual Counterfactual Contrastive Learning. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods across multiple domains and modal settings. |
ORGAN: Observation-Guided Radiology Report Generation via Tree Reasoning (2023.acl-long)
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| Challenge: | Existing methods to generate radiology reports only rely on high-level plans, but they lack important information. |
| Approach: | They propose an Observation-guided radiology Report Generation framework which generates free-text descriptions for a set of radiographs. |
| Outcome: | The proposed framework outperforms state-of-the-art methods regarding text quality and clinical efficacy. |
ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models (2024.emnlp-main)
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Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, Hayato Yamana
| Challenge: | Currently, tool-augmented large language models (LLMs) only achieve total scores of 45.3 and 37.0, respectively, on a scale of 100. |
| Approach: | They propose a multi-level diagnostic process to assess the LLM's hallucinations through two perspectives: depth and breadth. |
| Outcome: | The proposed diagnostic process assesses the hallucinations of large language models through two perspectives: depth and breadth. |
Neo-Classic: A Benchmark for Evaluating Linguistic-Aesthetic Reasoning in Classical Chinese Poetry (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) achieve high accuracy on established Classical Chinese Poetry benchmarks, but it remains challenging to distinguish transferable Linguistic-Aesthetic Reasoning from reliance on familiar pre-training patterns. |
| Approach: | They propose a benchmark that combines a constructionist Out-of-Sample dataset with reverse understanding probes to evaluate large-scale large-format models. |
| Outcome: | The proposed model performs well on classical Chinese poetry benchmarks, but a performance gap persists . the model can complete famous couplets and can be used to understand a variety of texts. |
Attn-GS: Attention-Guided Context Compression for Efficient Personalized LLMs (2026.acl-long)
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Shenglai Zeng, Tianqi Zheng, Chuan Tian, Dante Everaert, Yau-Shian Wang, Yupin Huang, Michael J. Morais, Rohit Patki, Jinjin Tian, Xinnan Dai, Kai Guo, Monica Xiao Cheng, Hui Liu
| Challenge: | Existing approaches to personalize large language models (LLMs) rely on heuristic methods to compress user profiles but they ignore how LLMs process and prioritize different profile components. |
| Approach: | They propose an attention-guided context compression framework that leverages attention feedback from a marking model to mark important personalization sentences and guides a compression model to generate task-relevant compressed user contexts. |
| Outcome: | The proposed framework outperforms baselines across tasks, token limits, and settings while reducing token usage by 50 times. |
Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding (2023.eacl-main)
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| Challenge: | Recent studies have focused on compressing pre-trained language models (PLMs) however, few studies have examined the impact of compression on generalizability and robustness of compressed models for out-of-distribution data. |
| Approach: | They propose to use knowledge distillation and pruning to reduce model generalization and generalization on out-of-distribution data. |
| Outcome: | The proposed compression techniques overfit on shortcut samples and generalize poorly on hard ones. |
AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach (2023.findings-acl)
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| Challenge: | Argument mining involves multiple subtasks, but each one is insufficient for understanding argumentative structure and reasoning process. |
| Approach: | They propose a quadruplet extraction task that extracts four argumentative components . they use a generative quadragging module to augment the training of the generative framework . |
| Outcome: | The proposed method can extract arguments from a large-scale dataset. |
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models (2024.acl-long)
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| Challenge: | Current methods for multimodal sarcasm target identification focus on superficial indicators in an end-to-end manner, overlooking the nuanced understanding of multimodal content. |
| Approach: | They propose a multimodal sarcasm target identification framework with a coarse-to-fine paradigm by augmenting sarcasm explainability with reasoning and pre-training knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art methods and exhibits explainability in deciphering sarcasm as well. |
Masked Text-to-Audio Flow-Matching and Reward Feedback Optimization (2026.findings-acl)
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Rongjie Huang, Dongchao Yang, Wenxiang Guo, Huadai Liu, Xize Cheng, Zehan Wang, Zhou Zhao, Xixin Wu, Helen M. Meng
| Challenge: | Experimental results show that Flow-matching generative models can scale training by increasing data, computational resources, and model size. |
| Approach: | They propose a flow-matching transformer with masked generative modeling for scaling text-to-audio inference-time prediction. |
| Outcome: | The proposed model scales inference-time computations by masking generation and re-predicting them through iterative decoding. |
GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification (2022.coling-1)
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| Challenge: | Existing fact verification models lack fine-grained reasoning over key entities . GLAF uses local fission reasoning to capture latent logical relations between clues . |
| Approach: | They propose a global-to-local fission and fissional network to capture latent logical relations hidden in multiple evidence clues. |
| Outcome: | The proposed network achieves state-of-the-art on a FEVER dataset with a 77.62% FEVER score. |
BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering (2024.emnlp-main)
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Haoyu Wang, Ruirui Li, Haoming Jiang, Jinjin Tian, Zhengyang Wang, Chen Luo, Xianfeng Tang, Monica Cheng, Tuo Zhao, Jing Gao
| Challenge: | Retrieval-augmented Large Language Models struggle with complex inputs and noisy knowledge retrieval hindering model effectiveness. |
| Approach: | They propose a query generation method that integrates query generation blending with knowledge filtering to enhance retrieval-augmented LLMs. |
| Outcome: | The proposed approach surpasses state-of-the-art benchmarks on open-domain question answering benchmarks. |
A Self-supervised Joint Training Framework for Document Reranking (2022.findings-naacl)
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| Challenge: | Pretrained language models have been successfully applied to a wide range of tasks . however, the pretraining tasks were based on the context of documents . |
| Approach: | They propose a self-supervised joint training framework with a method called Masked Query Prediction to establish semantic relations between given queries and positive documents. |
| Outcome: | The proposed framework outperforms existing models on document reranking tasks without further pre-training . it uses a self-supervised method to establish semantic relations between given queries and positive documents. |
Probing Relative Interaction and Dynamic Calibration in Multi-modal Entity Alignment (2025.acl-long)
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| Challenge: | Current methods for multi-modal entity alignment ignore relative interactions between modalities and the accuracy of weights. |
| Approach: | They propose a relative interaction and calibration framework for multi-modal entity alignment that uses attention mechanisms to perceive the uncertainty of the weight for each modality. |
| Outcome: | The proposed framework outperforms baselines across 5 datasets and 23 settings. |
Dehumanizing Machines: Mitigating Anthropomorphic Behaviors in Text Generation Systems (2025.acl-long)
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| Challenge: | Existing studies have focused on how text generation systems can lead to harmful outcomes such as over-reliance, emotional dependence, dehumanization, deception, or even physical harm. |
| Approach: | They propose to use an inventory of interventions to help identify possible interventions and provide a conceptual framework to help characterize the landscape of possible interventions. |
| Outcome: | The proposed interventions are based on an inventory of interventions grounded in prior literature and a crowdsourcing study where participants edited system outputs to make them less human-like. |
SGMEA: Structure-Guided Multimodal Entity Alignment (2025.coling-main)
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| Challenge: | Existing methods focus on interactions between neighboring entities in the structural modality while neglecting interactions between entities in visual and attribute modalities. |
| Approach: | They propose a structure-guided multimodal entity alignment method which prioritizes structural information from knowledge graphs to enhance the visual and attribute modalities. |
| Outcome: | The proposed method achieves state-of-the-art performance across multiple datasets, validating its effectiveness and superiority in practical applications. |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
Bit-Flip Error Resilience in LLMs: A Comprehensive Analysis and Defense Framework (2025.emnlp-main)
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Yuhang Chen, Zhen Tan, Ajay Kumar Jaiswal, Huaizhi Qu, Xinyu Zhao, Qi Lin, Yu Cheng, Andrew Kwong, Zhichao Cao, Tianlong Chen
| Challenge: | Bit-flip errors (BFEs) are hardware faults where individual bits in memory or processing units are unintentionally flipped. |
| Approach: | They propose a novel defense strategy to mitigate bit-flip errors (BFEs) they propose bfe protection and a self-correction mechanism to minimize performance degradation . |
| Outcome: | The proposed defense strategy minimizes performance degradation while significantly improving robustness against BFEs. |
“In-Dialogues We Learn”: Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning (2024.emnlp-main)
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| Challenge: | Existing approaches to personalized dialogue generate pre-defined profiles that are time-consuming and labor-intensive to create. |
| Approach: | They propose a framework that leverages dialogue history to characterize personas without pre-defined profiles. |
| Outcome: | The proposed framework improves BLEU and ROUGE scores on three datasets and human evaluations further validate the proposed method. |
MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing (2024.findings-acl)
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Jiaqi Li, Miaozeng Du, Chuanyi Zhang, Yongrui Chen, Nan Hu, Guilin Qi, Haiyun Jiang, Siyuan Cheng, Bozhong Tian
| Challenge: | Current benchmarks focus on coarse-grained knowledge, leaving the intricacies of fine-grounded knowledge unexplored. |
| Approach: | They propose a benchmark and dataset specifically designed for FG multimodal entity knowledge editing. |
| Outcome: | The proposed benchmark underscoring the complexity of FG knowledge editing in MLLMs. |
ChartM3: A Multi-Stage Code-Driven Pipeline for Constructing Multi-Dimensional and Multi-Step Visual Reasoning Data in Chart Comprehension (2025.findings-emnlp)
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| Challenge: | Currently, research on complex chart understanding tasks is limited . a pipeline for visual reasoning datasets addresses these limitations . |
| Approach: | They propose a code-driven pipeline for generating visual reasoning datasets . pipeline integrates retrieval-augmented generation to retrieve professional chart templates . |
| Outcome: | The proposed pipeline enhances chart diversity and data quality through model-based evaluation. |
An Empirical Study of Synthetic Data Generation for Implicit Discourse Relation Recognition (2024.lrec-main)
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| Challenge: | Existing methods to recognize implicit discourse relations are limited by the lack of training data. |
| Approach: | They propose a method to generate synthetic data for IDRR using a large language model . they extract confused discourse relation pairs based on false negative rate and use two-stage prompting to generate effective synthetic data. |
| Outcome: | The proposed method achieves state-of-the-art macro-F1 performance without sacrificing micro-F1. |
Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation (2023.findings-acl)
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| Challenge: | Multimodal machine translation (MMT) aims to improve translation quality by incorporating information from other modalities, such as vision. |
| Approach: | They propose a framework for multimodal machine translation that utilizes large-scale non-triple data and a multimodal translation dataset. |
| Outcome: | The proposed method can significantly improve translation performance with more non-triple data. |
SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization (2025.coling-main)
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| Challenge: | Existing methods to reduce the adverse effect of annotation errors are time-consuming because they require many trained models to detect errors. |
| Approach: | They propose a method that uses a tokenization technique called subword regularization to simulate multiple error detection models for detecting errors. |
| Outcome: | The proposed method performs weighting weighting four to five times faster than existing methods and improves in document classification and named entity recognition tasks. |
DocLens: Multi-aspect Fine-grained Medical Text Evaluation (2024.acl-long)
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Yiqing Xie, Sheng Zhang, Hao Cheng, Pengfei Liu, Zelalem Gero, Cliff Wong, Tristan Naumann, Hoifung Poon, Carolyn Rose
| Challenge: | Medical text generation systems are widely used to assist with administrative work and highlight salient information to support decision-making. |
| Approach: | They propose a set of metrics to evaluate completeness, conciseness, and attribution of medical text at a fine-grained level. |
| Outcome: | The proposed framework exhibits substantially higher agreement with medical experts than existing metrics. |
Distilling Knowledge Learned in BERT for Text Generation (2020.acl-main)
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| Challenge: | Large-scale pre-trained language models such as BERT have revolutionized the state of the art in many language understanding tasks. |
| Approach: | They propose a conditional masked language modeling approach to fine tune BERT on target generation tasks by imposing global sequence-level supervision on conventional Seq2Seq models. |
| Outcome: | The proposed model outperforms strong Transformer baselines on multiple language generation tasks such as machine translation and text summarization. |
Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework (2024.findings-emnlp)
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| Challenge: | Existing studies on retrieval-augmented generation (RAG) rarely address the issue of predictive uncertainty, i.e., how likely it is that a RAG model’s prediction is incorrect. |
| Approach: | They propose a framework that induces RAG models to alter latent factors and analyzes the effect on their answers. |
| Outcome: | The proposed framework identifies two critical factors affecting RAG models' confidence in their answers and analyzes the effect on their answers. |
Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs (2026.findings-acl)
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| Challenge: | Existing training-free methods are vulnerable to the attention sink phenomenon . Existing methods include contrastive decoding and auxiliary expert models . |
| Approach: | They propose a training-free attention intervention that constructs a PAD map to identify semantically core visual regions and applies per-head Median Absolute Deviation Scaling to adaptively control the intervention strength. |
| Outcome: | The proposed intervention improves visual grounding and reduces hallucinations on multiple LVLMs and benchmarks. |
Rhetorically Controlled Encoder-Decoder for Modern Chinese Poetry Generation (P19-1)
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| Challenge: | Rhetoric is a vital element in modern Chinese poetry, and plays an essential role in improving its aesthetics. however, to date, it has not been considered in research on automatic poetry generation. |
| Approach: | They propose a rhetorically controlled encoder-decoder for modern Chinese poetry generation . their model captures various rhetorical patterns in an encoder and incorporates mixtures . |
| Outcome: | The proposed model outperforms state-of-the-art methods in terms of fluency, coherence, meaningfulness, and rhetorical aesthetics. |
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models (2024.acl-long)
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Jinhao Duan, Hao Cheng, Shiqi Wang, Alex Zavalny, Chenan Wang, Renjing Xu, Bhavya Kailkhura, Kaidi Xu
| Challenge: | Large Language Models (LLMs) show promising results in language generation but often “hallucinate”, making their outputs less reliable. |
| Approach: | They propose to shift attention to more relevant components at token- and sentence-levels for better UQ. |
| Outcome: | The proposed approach improves the performance of a range of popular “off-the-shelf” LLMs with model sizes extending up to 33B parameters. |
Seq1F1B: Efficient Sequence-Level Pipeline Parallelism for Large Language Model Training (2025.naacl-long)
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| Challenge: | Current PP methods face severe bottlenecks, including pipeline bubbles and memory footprint. |
| Approach: | They propose a sequence-level one-forward-one-backward (1F1B) PP method for training LLMs on long sequences with high throughput and memory efficiency. |
| Outcome: | The proposed method achieves 1.14X training throughput with half memory footprint compared to baseline methods . it trains an LLM with 30B parameters on sequences up to 64k tokens using 64X NVIDIA A100 GPUs . |
Unsupervised Learning of Hierarchical Conversation Structure (2022.findings-emnlp)
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| Challenge: | Goal-oriented conversations often have sub-dialogue structure, but it can be domain-dependent . Increasingly, language understanding applications involve conversational speech and text . |
| Approach: | They propose an unsupervised approach to learning hierarchical conversation structure . they use turn and sub-dialogue segment labels to decode the structure based on dialogue acts and subtasks . |
| Outcome: | The proposed approach improves neural models for three conversation-level understanding tasks. |
Formally Specifying the Intended Behavior of the Program: LLM-Driven Neuro-Symbolic Program Specification Synthesis (2026.acl-demo)
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Cheng Wen, Hu Junjie, YiKun Hu, Jie Su, Bin Yu, Dugang Liu, Zhiwu Xu, Weidi Sun, Shengchao Qin, Cong Tian
| Challenge: | Formal verification typically requires developers to write detailed formal specifications . a formal verification system that generates candidate specifications is costly and error-prone . |
| Approach: | They propose an LLM-driven neuro-symbolic demonstration system that reframes specification writing as constrained structured synthesis. |
| Outcome: | The proposed system reduces hallucinations and produces proof-ready annotations. |
MPRF: Interpretable Stance Detection through Multi-Path Reasoning Framework (2025.emnlp-main)
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| Challenge: | Existing stance detection methods treat the task as a classification problem, where models output a stance label without providing interpretable reasoning paths. |
| Approach: | They propose a framework that generates, evaluates, and integrates multiple reasoning paths to improve accuracy, robustness, and transparency in stance detection. |
| Outcome: | The proposed framework outperforms existing models on the SEM16, VAST, and PStance datasets and is highly interpretable and reliable. |
Can Post-Training Quantization Benefit from an Additional QLoRA Integration? (2025.naacl-industry)
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| Challenge: | Large language models require considerable computing resources, which can be costly and often unavailable. |
| Approach: | They propose to integrate 4-bit Post-training Quantization with QLoRA to address these issues . they demonstrate that the integration outperforms standard quantization and fine-tuning . |
| Outcome: | The proposed integration outperforms standard PTQ and 16-bit full-parameter fine-tuning on LLMs. |
PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy (2025.acl-long)
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| Challenge: | Existing pipelines that combine document image restoration with semantic-aware post-OCR correction can improve text extraction from degraded images. |
| Approach: | They propose a two-stage pipeline that combines document image restoration with semantic-aware post-OCR correction to enhance both visual clarity and textual consistency. |
| Outcome: | The proposed pipeline reduces character error rates by 63.9-70.3% on 13,831 pages of real historical documents in English, French, and Spanish compared to OCR on raw images. |
DLTKG: Denoising Logic-based Temporal Knowledge Graph Reasoning (2025.findings-emnlp)
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| Challenge: | Current approaches to temporal knowledge representation face limited generalization to unseen facts and insufficient interpretability of reasoning processes. |
| Approach: | They propose a framework that uses a denoising diffusion process to complete reasoning tasks . they propose introducing a noise source and historical conditionguiding mechanism to improve interpretability . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three benchmark datasets. |
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. |
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents (2024.acl-long)
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Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Zhong Zhang, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Current language model-driven agents lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. |
| Approach: | They propose a benchmark to inspect users’ implicit intentions through explicit queries and a model expert as the upstream in agent design to enhance user-agent interaction. |
| Outcome: | The proposed approach excels at identifying vague user tasks, recovering and summarizing critical missing information, setting precise and necessary agent execution goals, and minimizing redundant tool usage, thus boosting overall efficiency. |
Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model (2022.emnlp-main)
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| Challenge: | Existing methods for improving multilingual models did not focus on learning the semantic structure of representation. |
| Approach: | They propose a method to improve multilingual language models by aligning parallel sentences . they propose token-, word-, sentence- and structure-level alignment objectives . |
| Outcome: | The proposed method outperforms baseline models on XNLI, PAWS-X, and XQuAD . it obtains comparable performance on low-resource languages, the authors show . |
Early-Exit and Instant Confidence Translation Quality Estimation (2026.eacl-long)
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| Challenge: | Quality estimation models are often opaque and computationally expensive, making them impractical to be part of large-scale pipelines. |
| Approach: | They propose an uncertainty-aware quality estimation model that matches previous approaches at a fraction of their costs. |
| Outcome: | The proposed method reduces evaluation costs by 50% and improves reranking performance. |
Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining (2026.findings-acl)
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| Challenge: | Conformal prediction (CP) has shown promise in offering correctness guarantees for LLMs, but it faces major challenges in continual domain pretraining (CDP). |
| Approach: | They propose an adaptive rejection and non-exchangeable CP framework that allows the LLM to selectively abstain from answering when its confidence or competence shifts significantly. |
| Outcome: | Experiments show that the proposed framework improves performance under continuous domain pretraining scenarios. |
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)
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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang
| Challenge: | Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability. |
| Approach: | They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs . |
| Outcome: | The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks. |
Uncertainty Quantification for Evaluating Gender Bias in Machine Translation (2026.findings-eacl)
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| Challenge: | Existing models can reproduce existing social inequalities but cannot be reduced. |
| Approach: | They propose that models should maintain uncertainty when input is ambiguous to avoid reinforcing biases. |
| Outcome: | The proposed model can detect gender bias when translated to ambiguous and unambiguous sources and shows that it does not correlate with high translation accuracy and debiases the two cases differently. |
ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks (2024.findings-naacl)
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| Challenge: | Existing static benchmarks do not guarantee that models can use the provided evidence for answering, which is essential to avoid hallucination when the required knowledge is new or private. |
| Approach: | They propose to automatically perturb existing static one for dynamic evaluation by using a chatGPT framework and a set of open-domain QA datasets. |
| Outcome: | The proposed framework generates new test cases on two open-domain QA datasets and is human-readable and useful to trigger hallucination in LLMs. |
Explicit Memory Learning with Expectation Maximization (2024.emnlp-main)
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| Challenge: | Large Language Models lack reliable learning mechanisms for updating information across interactions. |
| Approach: | They propose a framework that enhances explicit memory updates via the Expectation-Maximization algorithm. |
| Outcome: | The proposed framework outperforms existing methods without memory or with static external memory on streaming inference tasks. |
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language Models (2024.findings-acl)
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Linhao Yu, Yongqi Leng, Yufei Huang, Shang Wu, Haixin Liu, Xinmeng Ji, Jiahui Zhao, Jinwang Song, Tingting Cui, Xiaoqing Cheng, Liutao Liutao, Deyi Xiong
| Challenge: | Recent years have witnessed remarkable progress achieved by large language models in both natural language understanding and generation. |
| Approach: | They propose a large benchmark CMoralEval for moral evaluation of Chinese LLMs . they use a Chinese TV program discussing Chinese moral norms and Chinese moral anomies based on various sources . |
| Outcome: | The proposed dataset is characterized by diversity and authenticity. |
Weakly-Supervised Spoken Video Grounding via Semantic Interaction Learning (2023.acl-long)
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| Challenge: | Recent work on spoken video grounding challenges extracting semantic information from speech . previous studies focused on textual queries, but recent work focuses on spoken queries . |
| Approach: | They propose a framework for weakly-supervised spoken video grounding to represent cross-modal semantics without expensive temporal annotations. |
| Outcome: | The proposed framework is more efficient than existing methods. |
Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training (2026.acl-long)
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| Challenge: | Existing methods for reweighting data mixtures rely on manual designation with certain heuristics based on intuition or empirical results. |
| Approach: | They propose a model-based framework that learns to re-weight domains by reinforcement learning on large quantities of data mixing trajectories with corresponding feedback from an evaluation environment. |
| Outcome: | The proposed framework outperforms baselines in achieving balanced performance across source and target fields and domain spaces without retraining. |
Knowledge-enhanced Prompt Tuning for Dialogue-based Relation Extraction with Trigger and Label Semantic (2024.lrec-main)
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| Challenge: | Existing methods to determine semantic relation between two arguments in dialogues are limited due to the low information density of text. |
| Approach: | They propose a Knowledge-Enhanced Prompt-Tuning method to enhance DRE model by exploiting trigger and label semantics. |
| Outcome: | The proposed method achieves state-of-the-art in F1 and F1c scores on a DialogRE dataset. |
Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment (2023.findings-emnlp)
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| Challenge: | Multi-modal entity alignment (MMEA) is a critical task that aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs). |
| Approach: | They propose a novel MMEA transformer that hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance alignment task. |
| Outcome: | The proposed transformer hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance the alignment task. |
Developing a Production System for Purpose of Call Detection in Business Phone Conversations (2022.naacl-industry)
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| Challenge: | a commercial system detects Purpose of Call statements in call transcripts . the model is based on a set of rules and a neural model . |
| Approach: | They propose a system to detect Purpose of Call statements in English business call transcripts in real time. |
| Outcome: | The proposed model achieves 88.6 F1 on average in various types of business calls and has low inference time. |
LPNL: Scalable Link Prediction with Large Language Models (2024.findings-acl)
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| Challenge: | Existing studies on graph learning with large language models have focused on the link prediction task on large graphs. |
| Approach: | They propose a framework for scalable link prediction on large-scale heterogeneous graphs based on large language models. |
| Outcome: | The proposed framework outperforms baselines in link prediction tasks on large graphs. |
Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases (2020.coling-main)
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| Challenge: | Existing methods for question generation over knowledge bases have low diversity and poor fluency due to the limited information contained in the subgraphs and semantic drift due to decoder’s oblivion of the semantics of the answer entity. |
| Approach: | They propose a knowledge-enriched, type-constrained and grammar-guided KBQG model that generates natural-language questions over a set of triples in the KB. |
| Outcome: | The proposed model outperforms existing methods on two widely-used benchmark datasets. |
UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions (2026.acl-long)
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Chunyu Qiang, Xiaopeng Wang, Kang Yin, Yuzhe Liang, Yuxin Guo, Teng Ma, Ziyu Zhang, Tianrui Wang, Cheng Gong, Yushen Chen, Ruibo Fu, Longbiao Wang, Jianwu Dang
| Challenge: | Generative audio modeling has been fragmented into specialized tasks such as text-to-speech (TTS), text- to-music (TTM), and text-ta (TTA) specialized models require reference audio for timbre cloning and strict phoneme alignment, whereas TTA models generate unstructured textures from open-ended captions. |
| Approach: | They propose a unified flow-matching framework capable of synthesizing speech, music, sound effects . they propose 'token injection mechanism' that projects unstructured environmental sounds into structured temporal latent space . |
| Outcome: | The proposed framework achieves state-of-the-art performance in instruction-based TTS and TTM while maintaining competitive fidelity in TTA. |
GraphSynth: Resolving the Diversity-Reliability Trade-off with Probabilistic Factor Graphs (2026.acl-long)
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| Challenge: | Large language models are a scaleable solution for the generation of synthetic data . however, the utility of such data is capped by a critical tension between diversity and factual reliability. |
| Approach: | They propose a framework which leverages a probabilistic factor graph modeling the universe of attributes. |
| Outcome: | The proposed framework outperforms state-of-the-art models with a high structural integrity and a boost in performance on downstream tasks. |
The Mirage of Model Editing: Revisiting Evaluation in the Wild (2025.acl-long)
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| Challenge: | despite near-perfect results, effectiveness of model editing in real-world applications remains unclear. |
| Approach: | They propose QAEdit and WILD to better reflect real-world use of model editing . they propose a benchmark aligned with widely used question answering datasets and a task-agnostic evaluation framework . |
| Outcome: | The proposed QAEdit benchmark and WILD evaluation framework show that current models perform worse than previously reported. |
Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making (2023.findings-acl)
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| Challenge: | Pre-trained language models (PLMs) have shown strong potential in various downstream tasks. |
| Approach: | They propose to model adversarial attack task as a sequential decision-making problem where the whole attack process is sequential with two decision- making problems, i.e., word finder and word substitution. |
| Outcome: | The proposed approach achieves the highest attack success rate with a comparable modification rate and semantic similarity to attack fine-tuned BERT. |
Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain (2024.findings-acl)
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| Challenge: | Recent large language models like GPT-4 have demonstrated astonishing zero-shot capabilities in general domain tasks, but they often generate content with hallucinations in specific domains such as Chinese law. |
| Approach: | They propose a framework for adapting large language models (LLMs) to Chinese legal domains by reformulating generation as an adapt-retrieve-revise process. |
| Outcome: | The proposed framework outperforms existing models in the Chinese legal domain by +33.6 points in the zero-shot setting. |
From Word to World: Can Large Language Models be Implicit Text-based World Models? (2026.acl-long)
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Yixia Li, Hongru Wang, Jiahao Qiu, Zhenfei Yin, Dongdong Zhang, Cheng Qian, Zeping Li, Xiaoteng Ma, Guanhua Chen, Heng Ji
| Challenge: | Agentic learning increasingly hinges on interaction, yet real-world experience is expensive, limited, and often irreversible at inference time. |
| Approach: | They propose a framework that reframes language modeling as next-state prediction under interaction. |
| Outcome: | The proposed framework evaluates world models in text-based environments . it shows that sufficiently trained models capture coherent environment dynamics . |
Enhancing Dialogue Generation with Conversational Concept Flows (2023.findings-eacl)
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| Challenge: | Existing studies show that explicitly modeling concept flows with a large commonsense knowledge graph improves response quality, but there is a gap between the knowledge graph and the conversation. |
| Approach: | They propose to model human conversational concept flows with a commonsense knowledge graph . they extract abundant concepts and relations from natural conversations and build a conversation-aware knowledge graph. |
| Outcome: | The proposed method performs better than baselines on a large-scale reddit conversation dataset. |
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm (2024.findings-acl)
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Yuanzhen Xie, Xinzhou Jin, Tao Xie, Matrixmxlin Matrixmxlin, Liang Chen, Chenyun Yu, Cheng Lei, Chengxiang Zhuo, Bo Hu, Zang Li
| Challenge: | In-context learning of large-language models has achieved remarkable success in the field of natural language processing . however, the single-step chain-of-thought prompting approach faces challenges such as attention diffusion and inadequate performance in complex tasks like text-to-SQL. |
| Approach: | They propose a workflow paradigm method to enhance the attention and problem-solving scope of large-language models through decomposition. |
| Outcome: | The proposed method outperforms existing methods on three datasets and improves the upper limit of LLM-based approaches. |
Uncertainty-Aware Test-Time Search for Optimization Problem Solving (2026.acl-long)
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Linlin Yu, Xujiang Zhao, Dong Li, Yanchi Liu, Wei Cheng, Zhengzhang Chen, Chen Zhao, Feng Chen, Haifeng Chen
| Challenge: | Language model hallucinations and limited availability of labeled datasets often result in misaligned formulations, code errors and feasibility failures. |
| Approach: | They propose a Monte Carlo Tree Search framework that automates optimization problems from natural language descriptions with efficiency and reliability. |
| Outcome: | The proposed framework achieves state-of-the-art solution accuracy and reduces token usage. |
Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus (2023.emnlp-main)
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Tianhang Zhang, Lin Qiu, Qipeng Guo, Cheng Deng, Yue Zhang, Zheng Zhang, Chenghu Zhou, Xinbing Wang, Luoyi Fu
| Challenge: | Existing methods for detecting hallucinations in LLMs rely on external knowledge for reference retrieval or require sampling multiple responses for consistency verification. |
| Approach: | They propose a reference-free, uncertainty-based method for detecting hallucinations in Large Language Models that imitates human focus in factuality checking from three aspects: focus on the most informative keywords; focus on unreliable tokens in historical context; focus of token properties such as token type and token frequency. |
| Outcome: | The proposed method achieves state-of-the-art performance across all evaluation metrics and eliminates the need for additional information. |
Learning to Match Representations is Better for End-to-End Task-Oriented Dialog System (2024.findings-emnlp)
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| Challenge: | Existing systems for task-oriented dialogue lack belief states as supervisory signals. |
| Approach: | They propose a method for knowledge retrieval driven by matching representations . they use a matching signal extractor to extract matching representation between contexts and entities . |
| Outcome: | Experiments on three standard benchmarks show that the proposed method performs better than existing approaches. |
SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents (2024.acl-long)
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| Challenge: | Existing GUI agents interact with the environment through extracted structured data, which can be notably lengthy (e.g., HTML) and occasionally inaccessible (e-book). |
| Approach: | They propose to enhance SeeClick with GUI grounding pre-training and devise a method to automate curation of GUI ground data. |
| Outcome: | The proposed agent improves ScreenSpot, the first realistic GUI grounding benchmark that encompasses mobile, desktop, and web environments. |
ToolSpectrum: Towards Personalized Tool Utilization for Large Language Models (2025.findings-acl)
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| Challenge: | Existing approaches focus on functional tool selection following user instructions while overlooking the critical role of context-aware personalization in tool selection. |
| Approach: | They propose a benchmark to evaluate LLMs’ capabilities in personalized tool utilization. |
| Outcome: | The proposed benchmark evaluates LLMs' capabilities in personalized tool utilization. |
Train Once, and Decode As You Like (2020.coling-main)
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| Challenge: | Existing approaches to machine translation support autoregressive, semi-autoregressive and refinement-based non-auto-regressives. |
| Approach: | They propose a unified approach for supporting different generation manners of machine translation including autoregressive, semi-autoregressive and refinement-based non-auto-regressives. |
| Outcome: | The proposed approach achieves better or competitive translation performance compared with strong baseline models in all the settings. |
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion (2021.findings-acl)
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| Challenge: | Existing studies focus on partial aspects of knowledge abstraction, concretization, and completion (KACC). |
| Approach: | They propose a unified knowledge graph benchmark to improve existing benchmarks . they collect new datasets that contain larger concept graphs and cross-view links . |
| Outcome: | The proposed benchmark improves existing benchmarks in terms of dataset scale, task coverage, and difficulty. |
Causality-Guided Multi-Memory Interaction Network for Multivariate Stock Price Movement Prediction (2023.acl-long)
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| Challenge: | Existing models for stock price movement prediction use auxiliary data, but we assume other stocks should be utilized as auxiliary information to enhance performance. |
| Approach: | They propose a Causality-guided multi-memory interaction network for stock movement prediction which transforms basic attention into Causal Attention by calculating transfer entropy between multivariate stocks. |
| Outcome: | The proposed model outperforms existing models on three real-world datasets from the U.S. and Chinese markets. |
Balancing Fidelity and Plasticity: Aligning Mixed-Precision Fine-Tuning with Linguistic Hierarchies (2026.findings-acl)
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Changhai Zhou, Shiyang Zhang, Yuhua Zhou, Jun Gao, Qian Qiao, Shichao Weng, Weizhong Zhang, Cheng Jin
| Challenge: | Existing quantization-aware fine-tuning methods decouple weight precision and adapter capacity, overlooking that a layer’s ability to adapt is constrained by the information preserved in its frozen weights. |
| Approach: | They propose a framework that jointly optimizes per-layer quantization bit-width and LoRA rank. |
| Outcome: | Experiments on LLaMA and Qwen models show that the proposed framework matches or approaches 16-bit baselines while using substantially less memory. |
BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning (2022.coling-1)
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| Challenge: | Existing approaches to AMR focus on one-side improvements despite the duality of the two tasks . instead, we propose data-efficient Bidirectional Bayesian learning (BiBL) to facilitate bidirectional information transition. |
| Approach: | They propose a data-efficient bidirectional Bayesian learning approach to facilitate bidirectional information transition by adopting a single-stage multitasking strategy. |
| Outcome: | The proposed model outperforms existing models on benchmark datasets without extra training data. |
MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models (2025.coling-main)
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| Challenge: | Existing studies on knowledge editing focus on monolingual scenarios, neglecting the complexities presented by multilingual contexts and multi-hop reasoning. |
| Approach: | They propose a benchmark to evaluate the adaptability of multilingual knowledge editing methods. |
| Outcome: | The proposed benchmark evaluates the adaptability of multilingual knowledge editing methods across five languages. |
Textual Enhanced Contrastive Learning for Solving Math Word Problems (2022.findings-emnlp)
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| Challenge: | Recent studies show that current models rely on shallow heuristics to predict solutions . a textual Enhanced Contrastive Learning framework enforces the models to distinguish semantically similar examples while holding different mathematical logic. |
| Approach: | They propose a textual Enhanced Contrastive Learning framework which enforces models to distinguish semantically similar examples while holding different mathematical logic. |
| Outcome: | The proposed framework improves on benchmark and challenge datasets in English and Chinese. |
Continuous Speech Tokenizer in Text To Speech (2025.findings-naacl)
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| Challenge: | Autoregressive modeling is a common method for processing language sequences and is effective in token prediction. |
| Approach: | They propose a text-to-speech model based on continuous speech tokens and a continuous tokenizer for speech compression. |
| Outcome: | The proposed model has better continuity and higher estimated Mean Opinion Scores (MoS) this is attributed to better information preservation rate across low and high frequencies in the frequency domain. |
Novel Relation Detection: Discovering Unknown Relation Types via Multi-Strategy Self-Supervised Learning (2023.findings-emnlp)
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| Challenge: | Existing approaches to relation extraction can only recognize predefined relation types . new or out-of-scope relation types may continually emerge after the model is deployed . |
| Approach: | They propose a novel relation detection task that uses self-supervised learning to handle shallow semantic similarity problem. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two datasets. |
Data Efficient RLVR via Off-Policy Influence Guidance (2026.acl-long)
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Erle Zhu, Dazhi Jiang, Yuan Wang, Xujun Li, Jiale Cheng, Yuxian Gu, Yilin Niu, Aohan Zeng, Jie Tang, Minlie Huang, Hongning Wang
| Challenge: | Existing data selection methods for RLVR are heuristic-based, lacking theoretical guarantees and generalizability. |
| Approach: | They propose an off-policy influence estimation method that approximates data influence using offline trajectories. |
| Outcome: | The proposed method reduces the computational cost of policy rollouts and improves storage and computation efficiency. |
NOSE: Neural Olfactory-Semantic Embedding with Tri-Modal Orthogonal Contrastive Learning (2026.acl-long)
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| Challenge: | Existing representation methods fail to fully capture olfactory pathway . current approaches focus on isolated segments of the olefactory pathways . |
| Approach: | They propose a representation learning framework that aligns molecular structure, receptor sequence, and natural language description. |
| Outcome: | The proposed framework achieves state-of-the-art and excellent zero-shot generalization . it decouples contributions of molecular structure, receptor sequence, and natural language description . |
DAEA: Enhancing Entity Alignment in Real-World Knowledge Graphs Through Multi-Source Domain Adaptation (2025.coling-main)
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| Challenge: | Entity Alignment (EA) is a critical task in Knowledge Graph (KG) integration. |
| Approach: | They propose a novel approach that leverages the data characteristics of synthetic benchmarks to improve performance in real-world datasets. |
| Outcome: | The proposed approach outperforms state-of-the-art models on real-world datasets and achieves a 29.94% improvement in Hits@1 on DOREMUS and 5.64% improvement on AGROLD. |
Improving Multi-turn Dialogue Modelling with Utterance ReWriter (P19-1)
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| Challenge: | Recent research has achieved impressive results in single-turn dialogue modelling, but multi-turn models still remain challenging. |
| Approach: | They propose to rewrite human utterances as a pre-process to help multi-turn dialgoue modelling. |
| Outcome: | The proposed architecture achieves remarkably good performance on the utterance rewriting task. |
A Computational Method for Measuring Open Codes in Qualitative Analysis (2026.findings-acl)
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John Chen, Alexandros Nikolaos Lotsos, Sihan Cheng, Lexie Zhao, Yanjia Zhang, Jessica Hullman, Bruce Sherin, Uri Wilensky, Michael Horn
| Challenge: | Qualitative analysis is widely adopted across many social science disciplines. |
| Approach: | They propose a theory-informed computational method for measuring inductive coding results from humans and GAI. |
| Outcome: | The proposed method captures breadth, consensus, unique contribution, and systematic deviation without assuming ground truth. |
Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languages (2024.emnlp-main)
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| Challenge: | Existing zero-shot cross-lingual NER approaches require substantial prior knowledge of the target language, which is impractical for low-resource languages. |
| Approach: | They propose a phonemic representation based on the International Phonetic Alphabet (IPA) to bridge the gap between representations of different languages. |
| Outcome: | The proposed method outperforms baseline models in low-resource languages with highest average F1 score and lowest standard deviation. |
PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning (2026.acl-long)
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Jingcheng Hu, Yinmin Zhang, Shijie Shang, Xiaobo Yang, Yue Peng, Zhewei Huang, Hebin Zhou, Xin Wu, Jie Cheng, Fanqi Wan, Xiangwen Kong, Chengyuan Yao, Kaiwen Yan, Ailin Huang, Hongyu Zhou, Qi Han, Zheng Ge, Xiangyu Zhang, Heung-Yeung Shum
| Challenge: | Parallel Coordinated Reasoning (PaCoRe) overcomes a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window. |
| Approach: | They propose a training-and-inference framework to overcome a central limitation of language models: their inability to scale test-time compute (TTC) under a fixed context window. |
| Outcome: | The proposed model scales to multi-million-token effective TTC without exceeding context limits. |
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation (2023.findings-acl)
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Liyan Kang, Luyang Huang, Ningxin Peng, Peihao Zhu, Zewei Sun, Shanbo Cheng, Mingxuan Wang, Degen Huang, Jinsong Su
| Challenge: | Existing datasets focus on captions describing images or videos, which are not large and diverse enough. |
| Approach: | They propose a large-scale video subtitle translation dataset to facilitate multi-modality machine translation. |
| Outcome: | The proposed dataset is 10 times larger than the widely used *How2* and *VaTeX* datasets. |
MovieCORE: COgnitive REasoning in Movies (2025.emnlp-main)
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Gueter Josmy Faure, Min-Hung Chen, Jia-Fong Yeh, Ying Cheng, Hung-Ting Su, Yung-Hao Tang, Shang-Hong Lai, Winston H. Hsu
| Challenge: | MovieCORE is a video question answering dataset that focuses on surface-level comprehension. |
| Approach: | They propose a video question-answer dataset that uses large language models as thought agents to generate and refine high-quality question-anchor pairs. |
| Outcome: | The proposed model improves model reasoning capabilities post-training by 25% . the proposed model is based on a large language model and is scalable to a wide range of tasks . |
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation (2022.findings-acl)
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| Challenge: | a new text generation dataset is needed to controllable text summarization, but it lacks the domain knowledge. |
| Approach: | They propose to use existing text generation datasets to leverage input and control signals . they propose to annotate each meta-review sentence manually with a control signal . |
| Outcome: | The proposed method can be used to control the structure of a text generation dataset . it can be applied to a variety of tasks, including a task with a large number of meta-review sentences . |
MasRouter: Learning to Route LLMs for Multi-Agent Systems (2025.acl-long)
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| Challenge: | Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often face significant costs and challenges in dynamic LLM selection. |
| Approach: | They propose a multi-agent system routing solution that integrates all components of MAS into a unified routing framework. |
| Outcome: | The proposed solution is high-performing, cost-effective, and efficient . it reduces overhead by up to 52.07 compared to current methods on HumanEval . |
InstructSafety: A Unified Framework for Building Multidimensional and Explainable Safety Detector through Instruction Tuning (2023.findings-emnlp)
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| Challenge: | Existing safety detection systems have limitations in terms of their versatility and interpretability. |
| Approach: | They introduce a safety detection framework that unifies 7 common sub-tasks into a uniform formulation and process 39 human-annotated datasets for instruction tuning. |
| Outcome: | The proposed framework unifies 7 common sub-tasks into a uniform formulation and then runs on 39 human-annotated datasets to fine-tune it. |
PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models (2026.findings-acl)
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| Challenge: | Existing reward models evaluate empathy from a single perspective, overlooking bidirectional interaction nature of empathy. |
| Approach: | They propose a reward model that evaluates empathy from a single perspective . they propose PERM to integrate a bystander perspective to monitor overall interaction quality . |
| Outcome: | a new reward model outperforms state-of-the-art models on an emotional intelligence benchmark and an industrial daily conversation dataset. |
Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding (2021.acl-long)
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| Challenge: | Argument pair extraction (APE) is a research task for extracting arguments from two passages and identifying potential argument pairs. |
| Approach: | They propose a novel attention-guided multi-layer multi-cross encoding scheme that processes two passages with two individual sequence encoders and updates their representations using each other’s attention. |
| Outcome: | The proposed model significantly improves the performance over several alternatives. |
Breaking the Noise Barrier: LLM-Guided Semantic Filtering and Enhancement for Multi-Modal Entity Alignment (2025.emnlp-main)
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| Challenge: | Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multimodal knowledge graphs. |
| Approach: | They propose a novel LLMguided MMEA framework that prioritizes noise reduction before fusion. |
| Outcome: | The proposed framework prioritizes noise reduction before fusion and improves semantics on the noisy FB YG dataset. |
Towards Robust Few-Shot Relation Classification: Incorporating Relation Description with Agreement (2025.findings-emnlp)
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Mengting Hu, Jianfeng Wu, Ming Jiang, Yalan Xie, Zhunheng Wang, Rui Ying, Xiaoyi Liu, Ruixuan Xu, Hang Gao, Renhong Cheng
| Challenge: | Existing approaches to recognize relational relationships with a few support samples are limited for unlimited queries. |
| Approach: | They propose a simple but effective framework that uses relation descriptions as external knowledge to enhance the model’s comprehension of the relation semantics. |
| Outcome: | The proposed framework outperforms strong baselines while being robust against various NOTA rates. |
Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting (2021.acl-long)
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| Challenge: | Existing QG systems perform substantially worse in answering multi-hop questions than single-hop ones. |
| Approach: | They propose a framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. |
| Outcome: | The proposed framework increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. |
SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)
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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke
| Challenge: | Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis. |
| Approach: | They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis. |
| Outcome: | SciAssess evaluates 11 LLMs on multiple tasks across scientific fields. |
From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications (2024.findings-acl)
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| Challenge: | Existing evaluation metrics for large language models yield numerical scores that ignore user experience. |
| Approach: | They propose a metric that suggests revision edits that mimic the human writing process . their results show that the metric offers more insightful feedback and distinguishes between texts . |
| Outcome: | The proposed metric can provide a self-explained text evaluation result in a human-understandable manner beyond the context-independent score. |
Efficient Sequence Learning with Group Recurrent Networks (N18-1)
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| Challenge: | Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation and speech recognition. |
| Approach: | They propose an efficient architecture to improve the efficiency of such RNN model training by adopting the group strategy for recurrent layers while exploiting the representation rearrangement strategy between layers as well as time steps. |
| Outcome: | The proposed architecture achieves comparable or better accuracy compared with baselines, with a much smaller number of parameters and at a lower computational cost. |
INSET: Sentence Infilling with INter-SEntential Transformer (2020.acl-main)
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| Challenge: | Missing sentence generation fosters a wide range of applications in natural language generation . Developing models for sentence infilling can potentially facilitate many text generation applications . |
| Approach: | They propose a framework to decouple the problem from natural language processing . they propose generating missing sentences that can syntactically and semantically bridge context . |
| Outcome: | The proposed model learns a sentence representation and generates 'missing sentences' the proposed model can be used for document auto-completion and meeting note expansion . |
Dynamic Attention-Guided Context Decoding for Mitigating Context Faithfulness Hallucinations in Large Language Models (2025.findings-acl)
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| Challenge: | Existing methods, such as a n-terminal coding, do not provide accurate data for large language models. |
| Approach: | They propose a lightweight framework that leverages attention distributions and uncertainty signals in a single-pass decoding. |
| Outcome: | Experiments on open-book QA datasets show that DAGCD improves faithfulness and robustness while preserving computational efficiency. |
ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision (2023.eacl-main)
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| Challenge: | Existing weakly-supervised methods for solving math word problems are expensive and time-consuming. |
| Approach: | They propose a weakly-supervised approach to solve math word problems . they propose 'comsearch' algorithm which compresses the search space by excluding mathematically equivalent equations. |
| Outcome: | The proposed algorithm can compress the search space by excluding mathematically equivalent equations. |
Do We Always Need Query-Level Workflows? Rethinking Agentic Workflow Generation for Multi-Agent Systems (2026.findings-acl)
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| Challenge: | Existing approaches generate workflows either at task level or query level, but their relative costs and benefits remain unclear. |
| Approach: | They propose a query-level workflow generation framework that generates tasks at task level and query level. |
| Outcome: | The proposed framework reduces token usage by up to 83% compared to existing approaches . it maintains competitive performance with an average degradation of just 0.61% compared with existing approaches across multiple datasets . |
Document-Level Relation Extraction with Global Relations and Entity Pair Reasoning (2025.findings-acl)
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| Challenge: | Existing document-level relation extraction models focus on individual entity pairs, limiting their ability to handle complex reasoning tasks. |
| Approach: | They propose a document-level relation extraction framework based on global relations and entity pair reasoning that captures fine-grained interactions between entity pairs. |
| Outcome: | The proposed framework outperforms existing models on widely-used datasets. |
Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization (2023.findings-emnlp)
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| Challenge: | ChatGPT and GPT-4 are popular as evaluation metric for complex generative tasks . however, they are not ready as human replacements due to significant limitations . |
| Approach: | They conduct extensive analysis to examine the stability and reliability of LLMs as automatic evaluators for abstractive summarization. |
| Outcome: | The proposed methods outperform the commonly used automatic metrics but are not ready for human evaluation due to significant limitations. |
Multi-Stage Pre-training for Automated Chinese Essay Scoring (2020.emnlp-main)
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| Challenge: | Existing methods for automatic essay scoring are based on hand-crafted surface-level features, but recent advances in representation learning have improved performance. |
| Approach: | They propose a pre-training based automated Chinese essay scoring method with weakly supervised pre- training, supervised cross- prompt fine-tuning and supervised target- prompt refine-tuneing. |
| Outcome: | The proposed method improves a state-of-the-art neural essay scorer in terms of effectiveness and domain adaptation ability, while in-depth analysis also reveals its limitations. |
Aggregating Multiple Heuristic Signals as Supervision for Unsupervised Automated Essay Scoring (2023.acl-long)
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| Challenge: | Automated Essay Scoring (AES) aims to evaluate the quality score of input essays without human intervention. |
| Approach: | They propose an unsupervised approach to evaluate the quality of input essays . they use multiple heuristic quality signals as pseudo-groundtruths to train a neural AES model . |
| Outcome: | The proposed approach achieves state-of-the-art performance on eight prompts of ASPA dataset compared with previous unsupervised methods . |
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning (2022.acl-long)
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Qin Liu, Rui Zheng, Bao Rong, Jingyi Liu, ZhiHua Liu, Zhanzhan Cheng, Liang Qiao, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to generating adversarial perturbations scale up the cost of training computational complexity by the number of gradient steps it takes to obtain the adversarials. |
| Approach: | They propose a flood method which aims at better generalization and a criterion to bring hyper-parameter-dependent flooding into effect with a narrowed-down search space by measuring how the gradient steps taken within one epoch affect the loss of each batch. |
| Outcome: | The proposed method improves BERT’s resistance to textual adversarial attacks by a large margin and achieves state-of-the-art robust accuracy on various text classification and GLUE tasks. |
ALiiCE: Evaluating Positional Fine-grained Citation Generation (2025.naacl-long)
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| Challenge: | Existing research on citation generation is limited to sentence-level statements . positional fine-grained citations can appear anywhere within sentences . |
| Approach: | They propose a framework that allows LLMs to generate citations from sentences . they use dependency tree-based methods to parse sentence-level claims into atomic claims . |
| Outcome: | The proposed framework evaluates citation quality using three metrics including positional fine-grained citation recall, precision, and coefficient of variation of citation positions. |
Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs (2026.acl-long)
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Yihua Zhu, Qianying Liu, Jiaxin Wang, Fei Cheng, Chaoran Liu, Akiko Aizawa, Sadao Kurohashi, Hidetoshi Shimodaira
| Challenge: | Autoregressive LLMs perform well on relational tasks that require linking entities via relational words, but it is unclear whether they learn the logical semantics of such relations or whether left-to-right order bias is involved. |
| Approach: | They propose a framework that generates text from symmetric/inverse triples and trains autoregressive models from scratch. |
| Outcome: | The proposed framework generates text from symmetric/inverse triples, trains autoregressive models from scratch, and evaluates memorization, logical inference, and in-context generalization to unseen entities. |
OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens (2025.acl-demo)
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Jiacheng Liu, Taylor Blanton, Yanai Elazar, Sewon Min, Yen-Sung Chen, Arnavi Chheda-Kothary, Huy Tran, Byron Bischoff, Eric Marsh, Michael Schmitz, Cassidy Trier, Aaron Sarnat, Jenna James, Jon Borchardt, Bailey Kuehl, Evie Yu-Yen Cheng, Karen Farley, Taira Anderson, David Albright, Carissa Schoenick, Luca Soldaini, Dirk Groeneveld, Rock Yuren Pang, Pang Wei Koh, Noah A. Smith, Sophie Lebrecht, Yejin Choi, Hannaneh Hajishirzi, Ali Farhadi, Jesse Dodge
| Challenge: | tracing language models' outputs back to training data is a problem because they are trained on text corpora with trillions of tokens . existing methods for tracers have not been scaled to work within this multi-trillion-token setting . |
| Approach: | They propose a system that traces language models' outputs verbatim back to training data . OLMOTRACE retrieves documents from the model's training data that contain exact matches . |
| Outcome: | The proposed system can find verbatim matches between LM output and training data . it can be used to explore fact checking, hallucination, and creativity of language models . |
Vision-Language Models Can Self-Improve Reasoning via Reflection (2025.naacl-long)
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| Challenge: | Chain-of-thought (CoT) has been shown to improve the reasoning capability of large language models (LLMs). |
| Approach: | They propose a framework which iteratively enhances the model’s Vision-language Reasoning by Reflecting on CoT Rationales. |
| Outcome: | The proposed framework improves multimodal reasoning on vision-language tasks by 23% to 60% over baselines. |
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. |
Not All Modalities at Once: Dynamic Dropout and Bidirectional Fusion for Robust Multi-modal Knowledge Graph Completion (2026.findings-acl)
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| Challenge: | Existing MKGC methods train with all modalities available, implicitly assuming consistent complementarity . however, this often induces modality dependence and modality competition under heterogeneous noise, which can hinder robust multi-modal fusion and limit overall performance. |
| Approach: | They propose a framework to infer missing links in multimodal knowledge graphs by leveraging structured triples together with auxiliary modalities such as text and images. |
| Outcome: | The proposed framework outperforms baselines and achieves new state-of-the-art results. |
The Engage Corpus: A Social Media Dataset for Text-Based Recommender Systems (2022.lrec-1)
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| Challenge: | Existing studies have examined the impact of recommendation algorithms on how users discover and join online groups, but there are few standardized datasets for generating such models. |
| Approach: | They propose to use Reddit to build a dataset that can be used to build models of user engagement with online groups. |
| Outcome: | The proposed model is based on the behavior of subreddits banned in June 2020 as part of Reddit's efforts to stop the dissemination of hate speech. |
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack (2020.coling-main)
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| Challenge: | Existing approaches to zero-shot slot filling ignore constraints in the latent space and lack robustness. |
| Approach: | They propose a Contrastive Zero-Shot Learning with Adversarial Attack method for slot filling . they propose to map slot value contextual representations to slot description representations . |
| Outcome: | The proposed method outperforms state-of-the-art models under zero-shot and few-shot settings. |
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling (2021.findings-acl)
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| Challenge: | Existing few-shot learning methods learn a single task each time with a few examples . but, real-world applications often contain multiple closely related tasks . |
| Approach: | They propose a few-shot joint learning scheme that captures intent and slot relationships from only a handful of examples and adapts the bridged metric space to specific few- shot domain. |
| Outcome: | The proposed model outperforms baseline models on two public datasets on intent and slot . the proposed model significantly outperformed baseline models in one and five shots settings. |
Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have emerged as powerful tools for a wide range of tasks, from * Equal Contribution. |
| Approach: | They propose a framework that enhances communication efficiency and task effectiveness in LLM-based multi-agent systems through training. |
| Outcome: | The proposed framework improves communication efficiency and task effectiveness on multi-agent tasks with 2.8x performance gain with less than 10% tokens on tasks requiring heavy information exchange. |
AudioVSR: Enhancing Video Speech Recognition with Audio Data (2024.emnlp-main)
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Xiaoda Yang, Xize Cheng, Jiaqi Duan, Hongshun Qiu, Minjie Hong, Minghui Fang, Shengpeng Ji, Jialong Zuo, Zhiqing Hong, Zhimeng Zhang, Tao Jin
| Challenge: | Recent work has shown poor performance with non-Indo-European languages . previous work primarily utilizes video information to build VSR models . |
| Approach: | They propose a generative model for data inflation that integrates synthetic data with authentic visual data to enhance the VSR model. |
| Outcome: | The proposed model improves on the audio-visual alignment problem in audio-video tasks. |
Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments (2024.findings-acl)
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Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
| Challenge: | Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graphs and tables. |
| Approach: | They propose a framework that allows LLMs to efficiently and faithfully reason over structured environments. |
| Outcome: | The proposed framework surpasses state-of-the-art fine-tuned methods on three KGQA and two TableQA datasets and surpasse CWQ and WTQ methods. |
DarwinTOD: LLM-Driven Lifelong Self-evolution for Task-oriented Dialog Systems (2026.acl-long)
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| Challenge: | Continual learning approaches fail to achieve autonomy lifelong improvement in dynamic environments . current task-oriented dialog systems are static, unable to learn from ongoing interactions . |
| Approach: | They propose a lifelong self-evolving dialog framework that integrates evolutionary computation and LLM driven self-improvement into a single framework. |
| Outcome: | The proposed framework surpasses state-of-the-art methods and exhibits continuous performance gains throughout evolution. |
Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation (2021.acl-long)
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| Challenge: | Existing methods require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains. |
| Approach: | They propose a framework that allows to transfer the knowledge of source domain to the unlabeled target domain without using source data. |
| Outcome: | The proposed framework matches distributions between a trained source model and a set of target data and achieves superior performance on cross-domain text classification. |
AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models (2024.findings-emnlp)
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Jiale Cheng, Yida Lu, Xiaotao Gu, Pei Ke, Xiao Liu, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Large Language Models (LLMs) exhibit significant but subtle weaknesses, such as mistakes in instruction-following or coding tasks. |
| Approach: | They propose a framework to automatically expose weaknesses in Large Language Models (LLMs) they use three LLM-powered agents to perform comprehensive weakness identification . |
| Outcome: | The proposed framework shows that it is more effective than untargeted data augmentation methods like Self-Instruct to identify weaknesses in LLMs. |
Distilling Large Embeddings via Hyperspherical Householder Quantization (2026.acl-long)
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| Challenge: | Existing methods for quantizing large embeddings rely on Euclidean quantization, which is poorly aligned with the angular geometry induced by contrastive embeddment training. |
| Approach: | They propose a geometry-aware distillation method that compresses large embeddings into short discrete representations via iterative Householder transformations on the unit hypersphere. |
| Outcome: | The proposed method reduces decoding cost and maintains strong semantic retrieval accuracy. |
RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing graph-based or hybrid systems lack the ability to integrate supplementary evidence as reasoning unfolds. |
| Approach: | They propose a framework that integrates non-parametric knowledge into Large Language Models . they use a RL-based framework to optimize the entire generation process via RL . |
| Outcome: | The proposed framework outperforms existing RAG frameworks in five question answering benchmarks. |
RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning (2023.findings-emnlp)
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| Challenge: | Recent studies have focused on producing concise observations while neglecting the precise attributes that determine the severity of diseases. |
| Approach: | They propose a model that generates precise radiology reports via dynamic disease progression reasoning by combining historical and spatiotemporal information. |
| Outcome: | Experiments on two publicly available datasets show the proposed model can generate precise and accurate radiology reports with dynamic disease progression reasoning. |
CARE: Causality Reasoning for Empathetic Responses by Conditional Graph Generation (2022.findings-emnlp)
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| Challenge: | Existing approaches to empathetic response generation only consider causalities between the user’s emotion and the user's experiences and neglect interdependence among causalities and reason them independently. |
| Approach: | They propose to use a conditional variable Graph Auto-Encoder to reason all plausible causalities interdependently and simultaneously given the user’s emotion, dialogue history, and future dialogue content. |
| Outcome: | The proposed method achieves state-of-the-art in a real-world situation. |
DialogGen: Multi-modal Interactive Dialogue System with Multi-turn Text-Image Generation (2025.findings-naacl)
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Minbin Huang, Yanxin Long, Xinchi Deng, Ruihang Chu, Jiangfeng Xiong, Xiaodan Liang, Hong Cheng, Qinglin Lu, Wei Liu
| Challenge: | Text-to-image (T2I) generation models have advanced in recent years, but effective interaction with these models is challenging for average users due to the need for specialized prompt engineering knowledge and the inability to perform multi-turn image generation. |
| Approach: | They propose to use off-the-shelf MLLMs and T2I models to build a multi-modal interactive dialogue system (MIDS) that can generate correct output modalities and coherence of output images. |
| Outcome: | The proposed pipeline can generate correct output modalities and coherent multi-modal outputs compared with other state-of-the-art models. |
UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference (2021.emnlp-main)
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| Challenge: | Knowledge graph inference has been studied extensively due to its wide applications. |
| Approach: | They propose a framework that restricts logical rules to be definite Horn rules and can exploit the knowledge in logical rule-based reasoning and KGE in an extremely efficient way. |
| Outcome: | The proposed framework can exploit the knowledge in logical rules and improve KGE in an extremely efficient way. |
Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models? (2024.emnlp-main)
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| Challenge: | Analogical reasoning plays a critical role in human cognition, enabling us to understand new concepts by associating them with familiar ones. |
| Approach: | They propose to use free-form analogies to aid students in understanding scientific concepts . they also show that analogies generated by student LMs can improve their own performance . |
| Outcome: | The proposed model can help students understand scientific concepts, the authors show . |
OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding (2026.acl-long)
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Deming Ding, Shichun Liu, Enhui Yang, Jiahang Lin, Ziying Chen, Shihan Dou, Honglin Guo, Weiyu Cheng, Pengyu Zhao, Chengjun Xiao, Qunhong Zeng, Qi Zhang, Xuanjing Huang, Qidi Xu, Tao Gui
| Challenge: | coding scaffolds that follow heterogeneous instructions remain under-examined in software engineering . coding models are capable software agents, but their ability to follow constraints remains under-explored . |
| Approach: | They introduce OctoBench, which benchmarks scaffold-aware instruction following in agentic coding. |
| Outcome: | The proposed benchmark aims to accelerate the development of more scaffold-aware agents. |
No Context Needed: Contextual Quandary In Idiomatic Reasoning With Pre-Trained Language Models (2024.naacl-long)
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| Challenge: | idiomatic expressions (IEs) are a non-compositional aspect of a text that makes it difficult for a model to comprehend . general purpose PTLMs are negatively affected by the context, as performance increases with its removal. |
| Approach: | They propose to use idiomatic expressions to infer additional meaning from IEs . they argue that only IE-aware models are suitable for idiom- matic reasoning tasks . |
| Outcome: | The proposed models can reason in the presence of idiomatic expressions, the authors show . they show that general purpose PTLMs are negatively affected by the context . |
WESR: A Benchmark and Strong Baseline for Word-level Event-Speech Recognition (2026.findings-acl)
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Chenchen Yang, Kexin Huang, Liwei Fan, Qian Tu, Botian Jiang, Dong Zhang, Linqi Yin, Shimin Li, Zhaoye Fei, Qinyuan Cheng, Xipeng Qiu
| Challenge: | aaron carroll: the precise localization of non-verbal vocal events remains a critical yet under-explored challenge. carroll says current methods suffer from insufficient task definitions with limited category coverage. carrol: knowing exactly where an event occurred is not enough; knowing exactly what it happened is. |
| Approach: | They propose a taxonomy of 21 vocal events with a new categorization into discrete versus continuous types. |
| Outcome: | The proposed model disentangles ASR errors from event detection while maintaining ASR quality. |
Document Segmentation Matters for Retrieval-Augmented Generation (2025.findings-acl)
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Zhitong Wang, Cheng Gao, Chaojun Xiao, Yufei Huang, Shuzheng Si, Kangyang Luo, Yuzhuo Bai, Wenhao Li, Tangjian Duan, Chuancheng Lv, Guoshan Lu, Gang Chen, Fanchao Qi, Maosong Sun
| Challenge: | Existing rule-based chunking methods lead to suboptimal splits, where overly large chunks introduce irrelevant information and small chunks lack semantic coherence. |
| Approach: | They propose a method that leverages document summaries as pseudo-instructions to guide chunking by computing semantic similarity between sentences and the summary. |
| Outcome: | Experiments on multiple open-domain question-answering benchmarks show that PIC significantly improves retrieval accuracy (Hits@k) and end-to-end QA performance (Exact Match) without any additional training. |
Judge as A Judge: Improving the Evaluation of Retrieval-Augmented Generation through the Judge-Consistency of Large Language Models (2025.findings-acl)
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Shuliang Liu, Xinze Li, Zhenghao Liu, Yukun Yan, Cheng Yang, Zheni Zeng, Zhiyuan Liu, Maosong Sun, Ge Yu
| Challenge: | Existing evaluation metrics cannot fairly evaluate the outputs of RAG models during training and evaluation. |
| Approach: | They propose a method which prompts LLMs to generate different judgments based on various combinations of judgment dimensions and utilizes the judge-consistency to evaluate these judgments. |
| Outcome: | The proposed method generates more accurate evaluations for RAG models across different RAG model and datasets. |
Two Streams, One Sarcasm: Orthogonal Expert Tuning for Holistic Multimodal Sarcasm Understanding (2026.acl-long)
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| Challenge: | Existing benchmarks for multimodal satirical cognition hinder evaluation of multimodal Sarcasm Understanding . lack of a unified benchmark for holistic satire cognition hampers evaluation of MSU . |
| Approach: | They propose a framework to decouple experts into orthogonal shared perception and private execution streams to physically block gradient interference between tasks. |
| Outcome: | The proposed framework achieves superior performance on DocMSU-PLUS. |
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing (2022.acl-long)
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| Challenge: | Existing models struggle to handle hard mentions due to insufficient contexts, limiting their overall typing performance. |
| Approach: | They propose to exploit sibling mentions to enhance the mention representations by adding unseen test mentions as new nodes for inference. |
| Outcome: | The proposed model outperforms ten strong baseline models and outperformed strong baselines. |
Contrastive Distillation on Intermediate Representations for Language Model Compression (2020.emnlp-main)
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| Challenge: | Existing methods to compress language models use a simple L_2 loss to distill knowledge in the intermediate representations of a large BERT model to a smaller one. |
| Approach: | They propose a method that uses knowledge distillation to distill knowledge through intermediate layers of the teacher via a contrastive objective. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the GLUE benchmark. |
One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement (2026.acl-long)
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| Challenge: | Existing alignment methods for Large Language Models (LLMs) are expensive and lack the flexibility to fully activate their latent reasoning capabilities. |
| Approach: | They propose a modular framework that treats reasoning elicitation as an inference-time alignment task. |
| Outcome: | The proposed framework outperforms baselines by 2.1% on average across diverse architectures and benchmarks. |
Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles (2024.emnlp-main)
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| Challenge: | Existing methods for improving LLMs in simulations are limited due to privacy concerns and limited domain knowledge. |
| Approach: | They propose a pipeline that elicits qualitative feedback from a domain-expert and transforms it into a set of principles that govern an LLM-prompted roleplay. |
| Outcome: | The proposed pipeline shows a 30% improvement in response quality and principle following for the downstream task. |
Beyond Sequences: Two-dimensional Representation and Dependency Encoding for Code Generation (2025.acl-long)
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| Challenge: | Existing code generation approaches represent code as a linear sequence of tokens, but positional encodings compromise generalization . explicit positional encoders sacrifice permutation invariance, imposes a strict order on the input sequence . |
| Approach: | They propose to represent code snippets as two-dimensional entities with explicit encodings . they propose to use dictionary learning to perform semantic matching between code lines . |
| Outcome: | The proposed model captures the hierarchical and spatial structure of code, especially the dependencies between code lines. |
ATGL: An Adaptive-Threshold Global Loss for Document-level Relation Extraction (2026.acl-long)
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| Challenge: | Document-level relation extraction (DocRE) aims to determine which relations hold between a given entity pair in a document. |
| Approach: | They propose a document-level relation extraction paradigm that decouples existing losses into independent positive and negative losses, which interact solely with a shared threshold. |
| Outcome: | The proposed model outperforms existing models on four datasets and achieves state-of-the-art results. |
Effective Convolutional Attention Network for Multi-label Clinical Document Classification (2021.emnlp-main)
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| Challenge: | a large number of medical encounters need to be coded everyday due to long document sets and large label set. |
| Approach: | They propose a convolutional attention network for multi-label document classification problem . they use convolution-based encoders and convolution networks to aggregate information across documents . |
| Outcome: | The proposed model outperforms prior best model and multilingual Transformer model on a widely used dataset in the medical domain. |
NeuInfer: Knowledge Inference on N-ary Facts (2020.acl-main)
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| Challenge: | Existing studies on knowledge inference on binary facts have focused on finding out connotative valid facts. |
| Approach: | They propose a neural network model, NeuInfer, for knowledge inference on n-ary facts. |
| Outcome: | The proposed model can cope with the task to infer an unknown element in a whole fact, while ignoring the binary facts. |
Priority on High-Quality: Selecting Instruction Data via Consistency Verification of Noise Injection (2025.emnlp-main)
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| Challenge: | Existing methods for instruction selection rely on external models or rules, overlooking the intrinsic association between pre-trained model and instruction data. |
| Approach: | They propose a method that utilizes noise injection to identify the quality of instruction data without relying on external models. |
| Outcome: | The proposed method outperforms the model trained on the entire dataset and established baselines. |
Incremental Transformer with Deliberation Decoder for Document Grounded Conversations (P19-1)
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| Challenge: | Existing dialogue systems do not exploit document knowledge effectively enough. |
| Approach: | They propose a Transformer-based architecture for document grounded conversations that incorporates document knowledge into a two-pass decoder to improve context coherence and knowledge correctness. |
| Outcome: | The proposed model outperforms baselines on context coherence and knowledge relevance on a real-world document grounded dataset. |
MCS: An In-battle Commentary System for MOBA Games (2022.coling-1)
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Xiaofeng Qi, Chao Li, Zhongping Liang, Jigang Liu, Cheng Zhang, Yuanxin Wei, Lin Yuan, Guang Yang, Lanxiao Huang, Min Li
| Challenge: | In-battle commentary is an important component of live streaming of e-sports competitions and is applicable to a wide range of scenarios like combat information analysis and live streaming. |
| Approach: | They propose a generative system for in-battle real-time commentary in mobile MOBA games and propose 'transform' method to convert match statistics and utterances into consistent encoding space. |
| Outcome: | The proposed system is based on real-time match statistics and events and can be used for live streaming, e-sports commentary and combat information analysis. |
Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence (2020.acl-main)
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| Challenge: | Recent neural attention models conflate all steps into a single end-to-end system and simplify training process. |
| Approach: | They propose to explicitly segment target text into fragment units and align them with their data correspondences. |
| Outcome: | The proposed model outperforms neural attention models on E2E and WebNLG benchmarks. |
How does Attention Affect the Model? (2021.findings-acl)
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| Challenge: | Existing studies on the effectiveness of attention in NLP do not consider changes in semantic capability of different components. |
| Approach: | They propose a framework that exploits a convex hull representation of sequence semantics in an n-dimensional Semantic Euclidean Space and defines indicators to capture the impact of attention on sequence semantic. |
| Outcome: | The proposed framework exploits a convex hull representation of sequence semantics in an n-dimensional Semantic Euclidean Space and defines indicators to capture the impact of attention on sequence semantic. |
SRF: Enhancing Document-Level Relation Extraction with a Novel Secondary Reasoning Framework (2024.emnlp-main)
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| Challenge: | Existing methods for document-level relation extraction ignore bidirectional mention interaction when generating relational features for entity pairs. |
| Approach: | They propose a document-level relation extraction model that incorporates bidirectional mention fusion and a simple yet effective evidence extraction module for relation prediction. |
| Outcome: | The proposed model achieves SOTA performance and the proposed method is effective and general when integrated into existing models. |
OpenGenAlign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation (2026.findings-acl)
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| Challenge: | Existing reward models perform suboptimal on held-out benchmarks, resulting in poor quality outputs. |
| Approach: | They propose a framework and a high-quality dataset to evaluate reward models . they define four key metrics to assess generation quality and develop a pipeline to evaluate outputs . |
| Outcome: | The proposed framework and dataset improves hallucination-free, comprehensive, reliable, and efficient open-ended long-context generation. |
Continual Pretraining on Encrypted Synthetic Data for Privacy-Preserving LLMs (2026.findings-eacl)
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| Challenge: | Existing methods to protect PII from training on small corpora are difficult to implement in real-world applications. |
| Approach: | They propose an entity-based framework that synthesizes encrypted training data to protect PII. |
| Outcome: | The proposed framework outperforms base models and ensures PII security on limited-scale datasets while exhibiting a modest performance gap compared to models trained on unencrypted synthetic data. |
Language Tags Matter for Zero-Shot Neural Machine Translation (2021.findings-acl)
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| Challenge: | Existing studies on multilingual machine translation have ignored the importance of LTs. |
| Approach: | They propose to use language tag (LT) strategies to indicate translation directions in MNMT to enhance consistency and alleviate off-target issues in zero-shot directions. |
| Outcome: | The proposed model could translate between unsupervised languages and achieve a +8 BLEU score difference over other LT strategies in translation tasks. |
Curriculum Consistency Learning for Conditional Sentence Generation (2024.emnlp-main)
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| Challenge: | Consistency learning (CL) has proven to be a valuable technique for improving the robustness of conditional sentence generation models. |
| Approach: | They propose a strategy that guides models to learn consistency in alignment with their current capacity to differentiate between features. |
| Outcome: | The proposed strategy delivers +2.0 accuracy point improvement compared with vanilla IT and +0.7 COMET scores over traditional CL methods in MT tasks. |
Multi-Agent Collaboration via Cross-Team Orchestration (2025.findings-acl)
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Zhuoyun Du, Chen Qian, Wei Liu, Zihao Xie, YiFei Wang, Rennai Qiu, Yufan Dang, Weize Chen, Cheng Yang, Ye Tian, Xuantang Xiong, Lei Han
| Challenge: | Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents. |
| Approach: | They propose a framework that enables orchestrated teams to jointly propose various task-oriented solutions and interact with their insights in a self-independence while cross-team collaboration environment for superior solutions generation. |
| Outcome: | Experiments show that the framework can generate better software quality compared to state-of-the-art frameworks. |
The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems (2025.findings-acl)
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| Challenge: | Existing studies have focused on corpus poisoning, but there are no studies on adversarial attacks on RAG systems. |
| Approach: | They propose a novel imperceptible retrieve-to-generate attack against RAG systems . they propose regenerative reinforcement learning framework that tracks interactions between attacker and target RAG . |
| Outcome: | The proposed framework outperforms existing attacks on factual and non-factual RAG systems with small imperceptible text perturbations. |
LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety (2026.acl-long)
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Junxiao Yang, Haoran Liu, Jinzhe Tu, Jiale Cheng, Zhexin Zhang, Shiyao Cui, Jiaqi Weng, Jialing Tao, Hui Xue, Hongning Wang, Han Qiu, Minlie Huang
| Challenge: | Large language models (LLMs) have demonstrated better safety performance in high-resource languages than in low-resourced languages. |
| Approach: | They propose language-agnostic semantic alignment (LASA) which anchors safety alignment directly in semantic bottlenecks. |
| Outcome: | The proposed approach significantly improves safety across all languages: average attack success rate drops from 24.7% to 2.8% on LLaMA-3.1-8B-Instruct and remains within 3–4% across Qwen2.5 and Qwend3 Instruct models (7B–32B). |
Compete to Complete: Co-opetition Adversarial Learning for Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing approaches to reduce hallucination in large language models lack a robust mechanism for generating a generative model. |
| Approach: | They propose a framework that formulates retriever–generator training in RAG as a minimax game. |
| Outcome: | The proposed framework improves retrieval-augmented generation performance on seven benchmark datasets. |
Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach (P19-1)
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| Challenge: | Existing methods for reducing word omission errors in neural machine translation are prone to omit essential words on the source side. |
| Approach: | They propose a contrastive learning approach to reduce word omission errors in NMT by omitting words. |
| Outcome: | The proposed approach achieves better translation performance than baseline methods on Chinese-to-English, German-to English, and Russian-toEnglish translation tasks. |
Focusing Condition: Inference-Time Self-Contrastive Steering Elicits Better Conditional Text Embeddings in LLMs (2026.acl-long)
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| Challenge: | Existing methods for extracting conditional text embeddings from large language models (LLMs) relying on prompts often fails to produce high-quality conditional embeddables, resulting in degradation of quality. |
| Approach: | They propose a plug-and-play method that constructs unconditional general text embeddings and uses them to refine conditional text embeds. |
| Outcome: | The proposed method improves performance of prompt-based methods on clustering, Semantic Textual Similarity, and triplet alignment datasets. |
Iterative Structured Pruning for Large Language Models with Multi-Domain Calibration (2026.eacl-industry)
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| Challenge: | Existing models with unstructured pruning often yield irregular sparsity patterns that necessitate specialized hardware or software support. |
| Approach: | They propose a structured pruning framework that eliminates entire architectural components and maintains compatibility with standard hardware accelerators. |
| Outcome: | The proposed model pruning framework achieves significant compression with minimal performance degradation on multiple models across diverse downstream tasks. |
Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue (2024.findings-naacl)
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| Challenge: | emergence of large language models (LLMs) improves capabilities of dialogue systems . but they lack communication skills, which make them more like information seeking tools . |
| Approach: | They propose to empower LLMs with communication skills through inner monologues . they use a benchmark to evaluate the dialogue generation ability of the model . |
| Outcome: | The proposed model outperforms the baselines in the evaluation of communication skills. |
SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning (2026.findings-acl)
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| Challenge: | Standard RAG frameworks treat retrieval as a static, single-round auxiliary step . compressed workflow makes it difficult to form reliable evidence chains . |
| Approach: | They propose a framework that decouples tasks and allows for dynamic multi-round exploration . they propose retrieval-augmented generation (RAG) to mitigate hallucinations and knowledge obsolescence . |
| Outcome: | The proposed framework improves the strongest baseline by *+6.46* accuracy points on average across five benchmarks and five LLM backbones. |
From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification (2023.findings-emnlp)
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| Challenge: | Existing evidence retrieval models are based on probability ranking principle . existing models do not align with retrieval-enhanced verification frameworks . |
| Approach: | They propose a feedback-based evidence retriever that optimizes the evidence retrieval process by incorporating feedback from the claim verifier. |
| Outcome: | Empirical studies show that the proposed method is superior to baseline methods. |
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification (P19-1)
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| Challenge: | Existing methods to extract information from evidence are unable to grasp relational and logical information among the evidence. |
| Approach: | They propose a graph-based evidence aggregating and reasoning framework to integrate evidence from multiple pieces of evidence. |
| Outcome: | The proposed framework achieves significant performance improvements on a large-scale benchmark dataset. |
Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems (2022.aacl-short)
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| Challenge: | Existing methods to solve Math Word Problems rely on human annotation . empirical results suggest that our method universally improves the performance on single-unknown and multiple-un unknown benchmarks. |
| Approach: | They propose a controlled equation generation solver by leveraging a set of control codes to guide the model to consider certain reasoning logic and decode the corresponding equations expressions transformed from the human reference. |
| Outcome: | The proposed method improves performance on single-unknown and multiple-un unknown benchmarks with 13.2% accuracy on the challenging multiple-unequal datasets. |
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)
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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan
| Challenge: | Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems. |
| Approach: | They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations. |
| Outcome: | The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods. |
TabularMath: Understanding Math Reasoning over Tables with Large Language Models (2026.findings-acl)
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| Challenge: | Mathematical reasoning has long been a key benchmark for evaluating large language models. |
| Approach: | They propose a framework that transforms math word problems into scalable tabular reasoning tasks. |
| Outcome: | The proposed framework transforms math word problems into scalable and verified tabular reasoning tasks. |
Accelerating Multiple Intent Detection and Slot Filling via Targeted Knowledge Distillation (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive Spoken Language Understanding models suffer from multi-modality problem . current methods have little prior knowledge about the reference during inference . |
| Approach: | They propose a Targeted Knowledge Distillation Framework (TKDF) for multi-intent SLU that utilizes the knowledge distillation method to improve the performance. |
| Outcome: | The proposed model outperforms existing models on two public multi-intent datasets while speeding up by over 4.5 times. |
Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts (2026.acl-long)
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| Challenge: | Existing LLM-based agents lack inherent spatial awareness, relying on web search or text matching while hallucinating spatial relationships. |
| Approach: | They propose a spatial-based agent that can perform real-world geospatial computations . they use natural-language questions to parse into executable workflows based on geoFlow Graphs - directed acyclic graphs with nodes corresponding to spatial concepts and edges representing transformations. |
| Outcome: | The proposed agent outperforms existing baselines on MapEval-API and MapQA benchmarks while producing interpretable and executable geospatial workflows. |
Mitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models (2024.findings-acl)
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| Challenge: | a new text classification framework for large language models addresses the problem of boundary ambiguity and inherent biases in LLMs. |
| Approach: | They propose a two-stage classification framework for large language models to mitigate bottlenecks . their approach uses pairwise comparisons to efficiently narrow down options . |
| Outcome: | The proposed framework reduces the number of options and improves on four datasets. |
Is GPT-4 a Good Data Analyst? (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation, data storytelling, etc. |
| Approach: | They propose to use GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. |
| Outcome: | The proposed framework compares GPT-4 with human data analysts to perform end-to-end data analysis with databases from a wide range of domains. |
It Takes Two to Lie: One to Lie, and One to Listen (2020.acl-main)
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Denis Peskov, Benny Cheng, Ahmed Elgohary, Joe Barrow, Cristian Danescu-Niculescu-Mizil, Jordan Boyd-Graber
| Challenge: | Deception is a powerful tool for predicting when a lie occurs in long-lasting relationships . a functioning society is impossible without trust, but deception can be betrayed through false identities, spearphishing attacks and disinformation campaigns. |
| Approach: | They propose a dataset to analyze the use of deception in online negotiation-based game Diplomacy . it captures deceptions in long-lasting relationships where interlocutors combine truth with lies to advance objectives. |
| Outcome: | The proposed model predicts when a lie occurs nearly as well as human players. |
Exploiting Contextual Information via Dynamic Memory Network for Event Detection (D18-1)
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| Challenge: | Existing methods for event detection only process context once . a multi-hop mechanism to capture contextual information improves performance . |
| Approach: | They propose to use dynamic memory network to capture contextual information . they propose to model event triggers by identifying word or phrase which most represents it . |
| Outcome: | The proposed model achieves best F1 score compared to the state-of-the-art models. |
Medical Dialogue Generation via Dual Flow Modeling (2023.findings-acl)
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| Challenge: | Medical dialogue systems (MDS) aim to provide patients with medical services, such as diagnosis and prescription. |
| Approach: | They propose a Dual Flow enhanced Medical (DFMed) dialogue generation framework that extracts the medical entities and doctor's dialogue acts used in the dialogue history and models their transitions with an entity-centric graph flow and a sequential act flow. |
| Outcome: | The proposed framework exceeds baselines in both automatic and manual evaluations on two datasets. |
LLM2: Let Large Language Models Harness System 2 Reasoning (2025.naacl-short)
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| Challenge: | Empirical results on mathematical reasoning benchmarks substantiate the efficacy of Large language models (LLMs). |
| Approach: | They propose a framework that combines an LLM with a process-based verifier to generate plausible candidates and provide timely process-driven feedback to distinguish desirable and undesirable outputs. |
| Outcome: | Empirical results show that LLM2 improves accuracy on GSM8K and self-consistency increases major@20 accuracy. |
Exploring the Impacts of Feature Fusion Strategy in Multi-modal Entity Alignment (2025.coling-main)
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| Challenge: | Existing approaches to merge multi-modal knowledge only use one fusion strategy . however, the impact of the fusion on individual entities could be ignored . |
| Approach: | They propose an adaptive multi-modal feature fusion strategy for entity alignment that selects the optimal entity-level feature blending strategy. |
| Outcome: | The proposed model achieves state-of-the-art (SOTA) performance compared to models using the same modality on a dataset with multiple inconsistent images and styles. |
HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning (2022.findings-emnlp)
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Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Temporal Knowledge Graphs (TKGs) store facts as triples in the form of subject, relation, object, timestamps. |
| Approach: | They propose a Temporal Knowledge Graph (TKG) model that extends each triple with a timestamp to describe dynamic facts. |
| Outcome: | The proposed model improves on six benchmark datasets with up to 5.6% performance improvement compared to the state-of-the-art models. |
Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs (2024.acl-long)
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| Challenge: | Existing methods to compress long contexts have degraded dramatically as compression ratios increase, sometimes even falling to the closed-book level. |
| Approach: | They propose a query-guided compression method that preserves key information within the compressed context. |
| Outcome: | The proposed method can consistently perform well even at high compression ratios, and offers significant benefits in terms of inference cost and throughput. |
Discourse-Aware Neural Extractive Text Summarization (2020.acl-main)
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| Challenge: | Recent studies have shown that sentence-based extractive models result in redundant or uninformative phrases in the extracted summaries. |
| Approach: | They propose a discourse-aware neural summarization model that extracts sub-sentential discourse units as candidates for extractive selection on a finer granularity. |
| Outcome: | Experiments show that the proposed model outperforms state-of-the-art models on popular summarization benchmarks. |
T-MAD: Target-driven Multimodal Alignment for Stance Detection (2025.emnlp-main)
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| Challenge: | Existing methods for Multimodal Stance Detection struggle with generalizing to unseen targets and handling modality inconsistencies. |
| Approach: | They propose a multimodal stability detection model which captures target-specific relationships and balances modality contributions by iterative reasoning. |
| Outcome: | Experiments on the MMSD and MultiClimate datasets show that the proposed model outperforms state-of-the-art models with optimal results achieved using RoBERTa, ViT, and an iterative depth of 5. |
Tailoring Table Retrieval from a Field-aware Hybrid Matching Perspective (2025.emnlp-main)
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| Challenge: | Empirical results show that a hybrid retrieval approach to table retrieval outperforms state-of-the-art benchmarks. |
| Approach: | They propose a table-tailored HYbrid matching rEtriever which addresses table matching needs from a field-aware hybrid perspective. |
| Outcome: | Empirical results show that the proposed rEtriever outperforms state-of-the-art retrieval methods. |
R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling (2026.acl-long)
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| Challenge: | Existing RL-based approaches to function calling are misaligned between reasoning processes and tool-call decisions. |
| Approach: | They propose a reasoning-aware RL framework for interpretable function calling . they integrate a composite reward integrating format/correctness constraints, CER, and SMV . |
| Outcome: | Experiments on BFCL/ACEBench show R2IF outperforms baselines by 34.62% with positive Average CoT Effectiveness. |
From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning (2026.findings-acl)
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Guo Tang, Ke Cheng, Huiming Fan, Heng Chang, Wenxiang Zheng, Xianhao Ou, Junjia Xiang, Ming Liu, Yujun Zhou, Li Lanyu, Bing Qin
| Challenge: | Existing Large Language Models (LLMs) struggle with implicit modality alignment and suboptimal graph linearization. |
| Approach: | They propose a training-free, test-time adaptive framework that reframes TKG prediction as explicit evidence-driven reasoning. |
| Outcome: | ExE-LLM outperforms fully trained graph neural networks on four benchmarks . it achieves SOTA performance in inductive settings, significantly outperforming fully trained neural networks . |
AlignBench: Benchmarking Chinese Alignment of Large Language Models (2024.acl-long)
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Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Andrew Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Xiaotao Gu, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang
| Challenge: | Effective evaluation of alignment for emerging Chinese LLMs is still significantly lacking, calling for real-scenario grounded, open-ended, challenging and automatic evaluations tailored for alignment. |
| Approach: | They propose a multi-dimensional benchmark for evaluating LLMs’ alignment in Chinese with 8 main categories, 683 real-scenario rooted queries and corresponding human verified references. |
| Outcome: | The benchmark uses a human-in-the-loop data curation pipeline, 683 real-scenario rooted queries and human verified references. |
A Survey of Post-Training Scaling in Large Language Models (2025.acl-long)
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Hanyu Lai, Xiao Liu, Junjie Gao, Jiale Cheng, Zehan Qi, Yifan Xu, Shuntian Yao, Dan Zhang, Jinhua Du, Zhenyu Hou, Xin Lv, Minlie Huang, Yuxiao Dong, Jie Tang
| Challenge: | Large language models (LLMs) have demonstrated proficiency in understanding and generating human natural languages. |
| Approach: | They propose a framework for scaling large language models using supervised fine-tuning, RLxF and test-time compute methodologies. |
| Outcome: | The proposed model can be used to understand and generate human natural languages. |
MPTc-Bench: Measuring Cross-market Generative Ability of Vision-Language Models via Movie Poster Transcreation (2026.findings-acl)
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| Challenge: | Recent work adapts textual transcreation to image editing and formulates image transcreations to better match a target audience while preserving meaning. |
| Approach: | They propose a two-stage planner-editor pipeline in which an VLM planner specifies executable edits and an image editor renders them. |
| Outcome: | The proposed model can transcreate a visual asset for a different market while preserving its identity while matching market-specific design preferences and multilingual typography. |
Detoxification for LLM: From Dataset Itself (2026.acl-long)
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| Challenge: | Existing methods for large language models focus on post-training stage or inference time, while few tackle the source of toxicity, namely, the dataset itself. |
| Approach: | They propose to localize and rewrite toxic spans in raw corpora with SoCD, which guides an LLM to localized and preserving semantics while preserving toxicity. |
| Outcome: | The proposed method reduces TP from 0.42 to 0.18 and Expected Maximum Toxicity (EMT) from 0.43 to 0.20 on three LLMs. |
Adversarial Preference Optimization: Enhancing Your Alignment via RM-LLM Game (2024.findings-acl)
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| Challenge: | Existing methods for training large language models require additional annotations to adjust to shifted distributions. |
| Approach: | They propose an algorithm that allows LLMs and reward models to update alternatively via a min-max game to improve their alignment. |
| Outcome: | The proposed framework improves existing alignment baselines in terms of LLM helpfulness and harmlessness. |
Evaluating Memory Capability in Continuous Lifelog Scenario (2026.findings-acl)
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Jianjie Zheng, Zhichen Liu, Zhanyu Shen, Jingxiang Qu, Guanhua Chen, Yile Wang, Yang Xu, Yang Liu, Sijie Cheng
| Challenge: | Existing benchmarks focus on online one-on-one chatting or human-AI interactions, neglecting real-world scenarios. |
| Approach: | They propose a framework to curate a lifelog benchmark that combines two subsets of audio data to address temporal leakage in offline settings. |
| Outcome: | The proposed framework outperforms existing benchmarks on live chats and AI interactions. |
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models (2024.acl-long)
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| Challenge: | Retrieval-augmented generation (RAG) is a main technique for alleviating hallucinations in large language models. |
| Approach: | They propose to integrate RAG into large language models to analyze word-level hallucinations using a corpus of 18,000 naturally generated responses from diverse LLMs. |
| Outcome: | The proposed model can fine tune a relatively small LLM and achieve a competitive hallucination detection performance when compared to the existing prompt-based approaches. |
SPIDE: Serial and Parallel Intertwined Speculative Decoding (2026.findings-acl)
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Wenru Xu, Peixuan Xu, Ziqi Yang, Ming Hu, Zihui Wang, Jianzhong Qi, Rongshan Yu, Xiaoliang Fan, Cheng Wang
| Challenge: | Speculative decoding (SD) is a training-free SD framework that orchestrates dynamic alternation combining serial dynamic drafting with parallel draft verification. |
| Approach: | They propose a serial and parallel intertwined speculative DEcoding framework that orchestrates dynamic alternation combining serial dynamic drafting and parallel draft verification. |
| Outcome: | The proposed framework accelerates inference while reducing the LLM usage costs. |
COMPKE: Complex Question Answering under Knowledge Editing (2025.findings-acl)
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| Challenge: | Existing benchmarks for knowledge editing do not accurately evaluate how well models apply knowledge in real-life situations. |
| Approach: | They propose a benchmark to evaluate how well updated models apply new knowledge in real-life situations. |
| Outcome: | The proposed method achieves 39.47 accuracy on GPT-4o-mini but drops significantly to 3.83 on Qwen2.5-3B. |
CAVA: A Tool for Cultural Alignment Visualization & Analysis (2024.emnlp-demo)
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| Challenge: | Using CAVA, researchers can analyze country-specific biases encoded in large language models. |
| Approach: | They propose a visualization tool that allows users to identify biases in language models by adding country-based questions and models. |
| Outcome: | The proposed tool can be used to analyze the cultural competencies of large language models across the dimension of geographic locales. |
Unsupervised Concept Representation Learning for Length-Varying Text Similarity (2021.naacl-main)
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| Challenge: | Existing document similarity approaches suffer from the information gap caused by context and vocabulary mismatches when comparing varying-length texts. |
| Approach: | They propose an unsupervised concept representation learning approach to address this issue . they propose a concept-based document matching method to leverage recognition of local phrase features . |
| Outcome: | The proposed method achieves a better F1 score than baseline models on real-world data sets. |
Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback (2025.emnlp-industry)
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| Challenge: | Using a mixtral-87B model, we reduce the time it takes to generate concise notes during conversations, and reduce the amount of time spent on manual review. |
| Approach: | They propose a system that combines a Mixtral-87B model for continuous note generation with a DeBERTa-based classifier to filter trivial content. |
| Outcome: | The proposed system achieved a 3% reduction in case handling time compared to bulk summarization, and high agent satisfaction ratings from surveys. |
Reinforcement Learning with Token-level Feedback for Controllable Text Generation (2024.findings-naacl)
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| Challenge: | Existing methods for controllable text generation are guided by coarse-grained feedback, which may lead to suboptimal performance owing to semantic twists or progressions within sentences. |
| Approach: | They propose a reinforcement learning algorithm which formulates TOken-LEvel rewards for controllable text generation and employs a "first-quantize-then-noise" paradigm to enhance the robustness of the RL algorithm. |
| Outcome: | The proposed algorithm can achieve superior performance on single-attribute and multi-attract control tasks. |
DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding (2026.findings-acl)
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| Challenge: | Autoregressive (AR) large audio language models are expensive in data and computation . prior work shows diffusion-based LALMs can improve audio understanding under matched settings . |
| Approach: | They propose a diffusion-based LALM that upgrades the speech encoder and employs dual semantic and acoustic adapters. |
| Outcome: | a new model improves over existing autoregressive large language models and is competitive to strong AR models . the proposed model can make use of limited training data and improve inference efficiency . a recent study shows that diffusion-based models can improve audio understanding . |
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)
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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan
| Challenge: | Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures. |
| Approach: | They propose a toolkit that supports pre-training models of different modalities. |
| Outcome: | The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks. |
Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection (2026.findings-acl)
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Zhuoyang Wu, Xinze Li, Zhenghao Liu, Yukun Yan, Zhiyuan Liu, Minghe Yu, Cheng Yang, Yu Gu, Ge Yu, Maosong Sun
| Challenge: | Existing studies treat SLMs as student models and use long-form Chains-of-Thought (CoTs) as supervision signals for Supervised Fine-Tuning (SFT). Existing research focuses on distilling reasoning ability from LLMs to enhance the mathematical reasoning performance of small-scale models. |
| Approach: | They propose a framework that refines teacher CoTs through an error-aware reflection process to enable the student model to construct more tailored teacher Cots. |
| Outcome: | Experiments on multiple mathematical reasoning benchmarks show that ORION improves performance by more than 2% over all baselines. |
DCP: Dual-Cue Pruning for Efficient Large Vision-Language Models (2025.emnlp-main)
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| Challenge: | Existing pruning methods for large vision language models use visual tokens to prune . existing methods fail to balance efficiency and semantic alignment due to large number of visual token. |
| Approach: | They propose a cross-modal pruning framework that considers textual semantics and visual self-attention to combine them to achieve efficient inference acceleration. |
| Outcome: | The proposed pruning framework can retain only 25% of the visual tokens, with a minimal performance degradation of only 0.063% on LLaVA-1.5-13B. |
SEE: Continual Fine-tuning with Sequential Ensemble of Experts (2025.findings-acl)
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| Challenge: | Continual fine-tuning of large language models suffers from catastrophic forgetting . some approaches use routers to assign tasks to experts, but continual learning often requires retraining . |
| Approach: | They propose a framework that integrates routing and response mechanisms within each expert . it eliminates the need for an additional router and allows each expert to decide whether a query should be handled . |
| Outcome: | The proposed framework outperforms previous approaches in continual fine-tuning . it can handle learning tasks and out-of-distribution instances, paving the way for distributed model ensembling. |
Agent-in-the-Loop: A Data Flywheel for Continuous Improvement in LLM-based Customer Support (2025.emnlp-industry)
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Cen Zhao, Tiantian Zhang, Hanchen Su, Yufeng Zhang, Shaowei Su, Mingzhi Xu, Yu Liu, Wei Han, Jeremy Werner, Claire Na Cheng, Yashar Mehdad
| Challenge: | Existing offline approaches to improve an LLM-based customer support system rely on batch annotations. |
| Approach: | They propose an agent-in-the-loop framework that integrates four key types of annotations directly into live customer operations: (1) pairwise response preferences, (2) agent adoption and rationales, (3) knowledge relevance checks, and (4) identification of missing knowledge. |
| Outcome: | The proposed framework reduces retraining cycles from months to weeks by integrating four key types of annotations directly into live customer operations. |
Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation (2022.findings-emnlp)
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| Challenge: | Existing methods to detect toxic generation of pretrained language models rely on templates, data extraction, crowdsourcing workers or automatic generation. |
| Approach: | They propose a method to construct adversarial contexts conditioned on a given response . they augment existing dataset BAD+ and construct a new dataset B AD+ . |
| Outcome: | The proposed method can detect toxic or biased content in large pretrained language models. |
BC-Prover: Backward Chaining Prover for Formal Theorem Proving (2024.emnlp-main)
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| Challenge: | Existing methods for interactive theorem proving in formal logic lack robustness and robustness. |
| Approach: | They propose a backward chaining framework guided by pseudo steps for proofstep generation that prioritizes pseudo steps. |
| Outcome: | The proposed framework improves on the miniF2F benchmark. |
DiffusPoll: Conditional Text Diffusion Model for Poll Generation (2024.findings-acl)
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| Challenge: | Social media platforms manipulate public opinion through sheer numbers and cause biases, authors say . they say new paradigm for poll generation can generate high-quality samples while preserving diversity . |
| Approach: | They propose a non-autoregressive diffusion model that uses masks to generate polls . they use attribute tags to enhance the quality of polls and to diversify poll options . |
| Outcome: | The proposed model matches the Transformer model while offering greater diversity and quality. |
Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities (2025.naacl-long)
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Chung-En Sun, Xiaodong Liu, Weiwei Yang, Tsui-Wei Weng, Hao Cheng, Aidan San, Michel Galley, Jianfeng Gao
| Challenge: | Recent research shows that Large Language Models (LLMs) are vulnerable to automated jailbreak attacks. |
| Approach: | They propose a framework that crafts adversarial LLMs with enhanced jailbreak ability. |
| Outcome: | ADV-LLM significantly reduces the computational cost of generating adversarial suffixes while achieving nearly 100% ASR on various open-source LLMs. |
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models (2023.acl-short)
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Siheng Li, Cheng Yang, Yichun Yin, Xinyu Zhu, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang
| Challenge: | Existing research on information-seeking conversations is stymied by the lack of training data. |
| Approach: | They propose to use autoconv for synthetic conversation generation to capture the characteristics of the information-seeking process and fine tune an LLM with a few human conversations to generate synthetic conversations with high quality. |
| Outcome: | The proposed model improves on two commonly-used datasets and alleviates the dependence on human annotation. |
An Exploration of Post-Editing Effectiveness in Text Summarization (2022.naacl-main)
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Vivian Lai, Alison Smith-Renner, Ke Zhang, Ruijia Cheng, Wenjuan Zhang, Joel Tetreault, Alejandro Jaimes-Larrarte
| Challenge: | Automated summarization methods are efficient but can suffer from low quality. |
| Approach: | They conducted an experiment with 72 participants to compare post-editing provided summaries with manual summarization for summary quality, human efficiency, and user experience. |
| Outcome: | The results show that post-editing improves summary quality, human efficiency, and user experience on formal (XSum news) and informal (Reddit posts) text. |
DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering (2025.acl-long)
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Rong Cheng, Jinyi Liu, Yan Zheng, Fei Ni, Jiazhen Du, Hangyu Mao, Fuzheng Zhang, Bo Wang, Jianye Hao
| Challenge: | Existing approaches to multi-hop question answering struggle to identify and organize dynamic knowledge . et al., 2023; Liu e.t. al. 2023) suggest a dual-process framework for multi-step reasoning . |
| Approach: | They propose a synergistic dual-process framework that integrates reasoning and retrieval. |
| Outcome: | The proposed framework improves answer accuracy and coherence even in smaller-scale models. |
Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion (2024.acl-long)
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Rui Ying, Mengting Hu, Jianfeng Wu, Yalan Xie, Xiaoyi Liu, Zhunheng Wang, Ming Jiang, Hang Gao, Linlin Zhang, Renhong Cheng
| Challenge: | Current methods embed factual knowledge into continuous vector space and apply geometric operations to learn potential patterns in temporal knowledge graphs. |
| Approach: | They propose a temporal knowledge graph completion method that uses two geometric operations to learn missing facts in temporal graphs. |
| Outcome: | The proposed method significantly outperforms existing temporal knowledge graph embedding models. |
Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication (2024.findings-emnlp)
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Weize Chen, Chenfei Yuan, Jiarui Yuan, Yusheng Su, Chen Qian, Cheng Yang, Ruobing Xie, Zhiyuan Liu, Maosong Sun
| Challenge: | Natural language (NL) has long been the predominant format for human cognition and communication, but its utility in LLMs has not been thoroughly examined. |
| Approach: | They propose to allow LLMs to choose the most suitable format before reasoning or communicating, and to automate the selection process. |
| Outcome: | The proposed format improves reasoning efficiency and reduces token usage while maintaining communicative effectiveness. |
Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have aimed to refine their capacity to accurately follow human instructions and navigate intricate scenarios. |
| Approach: | They propose a method that uses a set of instructions to translate English into Japanese and then generates Japanese instruction data using GPT-4. |
| Outcome: | The proposed method outperforms Japanese-Alpaca models in the evaluation benchmarks without human references. |
MaCSC: Towards Multimodal-augmented Pre-trained Language Models via Conceptual Prototypes and Self-balancing Calibration (2024.naacl-long)
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| Challenge: | Existing approaches to training pre-trained language models (PLMs) focus on static image modality; inevitably encounter modality gaps and noise; and treat all modalities. |
| Approach: | They propose a multimodal-augmented framework that can infuse multimodal semantics into PLMs and facilitate a self-balancing calibration of information allocation. |
| Outcome: | The proposed framework outperforms baselines on multiple NLP tasks and outperformed existing frameworks. |
EqualizeIR: Mitigating Linguistic Biases in Retrieval Models (2025.naacl-short)
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| Challenge: | Existing information retrieval models show significant linguistic biases based on the linguistic complexity of queries. |
| Approach: | They propose a framework to mitigate linguistic biases in IR models by using a linguistically biased weak learner to capture biased queries and then train a robust model by regularizing and refining its predictions. |
| Outcome: | The proposed framework reduces performance disparities across simple and complex queries while improving overall retrieval performance. |
Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing approaches to train multilingual models to learn the inductive bias of a shared vocabulary and set of parameters across languages. |
| Approach: | They propose to use a multilingual crossover encoder-decoder to fuse language pairs at an instance level to encourage sharing of input and output spaces. |
| Outcome: | The proposed approach improves quality on English-to-Many, Many-to English and zero-shot translation tasks from +0.5 BLEU up to +5.5 BLUE points. |
HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents (2026.acl-long)
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| Challenge: | Existing memory systems represent conversation history as unstructured embedding vectors, retrieving information through semantic similarity. |
| Approach: | They propose a bio-inspired memory architecture that models memory as a dynamic graph with Hebbian learning dynamics. |
| Outcome: | The proposed architecture leverages both semantic similarity and learned associations . it can be used to build a bio-inspired memory graph with Hebbian learning dynamics . |
Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network (2022.coling-1)
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| Challenge: | Recent work on pre-trained language models (PrLMs) on labeled sentiment datasets has shown significant improvements on widerange of NLP tasks, including sentiment classification. |
| Approach: | They propose a multi-source unsupervised sentiment adaptation problem with pre-trained features to exploit the extracted pre-train features for efficient domain adaptation. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on multiple sentiment benchmarks and extensive ablation studies to verify the effectiveness of each module. |
ASPERA: A Simulated Environment to Evaluate Planning for Complex Action Execution (2025.acl-long)
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Alexandru Coca, Mark Gaynor, Zhenxing Zhang, Jianpeng Cheng, Bo-Hsiang Tseng, Peter Boothroyd, Hector Martinez Alonso, Diarmuid O Seaghdha, Anders Johannsen
| Challenge: | ASPERA framework allows developers to guide LLM generation of high-quality tasks based on user queries, simulation state and corresponding validation programs. |
| Approach: | They develop a framework comprising an assistant library simulation and a human-assisted LLM data generation engine to guide LLM generation of high-quality tasks . they use a dataset to evaluate 250 tasks generated using ASPERA . |
| Outcome: | The proposed framework can guide LLM generation of high-quality tasks tackling data availability and evaluation robustness challenges. |
PEPE: Long-context Extension for Large Language Models via Periodic Extrapolation Positional Encodings (2025.findings-emnlp)
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Jikun Hu, Dongsheng Guo, Yuli Liu, Qingyao Ai, Lixuan Wang, Xuebing Sun, Qilei Zhang, Quan Zhou, Cheng Luo
| Challenge: | Long-context extension attempts to extend contextual window in pre-trained LLMs . primary method involves expanding initial positional encodings, disrupting positional learning . |
| Approach: | They propose a new extension strategy based on Rotary Position Embedding to extend contextual window in pre-trained large language models. |
| Outcome: | The proposed method can extend the contextual window in pre-trained large language models . expansion disrupts positional encodings learned during pre-training, authors show . |
Multi-LLM Collaborative Search for Complex Problem Solving (2026.findings-acl)
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| Challenge: | Large language models (LLMs) often struggle with complex reasoning tasks due to the vast reasoning space inherent in the complexity and inherent ambiguities of natural languages. |
| Approach: | They propose a mixture-of-search-agents paradigm that integrates diverse reasoning pathways by combining independent exploration and iterative refinement among multiple LLMs. |
| Outcome: | The proposed approach improves performance over single-agent and multi-agend baselines in complex mathematical and commonsense reasoning tasks. |
Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective (2025.findings-acl)
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| Challenge: | Existing studies have shown that large language models (LLMs) can elicit implicit biases that hurt certain demographics without explicit harmful words. |
| Approach: | They propose three attack approaches to elicit agreements to biased viewpoints from LLMs from a psychometric perspective and built two benchmarks to compare them. |
| Outcome: | The proposed methods elicit agreements to biased viewpoints more effectively than baselines. |
NALA: an Effective and Interpretable Entity Alignment Method (2024.findings-emnlp)
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| Challenge: | Existing embedding-based EA methods encode entities as embeddables and learn to align embeddibles. |
| Approach: | They propose to capture three types of logical inference paths with Non-Axiomatic Logic to iteratively align entities and relations by integrating the conclusions of the inference path. |
| Outcome: | The proposed method outperforms state-of-the-art methods in terms of Hits@1 on all three datasets of DBP15K with both supervised and unsupervised settings. |
Structure-aware Knowledge Graph-to-text Generation with Planning Selection and Similarity Distinction (2023.emnlp-main)
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| Challenge: | Existing methods to generate knowledge graph-to-text (KG-to) text rely on pre-trained language models to bridge the gap between the different structures of the input KG and the target text. |
| Approach: | They propose a method that integrates graph structure-aware modules with pre-trained language models to capture the intricate topology information present in the KG. |
| Outcome: | The proposed model captures the topology information present in the knowledge graph and distinguishes similar input KGs through contrastive learning techniques. |
Fewer is More: Boosting Math Reasoning with Reinforced Context Pruning (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown impressive capabilities, yet they struggle with math reasoning. |
| Approach: | They propose a coarse-to-fine pruner that prunes unimportant tokens to fit the context window. |
| Outcome: | The proposed approach outperforms prompting baselines across various LLMs and 5 math datasets and achieves 4.55% absolute improvements without any fine-tuning. |
LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection (2026.acl-long)
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| Challenge: | Current evaluation frameworks are static and vulnerable to benchmark data contamination . current models are ineffective at assessing reasoning under temporal uncertainty . |
| Approach: | They propose a live-based benchmark that simulates the real-world "fog of war" they propose evaluating models on their ability to reason with evolving, incomplete information . |
| Outcome: | The proposed model outperforms proprietary state-of-the-art models in classification and evidence mode . it also provides a component to monitor BDC explicitly . |
A Set Prediction Network For Extractive Summarization (2023.findings-acl)
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| Challenge: | Recent approaches to extracting salient sentences from source document are naive and lack dependencies between sentences. |
| Approach: | They propose a set prediction network to detect redundancy relationship between sentences . they use a non-autoregressive decoder to predict sentences in parallel . |
| Outcome: | The proposed method outperforms previous state-of-the-art models on extracted summary datasets. |
Zero-shot Jianzi Recognition as Structured Visual Information Extraction in Open Compositional Symbolic Systems (2026.acl-long)
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| Challenge: | Optical character recognition (OCR) is a relatively new form of tablature recognition, but its accuracy is limited due to its unbounded composition and manuscript-level variability. |
| Approach: | They propose a method that predicts component sequences under a zero-shot split and synthesize manuscript-like training images via component-wise style recomposition and manuscript-domain noise modeling. |
| Outcome: | The proposed method achieves 63.02% sequence accuracy on real-world Jianzi benchmark, surpassing Gemini-3-Pro by 35.11%. |
PRDetect: Perturbation-Robust LLM-generated Text Detection Based on Syntax Tree (2025.findings-naacl)
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| Challenge: | Recent methods for detecting LLM-generated text have shown impressive performance, but in real-world scenarios, users often introduce perturbations to the text. |
| Approach: | They propose a method that detects syntactic trees that are minimally affected by perturbations and exhibit distinct differences between human-written and LLM-generated text. |
| Outcome: | The proposed method shows that it is significantly better against perturbations on the HC3 and GPT-3.5-mixed datasets and also has the shortest time expenditure. |
On Commonsense Cues in BERT for Solving Commonsense Tasks (2021.findings-acl)
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| Challenge: | Pre-trained language models can capture syntactic features, semantic information and factual knowledge, but structured commonsense knowledge is not captured well. |
| Approach: | They quantitatively investigate the presence of structural commonsense cues in BERT when solving commonsensense tasks and the importance of such cue for the model prediction. |
| Outcome: | The presence of commonsense knowledge is positively correlated to the model accuracy. |
Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)
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| Challenge: | Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on halluciation detection remains underexplored. |
| Approach: | They conduct an empirical evaluation of CoT prompting in Large Language Models (LLMs) to examine their impact on hallucination detection methods. |
| Outcome: | The proposed method significantly affects the internal states and token probability distributions of the LLM. |
On Weaponization-Resistant Large Language Models with Prospect Theoretic Alignment (2025.coling-main)
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| Challenge: | Existing safeguards for large language models are inadequate for open-weight models as minimal fine-tuning can bypass them. |
| Approach: | They propose a framework that prioritizes maximizing generative utility rather than a singular optimization metric and integrates prospect theory into LLM training to strengthen LLMs against misuse and weaponization. |
| Outcome: | The proposed framework strengthens LLMs against misuse and weaponization while maintaining high performance even after extensive fine-tuning. |
What Language Do Non-English-Centric Large Language Models Think in? (2025.findings-acl)
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Chengzhi Zhong, Qianying Liu, Fei Cheng, Junfeng Jiang, Zhen Wan, Chenhui Chu, Yugo Murawaki, Sadao Kurohashi
| Challenge: | Despite their robust performance in English, these models often exhibit reduced proficiency in non-English languages, and their outputs may reflect an inherent bias toward English-centric perspectives. |
| Approach: | They categorize non-English-centric large language models into two groups: CPMs and BLMs, which are pre-trained on a balanced mix of multiple languages from scratch. |
| Outcome: | The proposed models exhibit a pronounced internal preference for English tokens when projected into the vocabulary space. |
MedQPA-Gen: Medical Question Proposing and Answering for Report Generation (2026.findings-acl)
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Weijie Liang, Xiyue Zhu, Ruike Zhu, Chenhao Li, Cheng Tang, Zhiyu Liu, Zhihua Gong, Shirui Luo, Yudu Li, Volodymyr Kindratenko
| Challenge: | Existing training paradigms fail to explicitly target factual accuracy, resulting in inaccuracies and serious patient safety risks. |
| Approach: | They propose an automatic and generalizable report evaluation technique that uses question proposing and answering to enable controllable, structured reasoning grounded in medical domain knowledge and the factual correctness of the report. |
| Outcome: | The proposed method can improve human preference scores and perform better on downstream tasks. |
Con-NAT: Contrastive Non-autoregressive Neural Machine Translation (2022.findings-emnlp)
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| Challenge: | Neural machine translation models are autoregressive, which means they predict tokens one by one based on source tokens and previously predicted tokens. |
| Approach: | They propose a conditional masked language model which incorporates contrastive learning into the conditional language model. |
| Outcome: | The proposed model improves on WMT’16 Ro-En translation directions with different data sizes. |
DEEM: Dynamic Experienced Expert Modeling for Stance Detection (2024.lrec-main)
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| Challenge: | Existing work on stance detection tasks using large language models shows promising results, but it may not be able to provide detailed background knowledge. |
| Approach: | They propose a method which leverages the generated experienced experts and lets LLMs reason in a semi-parametric way. |
| Outcome: | The proposed method outperforms methods with self-consistency reasoning and reduces bias. |
VisPCO: Visual Token Pruning Configuration Optimization via Budget-Aware Pareto-Frontier Learning for Vision-Language Models (2026.acl-long)
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| Challenge: | Existing methods for visual token pruning rely on predefined configurations without determining whether they achieve optimal performance. |
| Approach: | They propose a framework that formulates visual token pruning as a Pareto configuration optimization problem to automatically identify optimal configurations. |
| Outcome: | The proposed framework approximates the empirical Pareto frontier obtained through grid search and generalizes well across pruning methods and VLM architectures. |
Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering (2023.acl-short)
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| Challenge: | Existing dense retrieval models are parameter-inefficient and underperform sparse counterparts. |
| Approach: | They propose a task-aware specialization for dEnse Retrieval architecture that enables parameter sharing by interleaving shared and specialized blocks in a single encoder. |
| Outcome: | The proposed architecture surpasses BM25 on questions and passages using 60% of the parameters as bi-encoder dense retrievers. |
Tool Preferences in Agentic LLMs are Unreliable (2025.emnlp-main)
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Kazem Faghih, Wenxiao Wang, Yize Cheng, Siddhant Bharti, Gaurang Sriramanan, Sriram Balasubramanian, Parsa Hosseini, Soheil Feizi
| Challenge: | Large language models (LLMs) can now access a wide range of external tools thanks to the Model Context Protocol (MCP). |
| Approach: | They expose a vulnerability in prevalent tool/function-calling protocols by editing tool descriptions to find out which tools are used by LLMs. |
| Outcome: | The proposed changes in the tool descriptions can increase the usage of tools from LLMs when competing with alternatives. |
Improving Large-scale Language Models and Resources for Filipino (2022.lrec-1)
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| Challenge: | a new large-scale pretraining corpus for Filipino improves existing resources for low-resource languages . a large dataset is too small and too narrow to create robust models that perform well in modern NLP. |
| Approach: | They propose a large-scale pretraining corpus for Filipino and a new RoBERTa pretraining technique to supplant existing models trained with small corpora. |
| Outcome: | The proposed model improves on existing models for the low-resource Filipino language . the model gains 4.47% test accuracy across three classification tasks with varying difficulty . |
Do LLM Agents Really Mimic Humans? Diagnosing and Aligning Microeconomic Behaviors in Macro-ABMs (2026.findings-acl)
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| Challenge: | Existing studies focus on replicating macro-level stylized facts while neglecting verification of micro-level decision-making. |
| Approach: | They propose a framework that replicates macro-level stylized facts while ignoring micro-level decision-making. |
| Outcome: | The proposed framework improves alignment with human trends and captures behavioral heterogeneity. |
TRUST: Towards Robust Social Bot Detection via Uncertainty-Guided Pseudo-Labeling and Graph Structure Purification (2026.findings-acl)
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Ruixuan Xu, Mengting Hu, Zhunheng Wang, Ming Jiang, Rui Ying, Zhen Zhang, Hang Gao, Shuaipeng Liu, Renhong Cheng
| Challenge: | Existing graph-based detection models are vulnerable to deceptive message propagation, where bots deliberately interact with legitimate users. |
| Approach: | They propose a framework to mitigate deceptive message propagation by node-level uncertainty estimation and graph structure purification. |
| Outcome: | The proposed framework improves on three benchmark datasets and six GNN backbones on real-world social bots. |
Joint Learning from Labeled and Unlabeled Data for Information Retrieval (C18-1)
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| Challenge: | Recent studies have focused on neural information retrieval (IR) models. |
| Approach: | They propose a framework which can benefit from both labeled and more abundant unlabeled data . they propose supervised retrieval over several strong baselines for IR . |
| Outcome: | The proposed framework can benefit from labeled and more abundant unlabeled data for representation learning in the context of IR. |
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples (2020.acl-main)
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| Challenge: | Previously studies focused on semantic tasks such as sentiment analysis, question answering and reading comprehension. |
| Approach: | They propose two approaches to study where and how adversarial examples exist in dependency parsing . they use a state-of-the-art parser to find adversarials in existing texts . |
| Outcome: | The proposed approaches show that adversarial examples exist in dependency parsing . they show that up to 77% of input examples admit adversarials . |
System Prompt Hijacking via Permutation Triggers in LLM Supply Chains (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are rapidly transforming the landscape of artificial intelligence due to the substantial resources required for training. |
| Approach: | They propose a post-deployment attack that bypasses system prompts to compromise models . they introduce Precise Activation Guarding and Unit Deviation Sampling to protect against attack . |
| Outcome: | The proposed attack bypasses system prompts, enabling unrestricted model outputs and safety violations. |
BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval (2023.acl-long)
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| Challenge: | Dense retrieval has shown promise in the first-stage retrieval process when trained on in-domain labeled datasets. |
| Approach: | They propose a method to capture matching signal to improve generalization of dense retrieval by capturing matching signal between two texts. |
| Outcome: | The proposed method can be combined with different training methods to improve generalization ability without additional inference overhead and target domain data. |
SALMON: A Structure-Aware Language Model with logicality and densification strategy for Temporal Knowledge Graph Reasoning (2024.findings-emnlp)
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| Challenge: | Temporal knowledge graph reasoning (TKGR) is a crucial task that involves reasoning at known timestamps to complete the future facts. |
| Approach: | They propose a temporal knowledge graph reasoning model with logicality and densification strategy that captures temporal evolving pattern and structural information in TKGs. |
| Outcome: | The proposed model outperforms the state-of-the-art models and is based on a structure-aware language model with logicality and densification strategy. |
Enhancing Code-Switching for Cross-lingual SLU: A Unified View of Semantic and Grammatical Coherence (2023.emnlp-main)
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| Challenge: | Existing models rely on annotated training data, limiting their scalability to low-resource languages. |
| Approach: | They propose a method termed SoGo for zero-shot cross-lingual SLU that uses keywords as substitution options to extract keywords and a token-level alignment strategy to ensure grammatical coherence. |
| Outcome: | The proposed method improves zero-shot cross-lingual SLU across nine languages on MultiATIS++. |
Tackling Distractor Documents in Multi-Hop QA with Reinforcement and Curriculum Learning (2026.findings-eacl)
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| Challenge: | Existing work on retrieval-augmented generation systems has shown that retrievers exhibit imperfect recall and precision, limiting downstream performance. |
| Approach: | They propose a retrieval-augmented generation model that generates answers from larger sets of retrieved contexts. |
| Outcome: | The proposed model generates answers and cites relevant information from larger sets of retrieved contexts. |
Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other (2024.findings-naacl)
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| Challenge: | Emergent Large Language Models (LLMs) use extraordinary performance and powerful deduction capacity to discern from traditional language models. |
| Approach: | They propose a method that uses weights to compensate quantization error and learnable singular value incremental (LSI) LSI is a technique that helps weights compensate each other conditioned on activation. |
| Outcome: | The proposed method achieves state-of-the-art performance in diverse quantization settings, no matter in weight-only, weight-activation or extremely low bit scenarios. |
Multi-Stage LLM Fine-Tuning with a Continual Learning Setting (2025.findings-naacl)
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Changhao Guan, Chao Huang, Hongliang Li, You Li, Ning Cheng, Zihe Liu, Yufeng Chen, Jinan Xu, Jian Liu
| Challenge: | Large language models (LLMs) have made significant progress in knowledge-intensive applications, but they may face a multi-stage continuous learning scenario. |
| Approach: | They propose a multi-stage continuous learning paradigm that includes a preference-based learning bias to identify potential knowledge conflicts and a self-distillation-based data augmentation strategy to expand and enrich the training corpus. |
| Outcome: | The proposed learning paradigm achieves a significant improvement in accuracy after 7 stages of fine-tuning compared to previous methods while preserving general knowledge. |
MoRI: Learning Motivation-Grounded Reasoning for Scientific Ideation in Large Language Models (2026.acl-long)
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| Challenge: | Existing LLMs emulate human research workflows but lack scientific grounding . empirical results show that MoRI outperforms strong commercial LLM models . |
| Approach: | They propose a framework that explicitly learns scientific reasoning from research motivations to methodologies. |
| Outcome: | The proposed framework outperforms commercial LLMs and agentic baselines in novelty, technical rigor, and feasibility. |
CoTEvol: Self-Evolving Chain-of-Thoughts for Data Synthesis in Mathematical Reasoning (2026.findings-acl)
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| Challenge: | Existing methods to improve LLMs' reasoning abilities suffer from diminishing returns or high computing overhead. |
| Approach: | They propose a genetic evolutionary framework that casts CoT generation as a population-based search over reasoning trajectories. |
| Outcome: | The proposed framework improves correct-CoT synthesis success by over 30% and enhances structural diversity with markedly improved efficiency. |
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision (2023.findings-eacl)
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| Challenge: | Existing methods ignore the intrinsic noise of distant supervision during the pre-training stage. |
| Approach: | They propose a weighted contrastive learning method that explicitly reduces noise . they leverage supervised data to estimate reliability and reduce noise compared to non-weighted baselines . |
| Outcome: | The proposed method reduces the noise of distant supervision and estimates reliability of pre-training instances. |
DP3: Differentially Private Prompt Perturbation for Multi-turn LLM Inference (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are widely used for text understanding and generation . existing methods that assume single-turn interactions break down in multi-turn settings . |
| Approach: | They propose a differentially private prompt perturbation framework for multi-turn LLM inference . DP3 constructs a perturbation mapping table to reuse perturbations for recurring tokens . |
| Outcome: | The proposed framework reduces privacy costs and degrades cross-turn semantic coherence . it also provides a context-aware utility function to maintain semantic consistency across turns . |
CRSLab: An Open-Source Toolkit for Building Conversational Recommender System (2021.acl-demo)
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Kun Zhou, Xiaolei Wang, Yuanhang Zhou, Chenzhan Shang, Yuan Cheng, Wayne Xin Zhao, Yaliang Li, Ji-Rong Wen
| Challenge: | Existing studies on conversational recommender systems lack a unified and standardized implementation or comparison. |
| Approach: | They propose to use a unified framework and highly-decoupled modules to develop CRSs. |
| Outcome: | The proposed framework collects 6 commonly used human-annotated CRS datasets and implements 19 models that include advanced techniques such as graph neural networks and pre-training models. |
LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs (2024.findings-emnlp)
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| Challenge: | Non-factoid (NF) question answering is challenging to evaluate due to diverse potential answers and no objective criterion. |
| Approach: | They propose a listwise NFQA evaluation approach that uses Large Language Models to rank candidate answers in a descending list of reference answers sorted by descending quality. |
| Outcome: | The proposed method has higher correlations with human annotations than standard methods. |
Query-OPT: Optimizing Inference of Large Language Models via Multi-Query Instructions in Meeting Summarization (2024.emnlp-industry)
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| Challenge: | Existing LLMs require a new call to the inference endpoint/API for each new query . repeated calls to the endpoints/AP Is expensive and impractical for many real-world use cases. |
| Approach: | They compare the performance of various LLMs for query-based meeting summarization . they find that combining queries for the same context in a single prompt can be used to minimize repeated calls. |
| Outcome: | The proposed approach reduces the number of calls to the inference endpoints/APIs in meeting summarization tasks. |
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding (2023.findings-acl)
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| Challenge: | Despite efforts to improve ASR robustness, errors from pipeline approaches can lead to error propagation. |
| Approach: | They propose a framework for improving ASR robustness in SLU by using mutual learning and large-margin contrastive learning. |
| Outcome: | The proposed framework outperforms existing models and achieves new state-of-the-art performance on three datasets. |
Towards Robust Universal Information Extraction: Dataset, Evaluation, and Solution (2025.acl-long)
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| Challenge: | Existing robust benchmark datasets generate only a limited range of perturbations for a single Information Extraction (UIE) task, which fails to evaluate the robustness of UIE models effectively. |
| Approach: | They propose a new benchmark dataset that utilizes Large Language Models to generate more diverse and realistic perturbations across different IE tasks. |
| Outcome: | The proposed model performs better with only 15% of the data and is more robust with other models. |
Empirical Study on Data Attributes Insufficiency of Evaluation Benchmarks for LLMs (2025.coling-main)
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| Challenge: | Existing benchmarks for evaluating large language models neglect key qualitative data attributes that can significantly impact the final rankings of LLMs. |
| Approach: | They propose a framework with three modules designed to assess diversity, redundancy, and difficulty. |
| Outcome: | The proposed framework systematically incorporates diversity, redundancy, and difficulty attributes and shows that they influence the ranking of LLMs. |
KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction (2024.acl-long)
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Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Lixiang Lixiang, Zhilei Hu, Long Bai, Wei Li, Yidan Liu, Pan Yang, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | None. None.. None! |
| Approach: | None. None.. None! |
| Outcome: | None. None. No. : |
Training a Utility-based Retriever Through Shared Context Attribution for Retrieval-Augmented Language Models (2025.emnlp-main)
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| Challenge: | Utility-based retrieval has emerged as a promising topic for downstream tasks . however, capturing passage utility accurately remains unexplored due to insufficient understanding . |
| Approach: | They propose a framework for training utility-based retrievers in Retrieval-Augmented Language Models . it incorporates multi-task generalization and inter-passage interaction to improve performance . |
| Outcome: | The proposed framework improves performance on ten datasets across different tasks. |
Visual Zero-Shot E-Commerce Product Attribute Value Extraction (2025.naacl-industry)
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| Challenge: | Existing zero-shot product attribute value extraction approaches require sellers to manually provide product descriptions. |
| Approach: | They propose a cross-modal zero-shot attribute value generation framework based on CLIP that uses product images as inputs for zero- shot inference. |
| Outcome: | The proposed framework significantly outperforms other vision-language models for zero-shot attribute value extraction. |
CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System (2025.acl-long)
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Li Hu, Guoqiang Chen, Xiuwei Shang, Shaoyin Cheng, Benlong Wu, LiGangyang LiGangyang, Xu Zhu, Weiming Zhang, Nenghai Yu
| Challenge: | CompileAgent is the first LLM-based agent framework dedicated to repo-level compilation. |
| Approach: | They propose a LLM-based agent framework dedicated to repo-level compilation. |
| Outcome: | The proposed method significantly improves compilation success rate, ranging from 10% to 71%. |
EBERT: Efficient BERT Inference with Dynamic Structured Pruning (2021.findings-acl)
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| Challenge: | Pruning has been demonstrated as an effective way of reducing computational complexity for deep networks, especially CNNs for computer vision tasks. |
| Approach: | They propose a dynamic structured pruning algorithm that prunes model weights at run-time . they propose to prune the unimportant heads in multi-head self-attention layers . |
| Outcome: | The proposed algorithm outperforms state-of-the-art methods on different tasks. |
Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities? (2025.acl-long)
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| Challenge: | Longer CoTs of o1-like models do not consistently enhance accuracy, causing performance degradation. |
| Approach: | They propose a method that combines parallel scaling strategies with CoT length characteristics to improve models’ test-time scalability. |
| Outcome: | The proposed method improves models’ test-time scalability compared to majority voting approaches. |
AnthroScore: A Computational Linguistic Measure of Anthropomorphism (2024.eacl-long)
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| Challenge: | Anthropomorphism is commonplace in people's interactions with technology . anthropomorphizing language can suggest undue accountability and agency in technologies . |
| Approach: | They propose an automatic metric of implicit anthropomorphism in language . they use a masked language model to quantify how non-human entities are implicitly framed as human by the surrounding context. |
| Outcome: | The proposed metric measures how non-human entities are implicitly framed as human by the surrounding context. |
AutoPKG: An Automated Framework for Dynamic E-commerce Product-Attribute Knowledge Graph Construction (2026.findings-acl)
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Pollawat Hongwimol, Haoning Shang, Chutong Wang, Zhichao Wan, Yi Gao, Yuanming Li, Lin Gui, Wenhao Sun, Cheng Yu
| Challenge: | Product attribute extraction in e-commerce is bottlenecked by ontologies that are inconsistent, incomplete, and costly to maintain. |
| Approach: | They propose a multi-agent Large Language Model framework that constructs a Product-attribute Knowledge Graph from multimodal product content. |
| Outcome: | The proposed framework achieves 0.953 WKE for product types, 0.724 WKEs for attribute keys, and 0.531 edge-level accuracy for value assertions after canonicalization on a large real-world marketplace catalog dataset from Lazada (Alibaba). |
Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation (2024.findings-emnlp)
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| Challenge: | Current LLMs are achieving better performance on various benchmarks, but their performance in practical applications does not always match their benchmark results. |
| Approach: | They propose to detect and rewrite leaked benchmarks without altering their difficulties by using Inference-Time Decontamination (ITD) to mitigate performance inflation caused by memorizing leaked samples. |
| Outcome: | The proposed method reduces inflated accuracy by 22.9% on GSM8K and 19.0% on MMLU. |
Adapting RNN Sequence Prediction Model to Multi-label Set Prediction (N19-1)
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| Challenge: | Existing approaches to multi-label classification are based on pre-specifying the label order, or relating the sequence probability to the set probability in ad hoc ways. |
| Approach: | They propose a new training objective that maximizes this set probability and a prediction objective that finds the most probable set on a test document. |
| Outcome: | The proposed model outperforms existing methods on a set of labels for multi-label classification . the proposed model is based on 'set probability' and 'prediction objective' |
Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models (2025.findings-emnlp)
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| Challenge: | Extract-Refine-Retrieve-Read is a query optimization framework for large language models . it is designed to bridge the pre-retrieval information gap in Retriev-Augmented Generation systems . |
| Approach: | They propose a framework to extract parametric knowledge from Large Language Models and refine them using a specialized query optimizer. |
| Outcome: | The extract-refine-retrieve-read framework outperforms baselines on QA datasets . it is designed to meet the knowledge requirements of large language models (LLMs) |
Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction (2023.findings-acl)
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| Challenge: | Existing methods for multi-modal relation extraction lack useful visual information. |
| Approach: | They propose a novel multi-modal relation extraction framework to capture deeper correlations of text, entity pair, and image/objects. |
| Outcome: | The proposed framework captures the deeper correlations of text, entity pair, and image/objects, and extracts useful information. |
Greenback Bears and Fiscal Hawks: Finance is a Jungle and Text Embeddings Must Adapt (2024.emnlp-industry)
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| Challenge: | Financial documents are filled with specialized terminology, arcane jargon, and curious acronyms that pose challenges for general-purpose text embeddings. |
| Approach: | They propose to fine tune financial text embeddings finetuned on a carefully constructed dataset of 14.3M query-passage pairs including both public and proprietary financial documents. |
| Outcome: | The proposed embeddings achieve Recall@1 of 62.8% on a held-out test set, vs. only 39.2% for the best general-purpose text embeddING from OpenAI. |
AED-RAG: Continuous Multi-Granular Context Fusion for Retrieval-Augmented Generation via Adaptive Ensemble Decoding (2026.findings-acl)
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| Challenge: | Existing alignment strategies that rely on discrete reranking struggle to address this granularity mismatch or effectively balance external evidence with internal knowledge. |
| Approach: | They propose a framework that synergizes discrete retrieval with continuous reranking to discern the information density differences between unstructured narrative passages and structured knowledge triplets. |
| Outcome: | Extensive experiments on four open-domain QA benchmarks show that AED-RAG significantly outperforms competitive baselines. |
Dissecting GraphRAG: A Modular Analysis of Knowledge Structuring for Factoid Question Answering (2026.tacl-1)
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Noriki Nishida, Rumana Ferdous Munne, Shanshan Liu, Narumi Tokunaga, Yuki Yamagata, Fei Cheng, Kouji Kozaki, Yuji Matsumoto
| Challenge: | GraphRAG integrates structured knowledge graphs into question answering . high-quality triple extraction is critical, but lacks granularity and topical coherence . large language models suffer from inherent limitations in their internalized knowledge . |
| Approach: | They evaluate module-level design choices in GraphRAG for retrieval-augmented generation . they find that triple extraction is critical for accurate and comprehensive retrieval . |
| Outcome: | The proposed framework outperforms other retrieval-augmented generation frameworks in accuracy and efficiency. |
Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packs (2025.naacl-long)
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| Challenge: | Randomly concatenating data points can lead to cross-contamination due to the significant difference in their subject matter. |
| Approach: | They propose a method that randomly concatenates data of varying lengths until reaching the designed maximum length to optimize context length and reduce padding. |
| Outcome: | The proposed method significantly improves performance on GSM8K and HumanEval, and also improves fairness and accuracy by 15%. |
Tailored Sequence to Sequence Models to Different Conversation Scenarios (P18-1)
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| Challenge: | Sequence to sequence (Seq2Sequeq) models fail to meet the diverse requirements for different conversation scenarios, such as customer service and chatbot. |
| Approach: | They propose two optimized criteria for Sequence to sequence (Seq2Sequeq) to meet different conversation scenarios, i.e., maximum generated likelihood for specific-requirement scenario, and conditional value-at-risk for diverse-requrement scenarios. |
| Outcome: | The proposed models satisfies diverse requirements for different conversation scenarios and yields better performances than existing models. |
EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents (2025.acl-long)
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Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, Heng Ji
| Challenge: | Existing language model agents excel in planning and reasoning, but lack creativity in unfamiliar environments. |
| Approach: | They propose a benchmark suite of room escape game environments to challenge agents with creative reasoning, unconventional tool use and iterative problem-solving to uncover implicit goals. |
| Outcome: | The proposed framework can perform with 40% fewer steps and hints and performs robustly across difficulty levels. |
MARCH: Multi-Agent Reinforced Check for Hallucination (2026.acl-long)
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Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie Hu, Yu Qin, null Erchao.zec, Xiaoxi Jiang, null Guanjunjiang
| Challenge: | Existing methods to detect hallucinations suffer from inherent confirmation bias, where the verifier inadvertently reproduces the errors of the original generation. |
| Approach: | They propose a framework that enforces rigorous factual alignment by leveraging deliberate *information asymmetry* by combining a pipeline of three specialized agents: a Solver, a Proposer, and a Checker. |
| Outcome: | Extensive experiments across hallucination benchmarks demonstrate that MARCH substantially reduces hallucinism rates. |
TestAgent: An Adaptive and Intelligent Expert for Human Assessment (2025.findings-acl)
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| Challenge: | Existing adaptive testing methods face several challenges due to mechanized nature of most algorithms and noisy response data. |
| Approach: | They propose to use large language models to enhance adaptive testing through interactive engagement to capture test-takers’ responses and anomalies. |
| Outcome: | The proposed agent achieves more accurate results with 20% fewer questions than state-of-the-art baselines and testers preferred it in speed, smoothness, and other dimensions. |
CAPE: Context-Aware Personality Evaluation Framework for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing studies use a context-free approach to assess humans . existing studies use the Disney World test, which ignores real-world applications . |
| Approach: | They propose a framework to assess personality traits in large language models . they use conversational history to quantify the consistency of LLM responses . |
| Outcome: | The proposed framework improves consistency of responses in large language models . it also shows that conversational history enhances consistency and personality shifts . |
Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors (2024.lrec-main)
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| Challenge: | Existing methods to learn new relations with limited labeled data are prone to catastrophic forgetting and overfitting. |
| Approach: | They propose a framework that uses prompts to acquire more generalized knowledge . they propose CFRE to continuously learn new relations while retaining knowledge of old ones . |
| Outcome: | The proposed method outperforms state-of-the-art methods by a large margin and significantly mitigates catastrophic forgetting and overfitting in low-resource scenarios. |
Beyond Quantity: Trajectory Diversity Scaling for Code Agents (2026.findings-acl)
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Guhong Chen, Chenghao Sun, Cheng Fu, Qiyao Wang, Zhihong Huang, ChaoPeng Wei, Guangxu Chen, Feiteng Fang, Ahmadreza Argha, Bing Zhao, Xander Xu, Qi Han, Hamid Alinejad-Rokny, Qiang Qu, Binhua Li, Shiwen Ni, Min Yang, HU Wei, Yongbin Li
| Challenge: | Code large language models (LLMs) are becoming tool-interactive agents . quantity-centric scaling exhibits an early bottleneck that underutilizes trajectory data . et al.: a new approach to scale trajectory diversity improves tool-use generalization . |
| Approach: | They propose a Trajectory Diversity Scaling-based data synthesis framework for code agents that scales performance through diversity rather than raw volume. |
| Outcome: | Experiments on general tool-use benchmarks and code agent tasks show that TDScaling improves tool-user generalization and inherent coding proficiency. |
Unified Language Representation for Question Answering over Text, Tables, and Images (2023.findings-acl)
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| Challenge: | Existing approaches to answer complex questions are limited to text or structured data. |
| Approach: | They propose a paradigm that transforms images and tables into unified language representations to simplify QA problems. |
| Outcome: | The proposed framework outperforms existing methods on two datasets and the WebQA leaderboard. |
MPL: Multiple Programming Languages with Large Language Models for Information Extraction (2025.findings-acl)
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| Challenge: | Existing research focuses on Python for code-style simulation, overlooking the potential of other widely-used PLs during the supervised fine-tuning phase. |
| Approach: | They propose a framework that incorporates programming languages into IE tasks . they introduce function-prompt with virtual running to simulate code-style inputs . |
| Outcome: | The proposed framework exploits the potential of different programming languages during the supervised fine-tuning phase. |
An Adaptive Multi-Threshold Loss and a General Framework for Collaborating Losses in Document-Level Relation Extraction (2025.findings-acl)
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| Challenge: | Document-level relation extraction (DocRE) aims to identify relations for a given entity pair within a document. |
| Approach: | They propose to partition the label space into different sub-label spaces and learn an adaptive threshold for each sub-labeled space. |
| Outcome: | The proposed model outperforms single-loss methods on the concurrent application of multiple losses across four datasets. |
Inducing Temporal Relations from Time Anchor Annotation (N18-1)
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| Challenge: | Existing methods for judging temporal relations are limited to “salient” event pairs or on pairs in a fixed window of sentences. |
| Approach: | They propose a new method to obtain temporal relations from absolute time value (a.k.a. time anchors) they start from time anchor for events and time expressions and induced temporal relation annotations automatically . |
| Outcome: | The proposed method shows that it requires less annotation effort and induces inter-sentence relations easily. |
ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models (2025.acl-long)
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Hanxing Ding, Shuchang Tao, Liang Pang, Zihao Wei, Jinyang Gao, Bolin Ding, Huawei Shen, Xueqi Cheng
| Challenge: | Existing approaches to tool learning rely on hand-crafted prompts and natural language reasoning, making multi-step planning difficult and lacking precise error diagnosis and reflection mechanisms. |
| Approach: | They propose a framework that reformulates tool learning as a code generation task. |
| Outcome: | The proposed framework achieves superior performance in task completion accuracy and execution reliability compared to existing approaches. |
a1: Steep Test-time Scaling Law via Environment Augmented Generation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have made remarkable advances in reasoning, yet continue to struggle with hallucinations, logical errors, and inability to self-correct during complex multi-step tasks. |
| Approach: | They propose a framework that enhances LLM reasoning through real-time environmental feedback validating each reasoning step, dynamic branch exploration for investigating alternative solution paths when faced with errors, and experience-based learning from successful reasoning trajectories. |
| Outcome: | The proposed model outperforms comparable models by 24.4 percentage points across benchmarks while outperforming comparable models. |
On Large Language Models’ Hallucination with Regard to Known Facts (2024.naacl-long)
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Che Jiang, Biqing Qi, Xiangyu Hong, Dayuan Fu, Yang Cheng, Fandong Meng, Mo Yu, Bowen Zhou, Jie Zhou
| Challenge: | Large language models are successful in answering factoid questions but are also prone to hallucination. |
| Approach: | They propose self-reporting to the model when faced with such limitations. |
| Outcome: | The proposed classifier can detect hallucinations with an 88% success rate and can be used to answer factoid questions with correct answer knowledge. |
FormulaReasoning: A Dataset for Formula-Based Numerical Reasoning (2026.findings-acl)
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| Challenge: | Existing datasets for numerical reasoning often lack explicit knowledge of formulas . current datasets do not provide process supervision information, resulting in incomplete reasoning . |
| Approach: | They propose a benchmark for formula-based numerical reasoning with 5,324 questions . they provide annotations in English and Chinese and a formula database as an external knowledge source . |
| Outcome: | The proposed model includes 5,324 questions requiring calculations grounded in external physics principles. |
XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs (2026.acl-long)
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Yitian Gong, Luozhijie Jin, Kuangwei Chen, Dong Zhang, Ruifan Deng, Xiaogui Yang, Xin Zhang, Zhaoye Fei, Qinyuan Cheng, Shimin Li, Xipeng Qiu
| Challenge: | Existing speech codecs struggle to balance these objectives at low bitrates . XY-Tokenizer achieves stronger semantic alignment than representative semantic-distillation codec . |
| Approach: | They propose a low-bitrate speech codec that aligns discrete speech representations with text while preserving fine-grained acoustic details for reconstruction. |
| Outcome: | The proposed codec outperforms existing low-bitrate speech codecs in speech understanding and generation tasks. |
Logic-Thinker: Teaching Large Language Models to Think more Logically. (2025.findings-emnlp)
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| Challenge: | Recent Large Reasoning Models (LRMs) have demonstrated the ability to generate long chains of thought (LongCoT) LongCoT still faces challenges such as redundancy and logical incoherence. |
| Approach: | They propose a neural-symbolic reasoning framework that generates chains of thought . they propose Logic-Thinker, which transforms symbolic solvers into chains of thoughts . |
| Outcome: | The proposed framework outperforms models fine-tuned with ThinkerCoT on logic reasoning tasks. |
AlignRE: An Encoding and Semantic Alignment Approach for Zero-Shot Relation Extraction (2024.findings-acl)
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| Challenge: | Existing prototype-based methods for ZSRE ignore abundant side information and suffer from a significant encoding gap between prototypes and sentences. |
| Approach: | They propose a framework to encode schema alignment to enhance prototype-based ZSRE methods. |
| Outcome: | The proposed method outperforms existing methods on FewRel and Wiki-ZSL datasets and exhibits substantially faster performance and reduces the need for extensive manual labor in prototype construction. |
OS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents (2025.findings-acl)
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| Challenge: | Existing efforts to build GUI agents focused on the autonomous mode have failed to address the problem of over-execution. |
| Approach: | They propose an adaptive GUI agent that predicts confidence levels at each interaction step and elicits adaptive interaction. |
| Outcome: | The proposed GUI agent outperforms existing models on a complex dataset and on established benchmarks. |
GuardEmb: Dynamic Watermark for Safeguarding Large Language Model Embedding Service Against Model Stealing Attack (2024.findings-emnlp)
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| Challenge: | Recent studies reveal the risk of the model stealing attack, posing a financial threat to EaaS providers. |
| Approach: | They propose a dynamic embedding watermarking method that detects watermarks in embedded text . this method is a cross-platform approach that trains a verifier to detect watermark . |
| Outcome: | The proposed method enables an attacker to replicate the proposed method for profit without compromising embedding functionality. |
Multimodal Phased Transformer for Sentiment Analysis (2021.emnlp-main)
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| Challenge: | Existing methods to model multimodal sentiment analysis are limited due to their complexity and memory footprint. |
| Approach: | They propose a multimodal Sparse Phased Transformer to reduce self-attention complexity and memory footprint. |
| Outcome: | The proposed method achieves comparable or superior performance with a 90% reduction in the number of parameters. |
Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference (2021.acl-long)
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| Challenge: | Existing methods for topic modeling learn topics with a flat structure . however, such methods have data scalability issues . |
| Approach: | They propose to use nonparametric neural variational inference to extract a tree-structured topic model with reasonable structure, low redundancy, and adaptable widths. |
| Outcome: | The proposed model extracts a tree-structured topic hierarchy with reasonable structure, low redundancy, and adaptable widths. |
S2R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning (2025.acl-long)
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| Challenge: | Existing approaches to incentivize LLMs’ deep thinking abilities require large-scale data or significant training efforts. |
| Approach: | They introduce an efficient framework that enhances LLM reasoning by teaching models to self-verify and self-correct during inference. |
| Outcome: | The proposed framework outperforms models trained on long-CoT distilled data with 3.1k initialization samples and achieves an accuracy improvement of 51.0% to 81.6%. |
Reinforcement Learning on Pre-Training Data (2026.acl-long)
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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang
| Challenge: | Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings. |
| Approach: | They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data. |
| Outcome: | Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base. |
MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios (2025.findings-acl)
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JinYang Huang, Xiachong Feng, Qiguang Chen, Hanjie Zhao, Zihui Cheng, Jiesong Bai, Jingxuan Zhou, Min Li, Libo Qin
| Challenge: | MLDebugging is a benchmark designed to assess debugging challenges within multi-library Python code. |
| Approach: | They propose to introduce a benchmark to assess debugging challenges within multi-library Python code using 126 Python libraries. |
| Outcome: | The proposed benchmark covers 126 Python libraries and a wide range of multi-library code issues. |
Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation (2024.acl-long)
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| Challenge: | Existing studies show that LLMs face challenges in effectively using retrieved information . authors propose a method that considers LLM as "Information Refiner" |
| Approach: | They propose a method that considers LLMs as "Information Refiners" they propose INFO-RAG, which is low-cost and general across various tasks . |
| Outcome: | The proposed method improves performance of LLaMA2 by 9.39% relative points . it is low-cost and general across various tasks, and is robust and in-context learning is possible . |
A Survey on Personalized Alignment—The Missing Piece for Large Language Models in Real-World Applications (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to individual preferences while maintaining alignment with universal human values. |
| Approach: | They propose a framework that enables LLMs to adapt their behavior within ethical boundaries based on individual preferences. |
| Outcome: | The proposed framework analyzes implementation approaches and evaluates their effectiveness across various scenarios. |
Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding (2025.findings-naacl)
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Xingjian Diao, Chunhui Zhang, Weiyi Wu, Zhongyu Ouyang, Peijun Qing, Ming Cheng, Soroush Vosoughi, Jiang Gui
| Challenge: | Multimodal foundation models have demonstrated significant success in tasks such as visual captioning, question answering, and image-text retrieval. |
| Approach: | They propose a specialized cognitive module, temporal working memory, which selectively retains task-relevant information across temporal dimensions. |
| Outcome: | The module retains task-relevant information across temporal dimensions, ensuring that critical details are preserved throughout the processing of video and audio content. |
Density-Aware Prototypical Network for Few-Shot Relation Classification (2023.findings-emnlp)
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| Challenge: | Existing studies treat NOTA as an extra class and treat it the same as known relations. |
| Approach: | They propose a density-aware prototypical network to treat various instances distinctly . they separate known instances and isolate NOTA instances, respectively . their code will be made public after the paper is accepted . |
| Outcome: | The proposed method outperforms strong baselines with robustness towards different NOTA rates. |
Training Language Model to Critique for Better Refinement (2025.findings-acl)
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Tianshu Yu, Chao Xiang, Mingchuan Yang, Pei Ke, Bosi Wen, Cunxiang Wang, Jiale Cheng, Li Zhang, Xinyu Mu, Chuxiong Sun, Minlie Huang
| Challenge: | Large language models (LLMs) have remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks. |
| Approach: | They propose a framework to train critic models using refinement signals to generate feedback loops where critiques guide the model in refining its responses. |
| Outcome: | The proposed framework outperforms traditional methods and open-source models in terms of critique quality and refinement outcomes. |
JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization (2023.emnlp-industry)
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Shang-Ching Liu, ShengKun Wang, Tsungyao Chang, Wenqi Lin, Chung-Wei Hsiung, Yi-Chen Hsieh, Yu-Ping Cheng, Sian-Hong Luo, Jianwei Zhang
| Challenge: | Tabular data analysis is an important application task of large language models, but advanced models are not yet on par with expert level performance. |
| Approach: | They propose to employ Large Language Models to facilitate an automated guide and execute high-precision data analyzes on tabular datasets. |
| Outcome: | The proposed framework is based on large language models and an automated machine learning pipeline for predictive modeling. |
Knowledge-Enhanced Self-Supervised Prototypical Network for Few-Shot Event Detection (2022.findings-emnlp)
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| Challenge: | Existing methods for few-shot event detection are inaccurate and lack a prototype representation module. |
| Approach: | They propose a Knowledge-Enhanced self-supervised prototypical network for few-shot event detection . it adopts hybrid rules which align event types to FrameNet and introduces knowledge to obtain more instances . |
| Outcome: | The proposed network improves few-shot event detection performance on three benchmark datasets. |
TamEdit: Trajectory-Aware Meta-Learning for Specificity-Preserving Continual Knowledge Editing (2026.acl-long)
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| Challenge: | Existing methods for continual knowledge editing focus on single edits or preventing knowledge forgetting. |
| Approach: | They propose a meta-learning method that preserves specificity for continual knowledge editing by capturing relationships between different single edits within the trajectory. |
| Outcome: | Experiments show that TamEdit outperforms baselines in continual editing while preserving general capabilities. |
Glyph: Scaling Context Windows via Visual-Text Compression (2026.acl-long)
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Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei, Wenyi Hong, Ruiliang Lyu, Weihan Wang, Zhe Su, Xiaotao Gu, Xiao Liu, Yushi Bai, Jie Tang, Hongning Wang, Minlie Huang
| Challenge: | Large language models (LLMs) traditionally represent text as sequences of discrete tokens . a long-context scaling problem requires processing more tokens more efficiently . |
| Approach: | They propose a framework that renders long texts into compact visual pages and processes them with a vision-language model. |
| Outcome: | The proposed framework renders long texts into compact visual pages and processes them with a vision-language model. |
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models (2022.acl-long)
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| Challenge: | Recent few-shot learning models such as GPT3 are expensive and slow to deploy for real-world applications. |
| Approach: | They propose a prompt-based low-resource learning method for VL tasks with a few examples . they pre-train a sequence-to-sequence transformer model with prefix and masked language modeling . |
| Outcome: | The proposed method outperforms Frozen on vision-language tasks with prompt-based learning by 18.2% point. |
Training LLMs to be Better Text Embedders through Bidirectional Reconstruction (2025.emnlp-main)
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Chang Su, Dengliang Shi, Siyuan Huang, Jintao Du, Changhua Meng, Yu Cheng, Weiqiang Wang, Zhouhan Lin
| Challenge: | Existing text embedding approaches often leverage the embeddment of the final token, typically a reserved special token such as ‘[EOS]‘. |
| Approach: | They propose to add a new training stage before contrastive learning to enrich the semantics of the final token embedding. |
| Outcome: | The proposed training stage improves performance on the Massive Text Embedding Benchmark (MTEB), achieving new state-of-the-art results across different LLM base models and scales. |
Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning (2022.emnlp-main)
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| Challenge: | Existing research on building ES conversation systems only considered single-turn interactions with users, which is over-simplified and has limited support for multi-turn systems. |
| Approach: | They propose a multi-turn ES conversation system that uses lookahead heuristics to estimate future user feedback after using particular strategies. |
| Outcome: | The proposed system significantly outperforms baselines in both dialogue generation and strategy planning. |
Probing Audio-Visual Reasoning in Multimodal Language Models through the Lens of Audio (2026.acl-long)
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Kaixiong Gong, Kaituo Feng, Bohao Li, Yibing Wang, Mofan Cheng, Shijia Yang, Jiaming Han, Benyou Wang, Yutong Bai, Zhuoran Yang, Xiangyu Yue
| Challenge: | Recent multimodal large language models lack robust audio-visual integration ability and performance on DeafTest is highly correlated with AV-Odyssey accuracy. |
| Approach: | They propose a benchmarking tool that integrates audio-visual reasoning with audio-video cues to infer solutions. |
| Outcome: | The proposed model performs well on DeafTest, but lacks audio perception in simple audio tasks. |
M-LongDoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework (2025.emnlp-main)
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Yew Ken Chia, Liying Cheng, Hou Pong Chan, Maojia Song, Chaoqun Liu, Mahani Aljunied, Soujanya Poria, Lidong Bing
| Challenge: | Existing benchmarks for large multimodal models focus on short documents with less than 50 pages and are limited to extraction-based questions. |
| Approach: | They propose a retrieval-aware tuning approach to improve the accuracy of multimodal document reading by 4.6%. |
| Outcome: | The proposed framework improves the accuracy of model responses by 4.6% compared to existing benchmarks on documents with hundreds of pages and longer documents with more complex content. |
NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding (2024.findings-emnlp)
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Chunkit Chan, Cheng Jiayang, Yauwai Yim, Zheye Deng, Wei Fan, Haoran Li, Xin Liu, Hongming Zhang, Weiqi Wang, Yangqiu Song
| Challenge: | Theory of mind evaluations currently focus on testing models using machine-generated data or game settings prone to shortcuts and spurious correlations. |
| Approach: | They propose a benchmark to stress-test machine ToM in real-world negotiation surrounding covered multi-dimensional mental states. |
| Outcome: | The proposed benchmark builds upon the Belief-Desire-Intention theory and conducts the necessary empirical experiments to evaluate large language models. |
Just Adjust One Prompt: Enhancing In-Context Dialogue Scoring via Constructing the Optimal Subgraph of Demonstrations and Prompts (2023.emnlp-main)
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| Challenge: | Using large language models as chatbots can cause hallucinations and lack of empathy, authors report . a dimension-agnostic scoring method is proposed to improve the performance of chatbot performance . |
| Approach: | They propose a dimension-agnostic scoring method that leverages in-context learning . they propose to automatically generate prompts and then request the LLM multiple times . |
| Outcome: | The proposed method outperforms baselines on five datasets. |
Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction (2022.emnlp-main)
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| Challenge: | Existing RE models are incapable of handling implicit expressions and long-tail relation types due to language complexity and data sparsity. |
| Approach: | They propose a method to enhance relation extraction using k nearest neighbors (kNN-RE) kNN is a nearest-neighbor search tool that allows the model to consult training relations at test time . |
| Outcome: | The proposed model outperforms the best model to date on ACE05, SciERC, and Wiki80 datasets and outperformed the best on i2b2 and Wik80 dataset. |
MEIC-DT: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints (2026.findings-acl)
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Kangyang Luo, Shuzheng Si, Yuzhuo Bai, Cheng Gao, Zhitong Wang, Cheng Huang, Yingli Shen, Yufeng Han, Wenhao Li, Cunliang Kong, Maosong Sun
| Challenge: | Existing supervised neural methods are underexplored for coreference resolution, especially in incremental clustering. |
| Approach: | They propose a dual-threshold incremental clustering approach based on a lightweight Transformer. |
| Outcome: | Experiments on common benchmarks show that MEIC-DT achieves highly competitive coreference performance under stringent memory constraints. |
Language Lives in Sparse Dimensions: Toward Interpretable and Efficient Multilingual Control for Large Language Models (2026.eacl-long)
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| Challenge: | Prior studies show that large language models map multilingual content into English-aligned representations at intermediate layers before projecting them back into target-language token spaces in the later layers. |
| Approach: | They propose a method to identify and manipulate dimensions that are sparse and sparsity-based . they propose to use as few as 50 sentences of either parallel or monolingual data to manipulate these dimensions . |
| Outcome: | Experiments on a multilingual generation control task show the interpretability of these dimensions. |
PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization (2025.emnlp-main)
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Ruoxi Cheng, Yizhong Ding, Shuirong Cao, Ranjie Duan, Xiaoshuang Jia, Shaowei Yuan, Simeng Qin, Zhiqiang Wang, Xiaojun Jia
| Challenge: | Existing methods to jailbreak Large Vision Language Models do not consider interaction between images and text. |
| Approach: | They propose a prior-guided bimodal interactive black-box jailbreak attack for toxicity maximization that exploits the interaction of images and text. |
| Outcome: | The proposed method outperforms state-of-the-art jailbreak methods in black box scenarios and in closed-source LVLMs. |
Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are often evaluated on math word problems . however, such metrics conflate two distinct sub-skills: abstract formulation and arithmetic computation. |
| Approach: | They propose to use Final-answer-based metrics to evaluate large language models on math word problems to conflate two distinct sub-skills: abstract formulation and arithmetic computation. |
| Outcome: | The proposed model performance is bottlenecked by arithmetic computation and not abstract formulation, the study shows. |
Multi-Label Few-Shot Learning for Aspect Category Detection (2021.acl-long)
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| Challenge: | Existing few-shot learning methods focus on single-label predictions, which can not work well for ACD since a sentence may contain multiple aspect categories. |
| Approach: | They propose a few-shot learning method that uses the prototypical network to learn aspects from a set of aspects. |
| Outcome: | The proposed method significantly outperforms baseline methods on three datasets. |
Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task (2022.coling-1)
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Liang Wen, Juan Li, Houfeng Wang, Yingwei Luo, Xiaolin Wang, Xiaodong Zhang, Zhicong Cheng, Dawei Yin
| Challenge: | Existing answer selection approaches for community question answering lack additional answer summaries due to redundancy and lengthiness issues of crowdsourced answers. |
| Approach: | They constructed a dataset which contains a corresponding reference summary for each original lengthy answer. |
| Outcome: | The proposed model improves the performance of a question and candidate answer on a WikiHowQA dataset. |
Event Detection with Multi-Order Graph Convolution and Aggregated Attention (D19-1)
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| Challenge: | Existing methods for event detection use first-order syntactic relations to identify trigger words. |
| Approach: | They propose a dependency tree-based method to model and aggregate multi-order syntactic representations in sentences. |
| Outcome: | The proposed method outperforms existing methods on a benchmark dataset . it uses a dependency tree based graph convolution network with aggregative attention . |
7 Points to Tsinghua but 10 Points to ? Assessing Large Language Models in Agentic Multilingual National Bias (2025.findings-acl)
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| Challenge: | Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, but studies on risks associated with cross biases are limited to immediate context preferences. |
| Approach: | They investigate multilingual bias in state-of-the-art Large Language Models by analyzing their responses to decision-making tasks across multiple languages. |
| Outcome: | The proposed model can provide personalized advice across university applications, travel, and relocation scenarios. |
SMART: Self-Aware Agent for Tool Overuse Mitigation (2025.findings-acl)
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Cheng Qian, Emre Can Acikgoz, Hongru Wang, Xiusi Chen, Avirup Sil, Dilek Hakkani-Tür, Gokhan Tur, Heng Ji
| Challenge: | Current Large Language Models (LLMs) lack self-awareness to balance reasoning and tool use, increasing computational overhead. |
| Approach: | They propose a paradigm that enhances an agent’s self-awareness to optimize task handling and reduce tool overuse. |
| Outcome: | The proposed model reduces tool use by 24% while improving performance by over 37%. |
LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models (2025.findings-acl)
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Jiayi Gui, Yiming Liu, Jiale Cheng, Xiaotao Gu, Xiao Liu, Hongning Wang, Yuxiao Dong, Jie Tang, Minlie Huang
| Challenge: | Large Language Models (LLMs) have demonstrated notable capabilities across various tasks, showcasing complex problem-solving abilities. |
| Approach: | They propose a benchmark to evaluate the rule-based logical reasoning capabilities of Large Language Models (LLMs) they create simulated scenarios in which models execute or plan operations to achieve specific outcomes. |
| Outcome: | The proposed benchmark evaluates the performance of large language models on a variety of scenarios with varying difficulty levels. |
High-Order Semantic Alignment for Unsupervised Fine-Grained Image-Text Retrieval (2024.lrec-main)
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| Challenge: | Existing studies focus on learning global or local correspondence, but lack fine-grained local-global alignment. |
| Approach: | They propose a High Order Semantic Alignment (HOSA) model that can provide complementary and comprehensive semantic clues to infer correlation scores. |
| Outcome: | The proposed model outperforms state-of-the-art models in retrieving the most relevant results. |
Reasoning in Flux: Enhancing Large Language Models Reasoning through Uncertainty-aware Adaptive Guidance (2024.acl-long)
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Zhangyue Yin, Qiushi Sun, Qipeng Guo, Zhiyuan Zeng, Xiaonan Li, Junqi Dai, Qinyuan Cheng, Xuanjing Huang, Xipeng Qiu
| Challenge: | Extensive experiments across various reasoning tasks demonstrate that UAG not only enhances the reasoning abilities of LLMs but consistently outperforms several strong baselines with minimal computational overhead. |
| Approach: | They propose an approach to guide LLMs onto an accurate and reliable trajectory by identifying and adjusting uncertainty signals within each step of the reasoning chain. |
| Outcome: | The proposed approach outperforms strong baselines and outperformed strong models with minimal computational overhead. |
KnowCoder-X: Boosting Multilingual Information Extraction via Code (2025.findings-acl)
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Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu, Zixuan Li, Long Bai, Hanbin Wang, Yutao Zeng, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | Empirical evidence indicates that Large Language Models exhibit spontaneous cross-lingual alignment in Information Extraction (IE) however, a significant imbalance across languages persists, highlighting an underlying deficiency. |
| Approach: | They propose a code LLM with advanced cross-lingual and multilingual capabilities for universal IE that standardizes the representation of multilingual schemas using Python classes and conducts IE alignment instruction tuning on translated instance prediction task. |
| Outcome: | The proposed model surpasses ChatGPT and SoTA by 30.17% without training in 29 unseen languages and significantly improves cross-lingual IE transferability. |
CODEMENV: Benchmarking Large Language Models on Code Migration (2025.findings-acl)
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Keyuan Cheng, Xudong Shen, Yihao Yang, TengyueWang TengyueWang, Yang Cao, Muhammad Asif Ali, Hanbin Wang, Lijie Hu, Di Wang
| Challenge: | Large language models (LLMs) have demonstrated remarkable proficiency in handling a wide range of tasks within the software engineering domain, but their ability to perform code migration—adapting code to different environments—remains underexplored. |
| Approach: | They propose a benchmark to evaluate large language models’ performance in handling code migration tasks. |
| Outcome: | The proposed benchmark comprises 922 data points across 19 Python and Java packages and offers three tasks to systematically evaluate code migration: identifying version-incompatible functions, determining function changes, and adapting code to target environments. |
Qsnail: A Questionnaire Dataset for Sequential Question Generation (2024.lrec-main)
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| Challenge: | Questionnaires are a professional research methodology used for qualitative and quantitative analysis of human opinions, preferences, and behaviors. |
| Approach: | They propose a questionnaire-based dataset that consists of 13,168 human-written questionnaires. |
| Outcome: | The proposed dataset contains 13,168 human-written questionnaires gathered from online platforms. |
Adversarial Category Alignment Network for Cross-domain Sentiment Classification (N19-1)
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| Challenge: | Existing methods for cross-domain sentiment classification focus on aligning marginal distribution without taking category-specific decision boundaries into consideration. |
| Approach: | They propose an adversarial category alignment network to enhance category consistency . experimental results show the proposed method can achieve state-of-the-art performance . |
| Outcome: | The proposed method achieves state-of-the-art performance and produces more discriminative features on benchmark datasets. |
CodeTool: Enhancing Programmatic Tool Invocation of LLMs via Process Supervision (2025.acl-long)
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YifeiLu YifeiLu, Fanghua Ye, Jian Li, Qiang Gao, Cheng Liu, Haibo Luo, Nan Du, Xiaolong Li, Feiliang Ren
| Challenge: | Existing approaches to tool invocation are often unnecessarily long and require lengthy reasoning paths. |
| Approach: | They propose a framework for stepwise code generation that improves LLM tool invocation . they incorporate two distinct process rewards: the On-the-spot and the Latent Reward . |
| Outcome: | The proposed framework improves LLM tool invocation by leveraging the concise nature of code. |
On Diversified Preferences of Large Language Model Alignment (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) can be fine tuned with human feedback, but human preferences can be diversified due to annotators’ different tastes, which hinders the effectiveness of LLM alignment methods. |
| Approach: | They propose a calibration error metric to evaluate large language models (LLMs) and a multi-objective reward learning method to enhance the calibration performance of RMs on shared preferences. |
| Outcome: | The proposed model can be adopted as a key calibration error and MORE can achieve superior alignment performance. |
Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks (2023.findings-acl)
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| Challenge: | Recent studies focus on retrieval to solve knowledge-intensive tasks, but the potential of retrieval for non-knowledge-intensive (NKI) tasks remains under-explored. |
| Approach: | They propose a task-agnostic retrieval framework for NKI tasks that uses a static index and a prompt-guided reranker to re-rank the nearest evidence according to task-specific relevance. |
| Outcome: | The proposed framework outperforms state-of-the-art retrieval-augmented methods on NKI tasks and will be released for further research. |
Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability (2025.findings-emnlp)
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| Challenge: | Large Vision-Language Models have demonstrated remarkable capabilities in processing both visual and textual information. |
| Approach: | They examine the challenge of alignment and misalignment in LVLMs through an explainability lens. |
| Outcome: | The findings highlight the need for standardized evaluation protocols and in-depth explainability studies. |
MovieChats: Chat like Humans in a Closed Domain (2020.emnlp-main)
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| Challenge: | Currently, open-domain chatbots are far from satisfactory. |
| Approach: | They propose a unified, readily scalable neural approach which reconciles all subtasks like intent prediction and knowledge retrieval. |
| Outcome: | The proposed approach outperforms commercial systems replying on complex rules on static and interactive tests and shows that the results are remarkably good. |
Selective Temporal Knowledge Graph Reasoning (2024.lrec-main)
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| Challenge: | Existing models cannot abstain from uncertain predictions, which will bring risks in real-world applications. |
| Approach: | They propose to abstain from uncertain future facts by using a confidence estimator . they take both the certainty of the current prediction and the accuracy of historical predictions into account . |
| Outcome: | The proposed abstention mechanism helps existing models make selective predictions instead of indiscriminate ones. |
TT-SI: Self-Improving LLM Agents with Test-Time Training (2026.findings-acl)
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| Challenge: | Existing methods for language model fine-tuning are expensive and inefficient . existing methods rarely assess whether a training sample provides novel information . |
| Approach: | They propose a test-time self-improvement algorithm that generates a sample that model struggles with . they also explore Test-Time Distillation, which leverages 'stronger supervisors' |
| Outcome: | The proposed algorithm improves performance with +5.48% absolute accuracy gain on average across benchmarks. |
MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper (2025.emnlp-main)
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Runjia Zeng, Guangyan Sun, Qifan Wang, Tong Geng, Sohail Dianat, Xiaotian Han, Raghuveer Rao, Xueling Zhang, Cheng Han, Lifu Huang, Dongfang Liu
| Challenge: | Empirical evaluations show that Mixture of Expert Prompt Tuning outperforms state-of-the-art parameter efficient baselines on SuperGLUE. |
| Approach: | They propose a pretrain-then-fine-tune paradigm for manifold mapping using multiple prompt experts. |
| Outcome: | Empirical results show that the proposed approach outperforms state-of-the-art methods on SuperGLUE while reducing activated prompts by 79.25%. |
Spectral Gravity Formant Estimation for Phonetic Segmentation (2026.findings-acl)
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| Challenge: | a recent study suggests that end-to-end orthographic approaches miss the mark on time . linguistic applications which require high fidelity in the temporal domain, the loss of timing information is untenable . |
| Approach: | a new algorithm uses spectral gravity to estimate formants for enhanced phonetic segmentation . a deadline-bounded expectation maximization algorithm is proposed to estimate salient speech frequencies . |
| Outcome: | a new algorithm outperforms the state-of-the-art on key clustering metrics . the proposed algorithm generates reasonable alignments across multiple languages with no a priori training. |
Order-Agnostic Data Augmentation for Few-Shot Named Entity Recognition (2024.acl-long)
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| Challenge: | Existing DA methods for named entity recognition (NER) are costly and labor-intensive to acquire, necessitating innovative approaches to data scarcity. |
| Approach: | They propose an order-agnostic data augmentation solution that exploits the order-based property in the training phase of sequence-to-sequence NER methods for data augmented. |
| Outcome: | The proposed method significantly enhances the few-shot capabilities of pre-trained language models in low-resource settings. |
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning (2024.naacl-long)
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Ming Li, Yong Zhang, Zhitao Li, Jiuhai Chen, Lichang Chen, Ning Cheng, Jianzong Wang, Tianyi Zhou, Jing Xiao
| Challenge: | Large Language Models (LLMs) have revolutionized the landscape of artificial intelligence. |
| Approach: | They propose a self-guided method to identify and select cherry samples from open-source datasets, minimizing manual curation and potential cost for instruction tuning an LLM. |
| Outcome: | The proposed method enables LLMs to identify discrepancies between expected responses and intrinsic generation capability, and a marked uptick in model training efficiency. |
EasyEA: Large Language Model is All You Need in Entity Alignment Between Knowledge Graphs (2025.findings-acl)
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| Challenge: | Entity alignment (EA) aims to identify entities in different knowledge graphs (KGs) that represent the same real-world object. |
| Approach: | They propose an end-to-end EA framework based on large language models that requires no training to implement. |
| Outcome: | The proposed framework significantly reduces the reliance on seed entity pairs while achieving state-of-the-art (SOTA) performance on diverse datasets. |
ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation (P19-1)
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| Challenge: | Existing hierarchical recurrent encoder-decoder models treat all contexts indiscriminately, which may hurt the following response generation process. |
| Approach: | They propose a hierarchical recurrent encoder-decoder model that treats all contexts indiscriminately and uses a word level LSTM encoder to obtain the initial representation of each context. |
| Outcome: | The proposed model outperforms baseline models on Chinese customer services and English Ubuntu dialogue datasets in terms of both metric-based and human evaluations. |
Learning Sparsity for Effective and Efficient Music Performance Question Answering (2025.acl-short)
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| Challenge: | Existing Music AVQA methods rely on dense and unoptimized representations, leading to inefficiencies in the isolation of key information, reduction of redundancy, and prioritization of critical samples. |
| Approach: | They propose a sparse learning framework specifically designed for Music AVQA to address these challenges. |
| Outcome: | The proposed framework reduces training time by 28.32% while maintaining accuracy while maintaining state-of-the-art performance on the Music AVQA datasets. |
Can We Edit Multimodal Large Language Models? (2023.emnlp-main)
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| Challenge: | Existing methods to edit multimodal models have been used to incrementally infuse a language model with a new set of facts. |
| Approach: | They construct a benchmark for editing multimodal Large Language Models and establish metrics for evaluation. |
| Outcome: | The proposed benchmarks show that editing multimodal models is not as difficult as editing single-modal models. |
XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)
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Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li
| Challenge: | Document AI parsing semi-structured image form is a key information extraction task. |
| Approach: | They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework. |
| Outcome: | The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings. |
Gracefully Filtering Backdoor Samples for Generative Large Language Models without Retraining (2025.coling-main)
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| Challenge: | Existing backdoor defense methods are ineffective for generative large language models . generative LLMs output sequences of high-dimensional token logits instead of low-dimensional classification logits . |
| Approach: | They propose a method that leverages sample-wise gradients to identify backdoor samples without retraining LLMs. |
| Outcome: | The proposed method outperforms baselines significantly in identifying backdoor samples without retraining LLMs. |
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark (2022.findings-acl)
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Hao Sun, Guangxuan Xu, Jiawen Deng, Jiale Cheng, Chujie Zheng, Hao Zhou, Nanyun Peng, Xiaoyan Zhu, Minlie Huang
| Challenge: | Dialogue safety problems severely limit the real-world deployment of generative conversational models. |
| Approach: | They propose a taxonomy for dialogue safety specifically designed to capture unsafe behaviors in human-bot dialogue settings. |
| Outcome: | The proposed taxonomy captures unsafe behaviors in human-bot dialogue settings with rich context-sensitive unsafe examples. |
Divergent Thinking: Escape the Homogeneity Trap in Generative Commonsense Reasoning (2026.findings-acl)
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| Challenge: | Generative commonsense reasoning requires models to synthesize coherent narratives that satisfy lexical constraints and commonsensical logic. |
| Approach: | They propose a framework that allows for deep semantic diversity rather than surface-level lexical variation. |
| Outcome: | The proposed framework achieves over 10% improvement in overall accuracy on NoRa and SPICE score on CommonGen-Lite. |
Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation (2024.acl-long)
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| Challenge: | Experimental results show that the model can be used to generate dialogues in new domains quickly. |
| Approach: | They propose to use LLMs to generate dialogue data to reduce dialogue collection and annotation costs. |
| Outcome: | The proposed model performs better than the baseline model trained on real data. |
Deploying a Retrieval based Response Model for Task Oriented Dialogues (2022.emnlp-industry)
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| Challenge: | a task-oriented dialogue system needs high conversational capability and can be easily adaptable to changing situations. |
| Approach: | They propose a retrieval-based conversational model that can rank a large set of responses . they propose supervised training and fine-tuning on limited data collected through a human-in-the-loop platform . |
| Outcome: | The proposed model can scale to rank a large set of responses in real-world situations. |
Towards a Versatile Medical-Annotation Guideline Feasible Without Heavy Medical Knowledge: Starting From Critical Lung Diseases (2020.lrec-1)
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| Challenge: | Current annotation policies for medical corpora are not standardized across clinical texts of different types. |
| Approach: | They propose to annotate medical records of various types using a named entity recognition (NER) task. |
| Outcome: | The proposed annotation scheme is applicable to large-scale clinical NLP projects. |
Safe Inputs but Unsafe Output: Benchmarking Cross-modality Safety Alignment of Large Vision-Language Models (2025.findings-naacl)
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Siyin Wang, Xingsong Ye, Qinyuan Cheng, Junwen Duan, Shimin Li, Jinlan Fu, Xipeng Qiu, Xuanjing Huang
| Challenge: | Recent studies focus on single-modality threats, but this approach fails to address cross-modal safety alignment. |
| Approach: | They propose a safety alignment challenge to evaluate cross-modality safety alignment . they propose 'Safe Inputs but Unsafe Output' to consider safety of single modalities . |
| Outcome: | The proposed safety alignment challenge examines cases where modalities are safe independently but could lead to unsafe outputs when combined. |
Editing Large Language Models: Problems, Methods, and Opportunities (2023.emnlp-main)
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Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in model editing for LLMs have created challenges and opportunities for the community. |
| Approach: | They propose to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. |
| Outcome: | The proposed method alters behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. |
Exploring the Impact of Model Scaling on Parameter-Efficient Tuning (2023.emnlp-main)
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Yusheng Su, Chi-Min Chan, Jiali Cheng, Yujia Qin, Yankai Lin, Shengding Hu, Zonghan Yang, Ning Ding, Xingzhi Sun, Guotong Xie, Zhiyuan Liu, Maosong Sun
| Challenge: | Parameter-efficient tuning (PET) methods can drive large pre-trained language models by training only minimal parameters. |
| Approach: | They propose a parameter-efficient tuning method that is compatible with a tunable module and uses a random number generator to optimize fewer table parameters. |
| Outcome: | The proposed method is compatible with a tunable module and tested on 11 NLP tasks. |
World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning (2025.acl-long)
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| Challenge: | Existing approaches focus on action selection or use pre-trained models as world models to enhance planning capabilities. |
| Approach: | They propose a new learning framework that optimizes state prediction and action selection through preference learning. |
| Outcome: | The proposed method outperforms existing methods and GPT-4o on VoTa-Bench and Qwen2-VL (7B), LLaVA-1.6 (7B) and LLama-3.2 (11B). |
Dialogue Summarization with Static-Dynamic Structure Fusion Graph (2023.acl-long)
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| Challenge: | Dialogue summarization is a challenging task since it has dynamic interaction nature and inconsistent information flow among various speakers. |
| Approach: | They propose a Static-Dynamic graph-based Dialogue Summarization model which fuses prior knowledge from human expertise and adaptively learns the graph structure in an end-to-end learning fashion. |
| Outcome: | The proposed model can help people capture the highlights of a semi-structured and multi-participant dialogue without reviewing the complex dialogue context. |
Gestures Are Used Rationally: Information Theoretic Evidence from Neural Sequential Models (2022.coling-1)
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| Challenge: | Verbal communication is companied by rich non-verbal signals, but few studies have explored the non- verbal channels with finer theoretical lens. |
| Approach: | They extract gesture representations from monologue video data and train neural sequential models to examine their results. |
| Outcome: | The proposed method shows that speakers use simple gestures to convey information that enhances verbal communication. |
Towards Unified Spoken Language Understanding Decoding via Label-aware Compact Linguistics Representations (2023.findings-acl)
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| Challenge: | Existing methods for intent detection and slot filling decoders could result in misaligned predictions for both tasks. |
| Approach: | They propose a method that leverages label embeddings to jointly guide the decoding process. |
| Outcome: | The proposed method outperforms existing methods on two single- and multi-intent SLU benchmarks and can be incorporated into existing models. |
GR1: Reinforcement-Enhanced LLM for Geoscience Reasoning (2026.findings-acl)
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Yule Xie, Jiaxin Ding, Cheng Deng, Shiqing Gao, Junran Zhang, Sibo Zhang, Zeyuan Wang, Ke Wu, Xin Ding, Luoyi Fu, Meng Jin, Xinbing Wang
| Challenge: | Recent advances in large language models have demonstrated RL's substantial capacity to enhance multi-step reasoning beyond what supervised instruction tuning achieves. |
| Approach: | They propose a framework that converts multimodal questions into descriptive text . they propose RL-enhanced geoscience reasoning that can be fine-tuned to a text-only level . |
| Outcome: | The proposed framework improves accuracy and accuracy on multimodal questions while preserving answerability and difficulty. |
Adaptive Parameterization for Neural Dialogue Generation (D19-1)
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| Challenge: | Existing models of open-domain dialogue generate responses based on sequence-to-sequence paradigms. |
| Approach: | They propose an Adaptive Neural Dialogue generation model which manages various conversations with conversation-specific parameterization. |
| Outcome: | The proposed model performs better on a large-scale conversational dataset. |
OrchestraLLM: Efficient Orchestration of Language Models for Dialogue State Tracking (2024.naacl-long)
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| Challenge: | Large language models (LLMs) are computationally expensive and often require computational resources. |
| Approach: | They propose a routing framework that seamlessly integrates a SLM and an LLM, or-lm, or a LLM into a single framework. |
| Outcome: | The proposed routing framework reduces the computational costs by over 50% in dialogue state tracking tasks. |
VideoQA-TA: Temporal-Aware Multi-Modal Video Question Answering (2025.coling-main)
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| Challenge: | Existing methods for video question answering align visual or textual features directly with large language models, limiting the deep semantic association between modalities and hindering a comprehensive understanding of interactions within spatial and temporal contexts. |
| Approach: | They propose a temporal-aware framework for multi-modal video question answering that aligns videos and questions at fine-grained levels. |
| Outcome: | The proposed framework improves reasoning ability and accuracy of videoQA by aligning videos and questions at fine-grained levels. |
Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning (2025.acl-long)
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Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Qiushi Sun, Kanzhi Cheng, Junxian He, Jun Liu, Zhiyong Wu
| Challenge: | Existing methods for enhancing LLM reasoning rely on supervisory signals . current methods rely heavily on outcome supervision and auxiliary reward models . |
| Approach: | They propose a gen-eralizable and purely unsupervised self-training framework to enhance LLM reasoning without supervision. |
| Outcome: | The proposed framework improves LLM reasoning without supervision without external supervision. |
T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback (2025.acl-long)
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Zehan Wang, Ke Lei, Chen Zhu, Jiawei Huang, Sashuai Zhou, Luping Liu, Xize Cheng, Shengpeng Ji, Zhenhui Ye, Tao Jin, Zhou Zhao
| Challenge: | Text-to-audio (T2A) models still struggle to satisfy human preferences for prompt-following and acoustic quality when generating complex multi-event audio. |
| Approach: | They propose to use AI feedback learning to enhance basic capabilities of text-to-audio models . they use a large audio preference dataset to evaluate the model's capabilities . |
| Outcome: | The proposed model improves in simple and complex scenarios with AI feedback learning. |
M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning (2024.emnlp-main)
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Taowen Wang, Yiyang Liu, James Liang, Junhan Zhao, Yiming Cui, Yuning Mao, Shaoliang Nie, Jiahao Liu, Fuli Feng, Zenglin Xu, Cheng Han, Lifu Huang, Qifan Wang, Dongfang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) exhibit remarkable performance across a wide range of domains. |
| Approach: | They propose a multimodal prompt tuning approach for efficient instruction tuning of MLLMs. |
| Outcome: | The proposed approach shows superior performance on multimodal evaluation datasets compared to state-of-the-art methods. |
SLANG: New Concept Comprehension of Large Language Models (2024.emnlp-main)
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| Challenge: | Dynamic nature of language limits the adaptability of Large Language Models (LLMs) Traditionally, LLMs are trained on static data, which limits their adaptability . |
| Approach: | They propose a benchmark to integrate novel data and assess LLMs’ ability to comprehend emerging concepts, alongside a causal inference-based approach to enhance LLM comprehension of new phrases and their colloquial context. |
| Outcome: | The proposed model outperforms baseline models in terms of precision and relevance in the comprehension of Internet slang and memes. |
MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization (2026.findings-acl)
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| Challenge: | Existing memory systems can support long-horizon human-LLM interactions by persisting historical interactions beyond limited context windows. |
| Approach: | They propose a framework that augments memory systems with a self-evolving meta-memory . meta-meso is iteratively distilling transferable knowledge utilization experiences . results show MetaMem outperforms strong baselines by over 3.6% . |
| Outcome: | The proposed framework outperforms baselines by over 3.6% in the long-horizon human-LLM interaction. |
Joint Learning for Emotion Classification and Emotion Cause Detection (D18-1)
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| Challenge: | Using a unified framework, we propose a joint approach for emotion classification and emotion cause detection. |
| Approach: | They propose a neural network-based joint approach for emotion classification and emotion cause detection which captures mutual benefits across the two sub-tasks. |
| Outcome: | The proposed approach can capture mutual benefits across two sub-tasks on Chinese microblogs. |
Feature Structure Matching for Multi-source Sentiment Analysis with Efficient Adaptive Tuning (2024.lrec-main)
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| Challenge: | Existing domain matching methods tend to pull all feature instances close, but they are expensive and expensive to update. |
| Approach: | They propose to extract multi-layer features from a large pre-trained model and propose a dynamic parameter fusion module to exploit them for efficient and adaptive tuning. |
| Outcome: | The proposed framework is more robust and generalizable in the multi-source scenario. |
SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models (2025.acl-long)
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Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi
| Challenge: | SIQ quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Approach: | They propose a human cognition-inspired evaluation pipeline for voice understanding large language models (LLM_Voice) that quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Outcome: | The proposed framework quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models, identifies annotation errors in existing benchmarks, and detects hallucinations in LLM_Voice. |
S2LPP: Small-to-Large Prompt Prediction across LLMs (2025.findings-emnlp)
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| Challenge: | a small model can be used to select effective prompt templates for a larger model. |
| Approach: | They propose a method to use a smaller model to select effective prompt templates for a larger model. |
| Outcome: | The proposed method significantly reduces the cost of prompt engineering while matching performance with optimal prompts among candidates. |
Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models (2023.acl-long)
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| Challenge: | Existing methods to measure stereotypes in large language models rely on manual templates or natural sentences that contain stereotypes. |
| Approach: | They propose a prompt-based method to measure stereotypes in large language models . they use natural language descriptions of the target demographic group alongside unmarked defaults . |
| Outcome: | The proposed method detects that portrayals contain higher rates of racial stereotypes than human-written portrayals. |
PAL: Persona-Augmented Emotional Support Conversation Generation (2023.findings-acl)
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| Challenge: | Recent work has demonstrated the effectiveness of dialogue models in providing emotional support due to the lack of human resources for mental health support. |
| Approach: | They propose a framework for dynamically inferring and modeling seekers’ persona from the conversation history and a model that leverages persona information to provide personalized emotional support. |
| Outcome: | The proposed model outperforms baseline models on the studied benchmark. |
What are the Generator Preferences for End-to-end Task-Oriented Dialog System? (2024.emnlp-main)
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| Challenge: | Existing methods to improve the accuracy of entity retrieval are not effective. |
| Approach: | They propose a framework that improves the performance of task-oriented dialogue systems by obtaining fine-grained matching information between contexts and entities and extracting the entity attribute shift matrix as preference signals. |
| Outcome: | The proposed framework outperforms existing methods and improves the quality of the dialogue. |
SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation (2026.findings-acl)
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Shuang Cheng, Yihan Bian, Dawei Liu, Yuhua Jiang, Yihao Liu, Linfeng Zhang, Qian Yao, Zhongbo Tian, Wenhai Wang, Qipeng Guo, Kai Chen, Biqing Qi, Bowen Zhou
| Challenge: | Autoregressive (AR) language models are a dominant paradigm in the field of parallelism and non-causal modeling. |
| Approach: | They propose a blockwise discrete diffusion model that preserves AR-compatible serving while enabling parallel intra-block generation. |
| Outcome: | The proposed model achieves theoretical speedups over 5 and wall-clock speedup of 2.3 on H200 GPUs in latency-critical regimes. |
StableToolBench-MirrorAPI: Modeling Tool Environments as Mirrors of 7,000+ Real-World APIs (2025.findings-acl)
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| Challenge: | Existing tool environments face challenges in balancing stability, scale, and realism, especially for benchmarking purposes. |
| Approach: | They propose a framework that trains specialized LLMs to accurately simulate real API responses by supervised fine-tuning and chain-of-thought reasoning. |
| Outcome: | The proposed framework achieves superior accuracy and stability compared to state-of-the-art methods on the newly constructed MirrorAPI-Bench and its integration into StableToolBench. |
Prompt Tuning with Contradictory Intentions for Sarcasm Recognition (2023.eacl-main)
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| Challenge: | Recent advances have shown that Pre-trained Language Models (PLMs) can achieve promising performance in many downstream Natural Language Processing (NLP) tasks. |
| Approach: | They propose to incorporate prior knowledge about contradictory intentions into prompt tuning for sarcasm recognition by mimicking the actual intention by verbalizer engineering. |
| Outcome: | The proposed model mimics the actual intention by prompt construction and indicates whether the actual intent contradicts the literal content by verbalizer engineering. |
SSA: Semantic Contamination of LLM-Driven Fake News Detection (2025.emnlp-main)
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| Challenge: | Evaluating 45 variants of nine LLMs, we find LIAR2 accuracy climbs monotonically with injected contamination, while the SSA Factor escalates in near-perfect lock-step. |
| Approach: | They propose a framework that detects BDC risks across semantic to label level via entity shift perturbation and an interpretable metric, the SSA Factor. |
| Outcome: | The proposed framework detects BDC risks across semantic to label level via entity shift perturbation and interpretable metric, the SSA Factor. |
Black-Box Prompt Optimization: Aligning Large Language Models without Model Training (2024.acl-long)
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| Challenge: | Large language models are often not well aligned with human intents, which requires additional training. |
| Approach: | They propose to use Black-Box Prompt Optimization (BPO) to perform alignments on large language models that are not well aligned with human intents. |
| Outcome: | The proposed model outperforms existing models and is model-agnostic. |
RIPRAG: Hack a Black-box Retrieval-Augmented Generation Question-Answering System with Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing methods to generate RAG documents require knowledge of the target RAG system’s internal composition and implementation details, whereas black-box methods are unable to utilize interactive information. |
| Approach: | They propose a RIPRAG attack framework that treats the target RAG system as a black box and leverages a Reinforcement Learning from Black-box Feedback (RLBF) method to optimize the generation model for poisoned documents. |
| Outcome: | The proposed method achieves an attack success rate (ASR) improvement of up to 0.72 compared to baseline methods. |
Intent-Driven Semantic ID Generation for Grounded Conversational News Recommendation (2026.acl-industry)
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| Challenge: | a new approach to news recommendation grounds each suggestion in a rapidly evolving article corpus while addressing implicit user intents that lack explicit retrievable keywords. |
| Approach: | They propose an intent-driven Semantic ID generation paradigm to address these challenges . they map diverse intents to hierarchical SID prefixes and then fuzzy-match them to current news pool . |
| Outcome: | The proposed model achieves 0% hallucination and 12.4% L1 match on a mainstream Chinese news platform. |
Knowledge-Rich Self-Supervision for Biomedical Entity Linking (2022.findings-emnlp)
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Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon
| Challenge: | Entity linking is challenging in high-value domains with myriad entities . standard classification approaches suffer from the annotation bottleneck . |
| Approach: | They propose a self-supervised approach to learn domain knowledge for biomedical entity linking . it generates self-reported mention examples on unlabeled text and trains contextual encoder . |
| Outcome: | The proposed method outperforms existing methods by 20 points in accuracy on biomedical datasets. |
Improving Contrastive Learning of Sentence Embeddings from AI Feedback (2023.findings-acl)
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| Challenge: | Existing methods to learn sentence embeddings with rich semantics are limited due to the discrete nature of natural language. |
| Approach: | They propose to use AI feedback to improve contrastive learning of sentence embeddings by combining human feedback and AI feedback. |
| Outcome: | The proposed method achieves state-of-the-art performance on several semantic textual similarity and transfer learning tasks compared to other unsupervised and supervised contrastive learning methods. |
Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have made significant strides in complex reasoning tasks, but their reasoning is often constrained by their intrinsic understanding, lacking external insights. |
| Approach: | They propose a framework that enables cross-model communication during problem-solving. |
| Outcome: | The proposed framework surpasses established baselines in complex reasoning tasks and is cost-effective. |
LogiDynamics: Unraveling the Dynamics of Inductive, Abductive and Deductive Logical Inferences in LLM Reasoning (2025.emnlp-main)
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Tianshi Zheng, Cheng Jiayang, Chunyang Li, Haochen Shi, Zihao Wang, Jiaxin Bai, Yangqiu Song, Ginny Wong, Simon See
| Challenge: | Modern large language models (LLMs) employ diverse logical inference mechanisms for reasoning. |
| Approach: | They analyze the comparative dynamics of inductive (System 1) versus abductive/deductive (system 2) inference in large language models by using a controlled analogical reasoning environment and a MCQ/free-text task format. |
| Outcome: | The proposed methods can significantly scale LLM reasoning. |
DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations (2025.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) have been used in real-world industrial scenarios for various natural language processing tasks, but their high inference cost makes their deployment impractical, necessitating the use of smaller models. |
| Approach: | They propose a continual pre-training technique that generates diverse task instructions and responses via reading comprehension on conversation transcripts, enabling better instruction generalization. |
| Outcome: | The proposed technique improves small LLMs’ domain adaptability for business conversational tasks, compared with traditional methods that rely on next-token prediction. |
Pre-training via Leveraging Assisting Languages for Neural Machine Translation (2020.acl-srw)
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| Challenge: | Sequence-to-sequence (S2S) pre-training with large monolingual data is not always available for the languages of interest (LOI). |
| Approach: | They propose to use monolingual corpora of other languages to complement the scarce monolingual LOI by script mapping (Chinese to Japanese) . Using only Chinese and French monolinguals, they improve Japanese-English translation quality by up to 8.5 BLEU in low-resource scenarios. |
| Outcome: | The proposed approach improves Japanese-English translation quality by up to 8.5 BLEU in low-resource scenarios. |
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)
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Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu
| Challenge: | Recent advances in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm . traditional methods of assessment and evaluation fail in dynamic and open-ended scenarios . |
| Approach: | They propose a paradigm where LLMs are leveraged to perform scoring, ranking, or selection for machine learning evaluation scenarios. |
| Outcome: | The proposed model-based judgment and evaluation paradigms are based on large language models and are compared to the current model-driven evaluation paradigm. |
DR-HM: Distill-then-Reinforce Training with Cognition-Aware Data Synthesis for Harmful Meme Detection (2026.findings-acl)
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Zihan Cheng, Jianxiang Ma, Xiaocui Yang, Peidong Wang, Wen Zhang, Shi Feng, Daling Wang, Yifei Zhang, Mingfu Zhang
| Challenge: | Current methods for harmful meme detection lack the knowledge required to identify such hate . current methods lack the ability to identify cultural stereotypes and visual metaphors . |
| Approach: | They propose a framework that decomposes meme analysis into a human-inspired reasoning process . they propose DR-HM to transfer knowledge from closed-source models while mitigating biases . |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |
Explicit Inductive Inference using Large Language Models (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) suffer a signifi- cant performance drop when entailment labels disagree with the attestation label of hypothesis H. |
| Approach: | They propose a pipeline that exploits an LLM's attestation bias to do explicit inductive inference . they transform a premise into attested alternatives and aggregate the results . |
| Outcome: | The proposed pipeline improves the performance of large language models on inference tasks and alleviates the attestation bias. |
Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings (2020.coling-main)
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| Challenge: | Existing methods for event coreference resolution do not identify paraphrase relations between events. |
| Approach: | They propose a new event-specific paraphrase and argument-aware semantic Embedding model for event coreference resolution based on event-related paraphrases and argument embeddings . EPASE recognizes deep paraphrase relations in an event- specific context of sentences and can cover event paraphrase of more situations . |
| Outcome: | Experiments on within- and cross-document event coreference show it is superior compared to existing methods. |
OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation (2025.acl-long)
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Qinglin Zhang, Luyao Cheng, Chong Deng, Qian Chen, Wen Wang, Siqi Zheng, Jiaqing Liu, Hai Yu, Chao-Hong Tan, Zhihao Du, ShiLiang Zhang
| Challenge: | Full-duplex spoken dialogue systems allow simultaneous bidirectional communication . low latency and natural interactions in full-duplice systems remains a challenge . |
| Approach: | They propose a multi-stage post-training scheme that adapts a text large language model into a speech-text dialogue LLM. |
| Outcome: | The proposed model can model human conversation behaviors with low latency and natural interactions with low delay. |
DEBAR: Mitigating Contextual Bias in Cross-Document Relation Extraction via Dual-Stream Decoupling (2026.acl-long)
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| Challenge: | Existing methods focus on sentence-level or singledocument settings, resulting in one-sided relation transfer contextual bias and incomplete reasoning chains. |
| Approach: | They propose a framework to explicitly decouple and preserve bidirectional bridge evidence and a dynamic loss optimization objective to separate head and tail contexts. |
| Outcome: | The proposed framework decouples and preserves bidirectional bridge evidence while capturing global dependencies through iterative message passing. |
TripleFact: Defending Data Contamination in the Evaluation of LLM-driven Fake News Detection (2025.acl-long)
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| Challenge: | Existing evaluation paradigms for fake news detection are based on static datasets and closed-world assumptions that are inadvertently memorized during pre-training. |
| Approach: | They propose a framework to mitigate BDC risk while prioritizing real-world applicability by integrating three components to assess robustness against human-crafted misinformation. |
| Outcome: | The proposed framework mitigates BDC risk while prioritizing real-world applicability. |
Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection (2026.acl-long)
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| Challenge: | a rapid proliferation of large language models (LLMs) generate text that increasingly resembles human writing . this makes it difficult to capture subtle cues that distinguish AI-generated content from human-written content . |
| Approach: | They propose a framework that disentangles AI-detection semantics from generator-aware artifacts by latent encoding and perturbation-based regularization. |
| Outcome: | The proposed framework disentangles AI-detection semantics from generator-aware artifacts on 20 representative LLMs across 7 categories. |
Trustworthy and Explainable Causal Representation Learning in Transformers (2026.findings-acl)
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| Challenge: | Existing approaches to interpretable representation learning rely on masks that weight the significance of input features, but the origin of these masks is uncertain. |
| Approach: | They propose a causal framework that directly learns identifiable representations from attention weights rather than relying on importance masks. |
| Outcome: | The proposed framework learns identifiable and explainable representations from attention weights, rather than masks, and guarantees faithfulness on real-world datasets. |
Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER (2022.coling-1)
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| Challenge: | Existing methods to extract entities from visually-rich documents ignore the inherent multimodality of VRDs and thus the suboptimal results are achieved. |
| Approach: | They propose a multimodal semantic enhancement method that filters redundant information in the current document and a cross-document information awareness technique to enrich the entity-related context. |
| Outcome: | The proposed method outperforms existing methods on two documents understanding benchmarks covering eight languages. |
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. |
SentBS: Sentence-level Beam Search for Controllable Summarization (2022.emnlp-main)
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| Challenge: | Structure-controlled summarization is a useful and interesting research direction . current structure-controlling methods have limited effectiveness in enforcing the desired structure. |
| Approach: | They propose a sentence-level beam search generation method to select suitable sentences for subsequent generations. |
| Outcome: | The proposed method significantly reduces structural discrepancies by 68% on a structure-controlled dataset. |
CafGa: Customizing Feature Attributions to Explain Language Models (2025.emnlp-demos)
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| Challenge: | Feature attribution methods, such as SHAP and LIME, quantify the influence of each input component in a model. |
| Approach: | They propose a tool for generating and evaluating feature attribution explanations at customizable granularities. |
| Outcome: | The proposed tool is compared with two baseline methods: PartitionSHAP and MExGen. |
ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues? (2025.emnlp-industry)
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| Challenge: | ECom-Bench is a benchmark framework for evaluating LLM agent with multimodal capabilities in e-commerce customer support domain. |
| Approach: | They introduce a benchmark framework for evaluating LLM agent with multimodal capabilities in the e-commerce customer support domain. |
| Outcome: | The proposed benchmark features dynamic user simulation based on persona information from real e-commerce customer interactions and a realistic task dataset derived from authentic ecommerce dialogues. |
KELE: A Multi-Agent Framework for Structured Socratic Teaching with Large Language Models (2025.findings-emnlp)
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| Challenge: | Socratic teaching places high demands on teachers’ expertise and real-time feedback capabilities, making it difficult to scale in large educational settings. |
| Approach: | They propose a multi-agent framework for structured Socratic teaching with LLMs that integrates a structured SocRule and a consultant-teacher collaborative teaching mechanism. |
| Outcome: | The proposed framework outperforms existing LLMs in natural language generation and dialogue comprehension in the classroom. |
Causal Tree Extraction from Medical Case Reports: A Novel Task for Experts-like Text Comprehension (2025.emnlp-main)
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| Challenge: | Existing methods to extract causal relationships from medical case reports are insufficient for capturing causal relationships of an entire case. |
| Approach: | They propose a task that generates a causal tree with the primary disease as the root and extracts causal relationships from a medical case report. |
| Outcome: | The proposed method outperforms the baseline method by 20.2 points in the human evaluation and introduces evaluation metrics that reflect clinician preferences. |
Are Embedded Potatoes Still Vegetables? On the Limitations of WordNet Embeddings for Lexical Semantics (2023.emnlp-main)
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| Challenge: | Knowledge Base Embedding (KBE) models are widely used to encode structured information from knowledge bases, including WordNet, but the evaluation task is often focused on link prediction, ignoring their semantic capabilities. |
| Approach: | They propose to evaluate the performance of Knowledge Base Embedding (KBE) models of WordNet on link prediction and their ability to encode semantic information. |
| Outcome: | The proposed model performs poorly on two semantic tasks and two downstream tasks. |
TrajGuard: Streaming Hidden-state Trajectory Detection for Decoding-time Jailbreak Defense (2026.findings-acl)
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| Challenge: | Existing jailbreak defense paradigms rely on static detection of prompts, outputs, or internal states . hidden states in critical layers during decoding carry stronger and more stable risk signals . |
| Approach: | They propose a decoding-time defense framework that aggregates hidden-state trajectories via a sliding window to quantify risk in real time. |
| Outcome: | The proposed framework achieves an average defense rate of 95% in 12 jailbreak attacks and open-source LLMs. |
Few-shot Link Prediction on Hyper-relational Facts (2024.lrec-main)
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| Challenge: | Existing methods to predict missing elements in hyper-relational facts require high-quality data. |
| Approach: | They propose a task to predict a missing entity in a hyper-relational fact with limited support instances. |
| Outcome: | The proposed model outperforms existing models on three datasets. |
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Gradient-based explanation methods are increasingly used to interpret neural models in natural language processing (NLP) however, in the context of Aspect-based Sentiment Analysis, only specific dimensions are pertinent. |
| Approach: | They propose a Gradient-based explanation framework that leverages an information bottleneck to refine word embeddings into a concise intrinsic dimension, maintaining essential features and omitting unrelated information. |
| Outcome: | The proposed framework improves both the models’ performance and explanations’ clarity by identifying sentiment-aware features. |
Dynamic and Generalizable Process Reward Modeling (2025.acl-long)
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| Challenge: | Existing Process Reward Models lack cross-domain generalization and focus on feedback results. |
| Approach: | They propose a process reward model that uses a reward tree to capture and store fine-grained, multi-dimensional reward criteria. |
| Outcome: | The proposed model performs on prevailing benchmarks and out-of-distribution scenarios. |
Current Agents Fail to Leverage World Model as Tool for Foresight (2026.acl-long)
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Cheng Qian, Emre Can Acikgoz, Bingxuan Li, Xiusi Chen, Yuji Zhang, Bingxiang He, Qinyu Luo, Gokhan Tur, Dilek Hakkani-Tür, Yunzhu Li, Heng Ji
| Challenge: | Generative world models could be used to enhance agents' cognition . agents are expected to operate in settings where tasks unfold over long horizons and involve intricate chains of interdependent decisions. |
| Approach: | They propose to use vision-language models as external simulators to enhance cognition . they find that agents rarely invoke simulation and misuse predicted rollouts . |
| Outcome: | The proposed model could be used to predict future states rather than short-horizon reasoning . the model could also be used for real-world planning and robotics . |
Every Answer Matters: Evaluating Commonsense with Probabilistic Measures (2024.acl-long)
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Qi Cheng, Michael Boratko, Pranay Kumar Yelugam, Tim O’Gorman, Nalini Singh, Andrew McCallum, Xiang Li
| Challenge: | Existing commonsense evaluations are often posed as multiple-choice questions, allowing models to exploit systematic biases. |
| Approach: | They propose a generative task that evaluates common sense via multiple open-ended generations and a method that strongly correlates with human judgments. |
| Outcome: | The proposed method outperforms strong language model baselines on a dataset of human and machine common sense. |
MWPO: Enhancing LLMs Performance through Multi-Weight Preference Strength and Length Optimization (2025.findings-acl)
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| Challenge: | Existing offline alternatives to Reinforcement Learning from Human Feedback (RLHF) are available at https://github.com/AIR-hl/MWPO. |
| Approach: | They propose an offline method to optimize preference pairs based on implicit reward margins and response length margins by reweighting them using a geometric mixture. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four different scales and reduces generation length by 9.4%. |
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space (2024.lrec-main)
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| Challenge: | Existing studies have tried to introduce discrete or Gaussian-based latent variables to address the one-to-many problem, but the diversity is limited. |
| Approach: | They propose a diffusion model to enhance the diversity of dialogue generation by using continuous latent variables instead of discrete ones. |
| Outcome: | The proposed model greatly enhances diversity of dialog response while keeping the coherence. |
An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs (2026.findings-acl)
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| Challenge: | Relevance emphasizes the aboutness of a result to a query, while utility refers to the result’s usefulness or value to an information seeker. |
| Approach: | They propose an Iterative utiliTy judgmEnt fraMework to promote each step in Retrieval-Augmented Generation (RAG) they propose to use relevance ranking, utility judgments, and answer generation to prioritize high-utility results over low-utilitity results. |
| Outcome: | The proposed framework improves relevance, ranking, and answer generation on retrieval (TREC DL, WebAP), utility judgment task (GTI-NQ), and factoid question-answering (NQ) datasets. |
MoE-I2: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition (2024.findings-emnlp)
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Cheng Yang, Yang Sui, Jinqi Xiao, Lingyi Huang, Yu Gong, Yuanlin Duan, Wenqi Jia, Miao Yin, Yu Cheng, Bo Yuan
| Challenge: | emergence of Mixture of Experts (MoE) LLMs has significantly advanced the development of language models. |
| Approach: | They propose a two-stage compression method tailored for Mixture of Experts to reduce the model size and decrease the computational cost. |
| Outcome: | The proposed method reduces model size and improves inference efficiency while maintaining performance in various zero-shot tasks. |
Pruning as a Domain-specific LLM Extractor (2024.findings-naacl)
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Nan Zhang, Yanchi Liu, Xujiang Zhao, Wei Cheng, Runxue Bao, Rui Zhang, Prasenjit Mitra, Haifeng Chen
| Challenge: | Large Language Models (LLMs) have exhibited remarkable proficiency across a wide array of NLP tasks. |
| Approach: | They propose a method for pruning large language models using general or task-specific weights to extract a compressed, task-agnostic LLM. |
| Outcome: | The proposed method extracts a compressed, domain-specific, and task- agnostic LLM by identifying LLM weights that are pivotal for general capabilities, like linguistic capability and multi-task solving, and domain- specific knowledge. |
Controlling Styles in Neural Machine Translation with Activation Prompt (2023.findings-acl)
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| Challenge: | Earlier studies on controlling styles in neural machine translation (NMT) have focused on regulating the level of formality, but they still encounter two major challenges. |
| Approach: | They propose a method to control the style of neural machine translation by retrieving prompts from stylized monolingual corpus. |
| Outcome: | The proposed method can control the style of translation and achieve remarkable performance. |
A Generalizable Rhetorical Strategy Annotation Model Using LLM-based Debate Simulation and Labelling (2025.findings-emnlp)
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Shiyu Ji, Farnoosh Hashemi, Joice Chen, Juanwen Pan, Weicheng Ma, Hefan Zhang, Sophia Pan, Ming Cheng, Shubham Mohole, Saeed Hassanpour, Soroush Vosoughi, Michael Macy
| Challenge: | Rhetorical strategies are important to persuasive communication, but their analysis relies on human annotation, which is costly, inconsistent and difficult to scale. |
| Approach: | They propose a framework that leverages large language models to generate and label debate data . they fine-tune transformer-based classifiers on this dataset and validate it against human data a . |
| Outcome: | The proposed model achieves high performance and strong generalization across topical domains. |
Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse (2026.acl-long)
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Chi Zhang, Mengqi Zhang, Xiaotian Ye, Runxi Cheng, Zisheng Zhou, Ying Zhou, Pengjie Ren, Zhumin Chen
| Challenge: | Existing approaches to reducing the effects of knowledge editing are insufficiently understood. |
| Approach: | They propose a plug-and-play framework that preserves the dominant subspace of the original weights and analyzes parameter updates in the spectral basis of the weights. |
| Outcome: | The proposed framework improves editing efficacy while preserving general abilities under long-horizon sequential editing, including extreme settings with up to 20,000 edits. |
Diversifying Dialogue Generation with Non-Conversational Text (2020.acl-main)
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| Challenge: | Neural network-based sequence-to-sequence models suffer from low diversity in open-domain dialogue generation. |
| Approach: | They propose a way to diversify dialogue generation by leveraging non-conversational text . they collect large-scale corpus from forum comments, idioms and book snippets . |
| Outcome: | The proposed model produces significantly more diverse responses without sacrificing relevance with context. |
AgentGC: Evolutionary Learning-based Lossless Compression for Genomics Data with LLM-driven Multiple Agent (2026.findings-acl)
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Sun Hui, Ding Yanfeng, Huidong Ma, Chang Xu, Keyan Jin, Lizheng Zu, Cheng Zhong, Xiaoguang Liu, Gang Wang, Wentong Cai
| Challenge: | Lossless compression has made significant advancements in Genomics Data storage, sharing and management. |
| Approach: | They propose a novel agent-based GD Compressor with 3 layers with a multi-agent named Leader and Worker. |
| Outcome: | The proposed method improves on existing methods with low-level modeling and limited adaptability and user-unfriendly interface. |
Semantic Role Labeling with Associated Memory Network (N19-1)
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| Challenge: | Existing work on semantic role labeling has been focused on using deep learning methods to solve the task. |
| Approach: | They propose a syntax-agnostic SRL model enhanced by the proposed associated memory network which makes use of inter-sentence attention of label-known associated sentences as a kind of memory to further enhance dependency-based SRL. |
| Outcome: | The proposed model achieves state-of-the-art on CoNLL-2009 benchmark datasets showing that it is not dependent on external resources. |
Geometric Signatures of Compositionality Across a Language Model’s Lifetime (2025.acl-long)
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| Challenge: | linguistic compositionality allows atoms to locally combine to create global meaning . a rich array of meanings at the level of a phrase may be explained by simple rules of composition. |
| Approach: | They propose to relate the degree of compositionality in a dataset to the intrinsic dimension of its representations under an LM, a measure of feature complexity. |
| Outcome: | The proposed model is based on a geometric view of the compositionality of a dataset and the intrinsic dimension of its representations under an LM. |
Automated Generation of Accurate & Fluent Medical X-ray Reports (2021.emnlp-main)
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| Challenge: | Existing medical report generation efforts focus on producing human-readable reports, yet the generated text may not be well aligned to the clinical facts. |
| Approach: | They propose to automate the generation of medical reports from chest X-ray image inputs . medical reports are the primary medium, which physicians communicate findings from scans - authors say . |
| Outcome: | The proposed method achieves fluency and clinical accuracy on common metrics. |
Improving Event Duration Question Answering by Leveraging Existing Temporal Information Extraction Data (2022.lrec-1)
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| Challenge: | Existing tasks that require knowledge about event duration require limited duration data . a two-stage fine-tuning approach might fail due to discrepancy between task and duration data. |
| Approach: | They propose to recast existing event duration classification task to a question answering task similar to McTACO. |
| Outcome: | The proposed model achieves a 13% Exact Match score improvement over baseline on the McTACO duration question answering task. |
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning (D19-1)
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| Challenge: | Existing knowledge graph reasoning methods require numerous trials for path-finding and require meticulous reward engineering to fit specific datasets. |
| Approach: | They propose a plug-and-play framework that uses generative adversarial imitation learning to enhance existing RL-based methods. |
| Outcome: | The proposed framework improves existing RL-based methods while eliminating reward engineering. |
Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem (2022.findings-emnlp)
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| Challenge: | Existing methods for generating complex semantics and diverse equations are limited by a fixed view. |
| Approach: | They propose a multi-view consistent contrastive learning approach that decouples human reasoning into two independent but consistent views. |
| Outcome: | The proposed approach significantly outperforms existing baselines on complex problems on multiple languages. |
Can Graph Descriptive Order Affect Solving Graph Problems with LLMs? (2025.acl-long)
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| Challenge: | Large language models (LLMs) have achieved significant success in reasoning tasks, including mathematical reasoning and logical deduction. |
| Approach: | They conduct the first comprehensive analysis of how the order of graph descriptions impacts LLM performance. |
| Outcome: | The results show that graph descriptions significantly improve LLMs’ comprehension of graph structures, and the robustness of LLM models to graph description order varies across different tasks. |
Fingerprinting LLMs via Prompt Injection (2026.acl-long)
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Yuepeng Hu, Zhengyuan Jiang, Mengyuan Li, Osama Ahmed, Zhicong Huang, Cheng Hong, Neil Zhenqiang Gong
| Challenge: | Existing provenance detection methods for large language models are infeasible for already published models and compare outputs using hand-crafted or random prompts. |
| Approach: | They propose a detection framework that constructs fingerprints by exploiting LLMs’ inherent vulnerability to prompt injection. |
| Outcome: | The proposed framework achieves high true positive rates while keeping false positive rates near zero. |
Improving the Robustness of Large Language Models via Consistency Alignment (2024.lrec-main)
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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Shuaiqiang Wang, Chong Meng, Zhicong Cheng, Zhaochun Ren, Dawei Yin
| Challenge: | Large language models have shown tremendous success in following user instructions and generating helpful responses, but their robustness is still far from optimal. |
| Approach: | They propose a two-stage training framework that helps a model generalize on following instructions via similar instruction augmentations. |
| Outcome: | The proposed training framework improves diversity and aligns the model with human expectations by differentiating subtle differences in similar responses. |
On the Robustness of Self-Attentive Models (P19-1)
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| Challenge: | Experimental results show that self-attentive neural models are more robust against adversarial perturbations compared to recurrent neural networks. |
| Approach: | They propose an adversarial attack algorithm that generates more natural adversarials . they propose to use the attention mechanism to learn a context-dependent representation . |
| Outcome: | The proposed attack algorithm generates more natural adversarial examples that could mislead models but not humans. |
Nested Event Extraction upon Pivot Element Recognition (2024.lrec-main)
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Weicheng Ren, Zixuan Li, Xiaolong Jin, Long Bai, Miao Su, Yantao Liu, Saiping Guan, Jiafeng Guo, Xueqi Cheng
| Challenge: | Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. |
| Approach: | They propose a new model that extracts nested events mainly based on recognizing PEs. |
| Outcome: | The proposed model can extract nested events based on recognizing PEs . it incorporates information from both event types and argument roles to improve performance . |
Reasoning Robustness of LLMs to Adversarial Typographical Errors (2024.emnlp-main)
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Esther Gan, Yiran Zhao, Liying Cheng, Mao Yancan, Anirudh Goyal, Kenji Kawaguchi, Min-Yen Kan, Michael Shieh
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning using Chain-of-Thought (CoT) prompting. |
| Approach: | They develop an algorithm that iteratively samples typos for words that are important to the query and selects the edit that is most likely to succeed in attacking. |
| Outcome: | The proposed algorithm detects typographical errors in large and closed-source LLMs and shows that they are robust to them. |
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation (2021.acl-demo)
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Weizhen Qi, Yeyun Gong, Yu Yan, Can Xu, Bolun Yao, Bartuer Zhou, Biao Cheng, Daxin Jiang, Jiusheng Chen, Ruofei Zhang, Houqiang Li, Nan Duan
| Challenge: | Existing models for pre-training are not convenient for users to find and set them up. |
| Approach: | They propose to extend ProphetNet into other domains and languages by pre-training models . they pre-train a cross-lingual generation model ProphetNet-Multi and a Chinese generation model . |
| Outcome: | The proposed models achieve new state-of-the-art on 10 benchmarks. |
Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries (2024.naacl-long)
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| Challenge: | Recent studies use in-context learning with large language models (LLM) to find similar dialogue exemplars for prompt learning. |
| Approach: | They propose to use a conversation retriever to find similar in-context examples for prompt learning. |
| Outcome: | The proposed approach improves on multiWOZ datasets with GPT-Neo-2.7B and LLaMA-7B/30B . |
EfficientRAG: Efficient Retriever for Multi-Hop Question Answering (2024.emnlp-main)
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Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng, Fangkai Yang, Jia Liu, Shujian Huang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Existing retrieval-augmented generation methods rely on multiple calls of large language models (LLMs) Large-language models lack knowledge underrepresented in training data and still face hallucinations. |
| Approach: | They propose an efficient retriever for multi-hop question answering that generates new queries iteratively without the need for LLM calls. |
| Outcome: | The proposed method surpasses existing methods on three open-domain multi-hop question-answering datasets. |
Is That Your Final Answer? Test-Time Scaling Improves Selective Question Answering (2025.acl-short)
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| Challenge: | Existing evaluations of test-time scaling assume that a reasoning system should always give an answer to any question provided. |
| Approach: | They propose to increase compute budget at inference time to increase confidence in correct responses by considering settings with non-zero levels of response risk. |
| Outcome: | The proposed model can answer more questions correctly and have higher confidence in correct responses. |
APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training (2025.findings-acl)
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| Challenge: | Large Language Models often require domain-specific fine-tuning to address targeted tasks, which risks degrading their general capabilities. |
| Approach: | They propose to use self-generated dis-preferred weakness data to enhance model performance with a targeted training approach that minimizes interference with existing knowledge base. |
| Outcome: | The proposed approach ensures no reduction in generic capacity and achieves superior performance on downstream tasks compared to existing methods. |
Text2Sql: Pure Fine-Tuning and Pure Knowledge Distillation (2025.naacl-industry)
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| Challenge: | Text2Sql is a task that translates natural language questions and database schemas into SQL queries. |
| Approach: | They employ pure fine-tuning strategy to reduce redundancy by using only 53% of the baseline prompt length to fine- tune the model. |
| Outcome: | The model outperforms the baseline model by 8.2% and 8.6% in Test-suite accuracy (TS) and exact-set-match accuracy (EM) under the most refined Spider dev set of prompts, the model achieves 73.5% and 75.4%, respectively, approaching state-of-the-art (SOTA) levels. |
TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data (2022.emnlp-main)
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| Challenge: | Existing auto-regressive pre-trained language models are challenged by recent emerging numerical reasoning datasets due to the error-prone implicit calculation. |
| Approach: | They propose a pre-computation tool to pre-compute aggregation/arithmetic results for the table in advance, so they are handy and readily available for PLMs to answer numerical reasoning questions. |
| Outcome: | The proposed model improves on TAT-QA and T5 and BART-large on multiple benchmarks. |
Exploiting Intrinsic Multilateral Logical Rules for Weakly Supervised Natural Language Video Localization (2024.acl-long)
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| Challenge: | Existing methods for WS-NLVL rarely consider complex temporal relations enclosing the language query, yielding illogical predictions. |
| Approach: | They propose a plug-and-play method to exploit temporal relations and logical rules for WS-NLVL. |
| Outcome: | The proposed method is able to retrieve the moment corresponding to a language query in a video with only video-language pairs utilized during training. |
VaseVQA: Multimodal Agent and Benchmark for Ancient Greek Pottery (2026.findings-eacl)
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Jinchao Ge, Tengfei Cheng, Biao Wu, Zeyu Zhang, Shiya Huang, Judith Bishop, Gillian Shepherd, Meng Fang, Ling Chen, Yang Zhao
| Challenge: | MLLMs that use domain-specific data are limited in understanding cultural heritage artifacts such as ancient Greek pottery . supervised fine-tuning improves adaptation to domain knowledge, but it struggles with deeper reasoning tasks. |
| Approach: | They propose a visual question-answer tool that augments SFT with reinforcement learning using verifiable rewards. |
| Outcome: | The proposed model outperforms baseline models on reasoning-intensive questions on ancient Greek pottery. |
Understanding the Behaviors of Environment-aware Information Retrieval (2026.acl-long)
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| Challenge: | Recent retrieval-augmented generation approaches have demonstrated strong capability in handling complex queries. |
| Approach: | They propose a branching-based rollout technique that improves training stability . they find different retrievers exhibit distinct optimal query styles . |
| Outcome: | The proposed method improves training stability and improves retrieval-aware systems. |
A Generative Framework for Personalized Sticker Retrieval (2025.findings-emnlp)
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| Challenge: | Existing relevance-based generative retrieval methods lack personalization, leading to a mismatch between diverse user expectations and the retrieved results. |
| Approach: | They propose a representation learning model that learns discriminative user representations to encode user-specific sticker preferences. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in generating relevant stickers for queries. |
Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language Model (2022.coling-1)
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| Challenge: | Neural network models have a large number of parameters to train, but data augmentation is relatively under-explored in natural language processing. |
| Approach: | They propose a bi-directional conditional Masked Language Model (CMLM) that can be conditional on both left and right contexts and the label. |
| Outcome: | The proposed method achieves the best performance on four translation datasets and yields up to 1.90 BLEU points over the baseline. |
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases (2022.coling-1)
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| Challenge: | Existing methods train one encoder-decoder-based model to fit all questions . however, such a one-size-fits-all strategy may not perform well for complex questions involving multiple KB relations or functional constraints. |
| Approach: | They propose a meta-learning framework for complex question generation over knowledge bases . they propose he meta-trained generator can acquire universal meta-knowledge . |
| Outcome: | The proposed framework can acquire universal and transferable meta-knowledge and quickly adapt to long-tailed samples under different dimensions. |
AgentOCR: Reimagining Agent History via Optical Self-Compression (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have enabled agentic systems trained with reinforcement learning over multi-turn interaction, but practical deployment is bottlenecked by rapidly growing textual histories that inflate token and memory costs. |
| Approach: | They propose a framework that represents the accumulated observation-action history as a compact rendered image. |
| Outcome: | The proposed framework preserves over 95% of text-based agent performance while significantly reducing token consumption (>50%), yielding consistent token and memory efficiency. |
VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models (2025.naacl-long)
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| Challenge: | Existing text simplification and paraphrase datasets focus on sentence-level translation. |
| Approach: | They propose a novel academic-to-general-audience text paraphrase dataset . they also propose DSPT5 dynamic soft prompt generative language model . |
| Outcome: | The proposed dataset is the first academic-to-general-audience text paraphrase dataset . it is based on document-level these and dissertation abstract pairs from 8 colleges . |
Entity Pair-guided Relation Summarization and Retrieval in LLMs for Document-level Relation Extraction (2025.findings-naacl)
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| Challenge: | Document-level relation extraction (DocRE) aims to extract relations between entities in a document. |
| Approach: | They propose an entity pair-guided relation summarization and retrieval model for DocRE . the model uses entity pairs to guide relation summaries and retrievals . |
| Outcome: | The proposed model achieves state-of-the-art (SOTA) performance on three datasets. |
CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era (2025.findings-acl)
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Kanzhi Cheng, Wenpo Song, Jiaxin Fan, Zheng Ma, Qiushi Sun, Fangzhi Xu, Chenyang Yan, Nuo Chen, Jianbing Zhang, Jiajun Chen
| Challenge: | Image captioning has been a challenge for vision-language researchers for decades . current VLMs focus on tasks like visual question answering (YA) but image captioning is not as advanced as expected. |
| Approach: | They evaluate VLMs' performance on image captioning using human annotations . they find that some metrics show high caption-level agreement with humans . |
| Outcome: | The proposed model outperforms open-source models on image captioning . it achieves 93.4% correlation with human rankings at $4 per test . |
Don’t be my Doctor! Recognizing Healthcare Advice in Large Language Models (2024.emnlp-industry)
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| Challenge: | Large language models (LLMs) are becoming increasingly popular in everyday use, especially in highly regulated domains such as healthcare, where misleading advice may influence users to commit malpractice. |
| Approach: | They present a large-scale health-advice benchmark dataset that evaluates large language models' ability to recognize health-related advice in industrial settings. |
| Outcome: | The proposed model can be misinterpreted as direct advice in highly regulated domains such as healthcare, but the results are not enough to protect them from misinterpreting them as medical advice. |
XToM: Exploring the Multilingual Theory of Mind for Large Language Models (2026.acl-long)
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Chunkit Chan, Yauwai Yim, Hongchuan Zeng, Zhiying Zou, Xinyuan Cheng, Zhifan Sun, Zheye Deng, Kawai Chung, Yuzhuo Ao, Fan Yixiang, Cheng Jiayang, Ercong Nie, Ginny Wong, Helmut Schmid, Hinrich Schuetze, Simon See, Yangqiu Song
| Challenge: | Existing evaluations of ToM in LLMs are limited to English, neglecting the linguistic diversity that shapes human cognition. |
| Approach: | They propose a multilingual benchmark that evaluates ToM across five languages . they find that models excel in multilingual language understanding, but their ToM performance varies across languages. |
| Outcome: | The proposed benchmark evaluates LLMs across five languages and incorporates diverse task scenarios. |
PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are too large to be fine-tuned with budget constraints and some are only accessible via APIs. |
| Approach: | They propose a pluggable Reward-Driven Contextual Adapter that integrates large language models as generators and trains them to refine the retrieved information. |
| Outcome: | The proposed method improves ReQA performance on three datasets by up to 20% compared to existing methods. |
An Empirical Investigation of Implicit and Explicit Knowledge-Enhanced Methods for Ad Hoc Dataset Retrieval (2023.findings-emnlp)
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| Challenge: | Existing methods for ad hoc dataset retrieval are lexical and cannot capture semantic similarity. |
| Approach: | They propose to implement and evaluate a set of implicit and explicit knowledge-enhancement retrieval methods on two test collections to find semantic matches for ad hoc dataset retrieval. |
| Outcome: | The proposed methods are compared with existing methods on two test collections and reveal the unique features of the task and suggest an interpolation of different kinds of methods as the current best practice. |
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (2022.acl-long)
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| Challenge: | Argument mining (AM) is a computational process that is used to analyze information in a debating system. |
| Approach: | They propose to use a large dataset to automate the manual process of debating . they propose to integrate claim extraction, stance classification and evidence extraction tasks . |
| Outcome: | The proposed tasks can extract claims, stances, evidence and more from a large dataset . the proposed tasks are highly efficient and can be applied to argument mining tasks . |
PACHAT: Persona-Aware Speech Assistant for Multi-party Dialogue (2025.emnlp-main)
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| Challenge: | Extensive research on spoken dialogue systems has advanced the development of intelligent voice assistants, but integration of role information within speech remains an underexplored area. |
| Approach: | They propose a language-based spoken dialogue system that integrates role information within speech to generate contextually appropriate responses. |
| Outcome: | The proposed architecture achieves speaker-specific responses, character understanding, and the generation of targeted replies in multi-party dialogue scenarios, surpassing existing spoken dialogue systems. |
LEDGER: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval (2026.findings-acl)
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| Challenge: | Document editing requires full-context awareness of dependencies, but processing entire documents for each edit incurs prohibitive token costs and latency. |
| Approach: | a framework that constructs lightweight dependency graphs captures semantic relationships and structural hierarchies across document elements is proposed for agentic document editing . a scaLing agentic agentic framework is based on a dependency graph framework that captures dependencies and refactors function dependencies. |
| Outcome: | a new framework achieves 76 consistency versus 56 baseline while reducing token usage by 85 . the framework is based on a framework that captures semantic relationships and structural hierarchies across document elements . it can be used to improve document consistency, but it also reduces token costs and latency . |
RQT: Hierarchical Residual Quantization for Multi-Model Compression (2025.findings-acl)
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| Challenge: | Existing methods for decomposing fine-tuned LLMs are sensitive to the magnitude of delta values. |
| Approach: | They propose a hierarchical quantization framework that shares low-bit integer weights across similar models. |
| Outcome: | The proposed framework achieves an average accuracy degradation of approximately 3% on fine-tuned models across mathematics, coding, chatbot, and Chinese LLMs. |
Bootstrapping Code Translation with Weighted Multilanguage Exploration (2026.acl-long)
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| Challenge: | Existing methods to improve code translation depend on abundant parallel code of high quality, which may not always be available. |
| Approach: | They propose a method that leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning. |
| Outcome: | The proposed method leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning (RL) training. |
Jailbreak LLMs through Internal Stance Manipulation (2025.emnlp-main)
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| Challenge: | Existing approaches to exploit LLMs' inherent safety mechanism, including GCG and AutoDAN, are ineffective for certain malicious requests. |
| Approach: | They propose a method that generates jailbreak prompts to suppress a refusal stance and induce affirmative responses by modifying adversarial prompts. |
| Outcome: | The proposed method outperforms the best baseline approach in Llama-2-7b-chat and achieves a 92.2% success rate across all models. |
WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models (2023.emnlp-industry)
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Jun-Yan He, Zhi-Qi Cheng, Chenyang Li, Jingdong Sun, Wangmeng Xiang, Xianhui Lin, Xiaoyang Kang, Zengke Jin, Yusen Hu, Bin Luo, Yifeng Geng, Xuansong Xie
| Challenge: | Existing typography solutions lack adaptability, creativity, and computational efficiency. |
| Approach: | They propose a user-driven framework for artistic typography synthesis based on the Large Language Model (LLM) the LLM Engine interprets user inputs and generates actionable prompts for the other modules, transforming abstract concepts into tangible designs. |
| Outcome: | The proposed framework incorporates four key modules: the LLM Engine, SemTypo, StyTyPo, and TexTyPO. |
Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification (D19-1)
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| Challenge: | Existing approaches to cross-domain sentiment classification focus on domain-invariant representations, but few focus on the domain-specific information. |
| Approach: | They propose to distill domain-invariant sentiment features with an orthogonal domain-dependent task . the orthogonalist task is built on the aspects varying widely in different domains . |
| Outcome: | The proposed method improves domain-invariant features and transfer performance on three public datasets. |
Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization (2025.findings-acl)
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| Challenge: | Extensive experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization. |
| Approach: | They propose a novel approach that emphasizes extracting inter-user differences to enhance LLM personalization. |
| Outcome: | The proposed approach extracts inter-user differences to enhance LLM personalization. |
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)
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Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun
| Challenge: | Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically. |
| Approach: | They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance. |
| Outcome: | The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs. |
Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction (2021.acl-long)
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| Challenge: | Existing methods to extract relationships from texts depend on memory size and replay these memorized samples in subsequent tasks. |
| Approach: | They propose to use a model to extract relations between entities from texts where the samples of different relations are delivered into the model continuously. |
| Outcome: | The proposed model outperforms the state-of-the-art models and avoids catastrophic forgetting. |
Apeiron: A Scalable LLM-agentic Framework for Autonomous Full-lifecycle Demand-optimized Application Synthesis (2026.findings-acl)
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| Challenge: | Traditional, rigid, 'one-size-fits-all' apps are struggling in the contemporary landscape. |
| Approach: | They propose a scalable and extensible framework for addressing *amorphous* user demands through autonomous, full-lifecycle application synthesis. |
| Outcome: | The proposed framework outperforms baselines in CUA ratings and user-demand task scores across 300 app scenarios, 2,400 personas, and 46,338 demands. |
SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer (2025.findings-naacl)
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| Challenge: | Existing attacks are classified into end-to-end and pre-training types based on the attack phase . Existing backdoor attacks are based upon perplexity, fine-pruning, and maxEntropy. |
| Approach: | They propose an entropy-based poisoning filter that mitigates backdoor attacks . they propose an invisible and universal task-agnostic backdoor attack via syntactic transfer . |
| Outcome: | The proposed attack can transfer backdoors to various downstream tasks while preserving pre-trained language models' pre-training capabilities. |
G-Cap: A Game Character Caption Generator (2026.acl-long)
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| Challenge: | Existing studies on Large Vision-Language Models (LVLMs) primarily focus on real-world scenarios, leaving surreal, highly stylized, and semantically hybrid virtual-world situations significantly underexplored. |
| Approach: | They propose to use a manually annotated benchmark to evaluate LVLMs' ability to perceive and describe game character from the virtual-world. |
| Outcome: | The proposed task evaluates LVLMs’ ability to perceive and describe game character from the virtual-world. |
ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models (2023.emnlp-demo)
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Chenliang Li, He Chen, Ming Yan, Weizhou Shen, Haiyang Xu, Zhikai Wu, Zhicheng Zhang, Wenmeng Zhou, Yingda Chen, Chen Cheng, Hongzhu Shi, Ji Zhang, Fei Huang, Jingren Zhou
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior. |
| Approach: | They propose a framework that equips large language models with tool-use capabilities . they propose LLaMA and Chat-GLM as controllers, and a model-based agent framework . |
| Outcome: | The proposed framework equips open-source LLMs with tool-use capabilities . it provides a user-friendly system library with a customizable engine design . |
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning (2021.findings-emnlp)
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| Challenge: | Existing neural dialogue models only capture syntactic and semantic information, but fail to model the logical consistency between the dialogue history and the generated response. |
| Approach: | They propose a fine-grained comparison model to capture syntactic and semantic information and then compare each candidate's representation with the whole history to obtain a history consistency representation. |
| Outcome: | The proposed model obtains higher ranking scores than baseline models on two public dialogue datasets. |
OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents (2026.acl-long)
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Bowen Yang, Kaiming Jin, Zhenyu Wu, Zhaoyang Liu, Qiushi Sun, Zehao Li, JingJing Xie, Zhoumianze Liu, Fangzhi Xu, Kanzhi Cheng, Yian Wang, Qingyun Li, Yu Qiao, Zun Wang, Zichen Ding
| Challenge: | Vision-Language Models (VLMs) lack visual-aware tutorial retrieval and historical visual context curation and pruning. |
| Approach: | They propose a framework that integrates an orchestrator and a Reflection-Memory Agent for robust automation. |
| Outcome: | Experimental results show that OS-Symphony delivers substantial performance gains across model scales. |
Cross-Thought for Sentence Encoder Pre-training (2020.emnlp-main)
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| Challenge: | Existing models to pretrain sentence encoders with large unlabeled corpus are lacking in linguistic information retrieval. |
| Approach: | They propose a novel approach to pre-training sequence encoder using transformers . they propose to train a Transformer-based sequence encoded over a large set of short sequences based on a set of masked words . |
| Outcome: | The proposed approach outperforms state-of-the-art encoders on hotpotQA by improving intermediate information retrieval performance. |
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. |
RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning (2024.emnlp-main)
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| Challenge: | Pre-trained language models have strong generalizability, but fine-tuning involves updating all parameters, rendering full fine-uning resource-intensive. |
| Approach: | They propose a parameter-efficient fine-tuning method that updates all pre-trained parameters during inference. |
| Outcome: | The proposed method outperforms baseline methods on five benchmarks across 20 datasets. |
The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models (2024.acl-long)
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| Challenge: | a growing number of researchers are studying the hallucination issue in large language models. |
| Approach: | They propose a hallucination detection benchmark and a method to detect hallucines in LLMs. |
| Outcome: | The proposed method detects hallucinations and mitigates them using different training stages. |
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. |
HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation (2025.emnlp-main)
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| Challenge: | Recent studies have incorporated reward models to guide response selection or decoding, aiming to obtain higher-quality data. |
| Approach: | They propose a Hierarchical Sampling framework for self-taught reasoners that allocates a fixed sampling budget to problem boundary-level problems and then reallocates the remaining budget toward high-utility problems during a re-sampling phase. |
| Outcome: | The proposed framework outperforms baseline models without additional sampling budgets across multiple reasoning benchmarks and backbone LLMs. |
Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization on Multi-party Conversation (2025.acl-long)
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Luyao Cheng, Hui Wang, Chong Deng, Siqi Zheng, Yafeng Chen, Rongjie Huang, Qinglin Zhang, Qian Chen, Xihao Li, Wen Wang
| Challenge: | Mainstream speaker diarization systems rely only on acoustic information, making it challenging in complex aural environments. |
| Approach: | They propose a multimodal approach that integrates audio, visual, and semantic cues to enhance speaker diarization. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on multi-party conversations . it integrates audio-visual-semantic cues into the clustering process for acoustic speaker embeddings . |
Chain-of-Skills: A Configurable Model for Open-Domain Question Answering (2023.acl-long)
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| Challenge: | Using customized retrieval models, model transferability and scalability are limited. |
| Approach: | They propose a modular retrieval model where individual modules correspond to key skills that can be reused across datasets. |
| Outcome: | The proposed model outperforms self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA. |
When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval (2026.findings-acl)
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| Challenge: | Existing dense retrieval methods have achieved notable progress, but their effectiveness in legal case retrieval remains limited. |
| Approach: | They propose a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training. |
| Outcome: | The proposed framework outperforms non-evolutionary baselines, including human-designed rules and greedy rule selection, especially when powered by a high-capacity core LLM. |
Empowering Reliable Visual-Centric Instruction Following in MLLMs (2026.findings-acl)
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| Challenge: | Existing benchmarks for evaluating instruction-following capabilities focus on verbal instructions in the textual modality. |
| Approach: | They propose to incorporate vision-dependent constraints into instruction design to enable a more rigorous assessment of how well MLLMs align their outputs with both visual input and textual instructions. |
| Outcome: | The proposed benchmark incorporates vision-dependent constraints into instruction design, enabling a more rigorous and fine-grained assessment of how well MLLMs align their outputs with both visual input and textual instructions. |
A Visual Attention Grounding Neural Model for Multimodal Machine Translation (D18-1)
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| Challenge: | Existing approaches to multimodal machine translation do not integrate visual information into the translation process. |
| Approach: | They propose a multimodal machine translation model that utilizes parallel visual and textual information. |
| Outcome: | The proposed model outperforms existing methods on the Multi30K and Ambiguous COCO datasets. |
RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution (2025.emnlp-main)
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| Challenge: | Experimental results demonstrate the superiority of our approach to aligning large language models with human preferences. |
| Approach: | They propose a method that evaluates and assigns specific credit to each token using an off-the-shelf reward model. |
| Outcome: | The proposed method evaluates and assigns specific credit to each token using an off-the-shelf reward model. |
MarkQA: A large scale KBQA dataset with numerical reasoning (2023.emnlp-main)
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| Challenge: | Existing KBQA datasets are insufficient for numerical reasoning . existing KBqa datasets lack multi-hop reasoning and numerical reasoning. |
| Approach: | They propose a task that necessitates the ability to perform multi-hop reasoning and numerical reasoning. |
| Outcome: | The proposed task necessitates the ability to perform multi-hop reasoning and numerical reasoning. |
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation (2020.acl-main)
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| Challenge: | Recent work in neural machine translation has led to dramatic improvements in both research and commercial systems. |
| Approach: | They propose a adversarial augmentation method for Neural Machine Translation that minimizes vicinal risk over virtual sentences . they use a novel vicinity distribution for adversarials to describe a smooth interpolated embedding space . |
| Outcome: | The proposed method outperforms the current method on Chinese-English, English-French, and English-German translation benchmarks. |
Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking (2026.acl-long)
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| Challenge: | identifying relevant documents for Reasoning-intensive queries remains a challenge . large language models have shown strong performance in zero-shot document reranking . |
| Approach: | They propose a reranking algorithm that estimates contextual relevance by aggregating LLMs' relevance judgments across batches. |
| Outcome: | The proposed algorithm improves nDCG@10 over retrieval and reranking baselines by 15% and 6–21% respectively. |
Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are difficult to interpret due to their black-box nature and randomness. |
| Approach: | They propose a new method which enhances influence functions by addressing fitting errors by eliminating knowledge bias present in the base model before fine-tuning. |
| Outcome: | The proposed method outperforms existing methods and achieves an average AUC of 91.64%. |
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension (2022.acl-long)
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| Challenge: | Existing methods and limitations for machine reading comprehension are insufficient for logical reasoning over text. |
| Approach: | They propose a neural-symbolic approach which passes messages over a graph representing logical relations between text units to predict an answer. |
| Outcome: | The proposed approach outperforms existing methods on ReClor and LogiQA. |
VisuoThink: Empowering LVLM Reasoning with Multimodal Tree Search (2025.acl-long)
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| Challenge: | Existing approaches to large vision-language models fail to capture interleaved nature of human visual-verbal reasoning processes. |
| Approach: | They propose a framework that integrates visuospatial and linguistic domains to facilitate multimodal slow thinking by enabling progressive visual-textual reasoning. |
| Outcome: | Experiments show that VisuoThink significantly improves reasoning capabilities even without fine-tuning. |
Plug-in Language Model: Controlling Text Generation with a Simple Regression Model (2024.findings-naacl)
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| Challenge: | Large-scale pre-trained language models have demonstrated unrivaled capacity in generating text that closely resembles human-written content. |
| Approach: | They propose a plug-in language model that leverages reinforcement learning to adjust latent states to control text generation. |
| Outcome: | The proposed model outperforms existing methods that rely on gradient-based, weighted decoding, or prompt-based methods. |
Revisiting LoRA through the Lens of Parameter Redundancy: Spectral Encoding Helps (2025.findings-acl)
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| Challenge: | Low-Rank Adaptation (LoRA) has emerged as a prominent technique for fine-tuning large foundation models. |
| Approach: | They propose a low-rank Adaptation technique that harnesses the expressiveness of spectral bases to re-parameterize LoRA from a sparse spectral subspace. |
| Outcome: | The proposed technique achieves greater efficiency with fewer parameters than baselines on various downstream tasks, including commonsense reasoning, math reasoning, and code generation. |
BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations (2022.naacl-industry)
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Md Tahmid Rahman Laskar, Cheng Chen, Aliaksandr Martsinovich, Jonathan Johnston, Xue-Yong Fu, Shashi Bhushan Tn, Simon Corston-Oliver
| Challenge: | Existing systems that align textual mentions of entities to knowledge bases are difficult to deploy in production environments. |
| Approach: | They propose a neural entity linking system that connects entities in business phone conversations to their corresponding Wikipedia and Wikidata entries. |
| Outcome: | The proposed system improves inference speed and memory consumption while maintaining high accuracy. |
Rhythm Controllable and Efficient Zero-Shot Voice Conversion via Shortcut Flow Matching (2025.acl-long)
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Jialong Zuo, Shengpeng Ji, Minghui Fang, Mingze Li, Ziyue Jiang, Xize Cheng, Xiaoda Yang, Chen Feiyang, Xinyu Duan, Zhou Zhao
| Challenge: | Existing methods focus on disentangling speakers and content, while others focus on preserving the source's prosody. |
| Approach: | They propose a rhythm-controllable and efficient zero-shot voice conversion model that transforms the source speaker’s timbre into an unseen one while retaining speech content. |
| Outcome: | The proposed model adapts the linguistic content duration to the desired speaking style, facilitating the transfer of the target speaker’s rhythm. |
Learning to Plan and Realize Separately for Open-Ended Dialogue Systems (2020.findings-emnlp)
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Sashank Santhanam, Zhuo Cheng, Brodie Mather, Bonnie Dorr, Archna Bhatia, Bryanna Hebenstreit, Alan Zemel, Adam Dalton, Tomek Strzalkowski, Samira Shaikh
| Challenge: | Existing approaches to natural language generation are construed as end-to-end systems . however, some issues persist, such as coherence of output and repetition/hallucination of tokens . |
| Approach: | They propose to decouple natural language generation into two phases: planning and realization. |
| Outcome: | The proposed approach performs better than an end-to-end approach. |
Astra: Activation-Space Tail-Eigenvector Low-Rank Adaptation of Large Language Models (2026.findings-acl)
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| Challenge: | Existing methods for fine-tuning pre-trained models are limited due to suboptimal activation subspaces. |
| Approach: | They propose a method that leverages tail eigenvectors of model output activations to construct low-rank adapters. |
| Outcome: | The proposed method outperforms existing methods across 16 benchmarks and surpasses full fine-tuning in certain scenarios. |
The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning (2025.findings-acl)
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Bingxiang He, Ning Ding, Cheng Qian, Jia Deng, Ganqu Cui, Lifan Yuan, Haiwen Hong, Huan-ang Gao, Longtao Huang, Hui Xue, Huimin Chen, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing work on instruction tuning has focused on task level, without considering that tasks are artificially defined and, to LLMs, merely consist of tokens and representations. |
| Approach: | They propose a training data arrangement framework that allows for continual learning and loss reduction. |
| Outcome: | The proposed framework promotes continual learning and loss reduction on unseen tasks. |
RAG-HAT: A Hallucination-Aware Tuning Pipeline for LLM in Retrieval-Augmented Generation (2024.emnlp-industry)
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| Challenge: | Retrieval-augmented generation (RAG) has emerged as a significant advancement in the field of large language models (LLMs). |
| Approach: | They propose a method that uses hallucination detection labels to correct hallucines by integrating up-to-date information into their initial training. |
| Outcome: | The proposed method is based on the Retrieval Augmented Generation (RAG) method, which has shown to be effective in mitigating hallucinations and improving answer quality. |
Mapping the Design Space of Human-AI Interaction in Text Summarization (2022.naacl-main)
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| Challenge: | Automated text summarization systems involve humans for preparing data or evaluating model performance, yet, there is no systematic understanding of human-AI interactions and how to design for them. |
| Approach: | They conducted a systematic literature review of 70 papers and designed prototypes for each interaction. |
| Outcome: | The proposed design considerations were based on the results of a systematic literature review of 70 papers and interviews with 16 users. |
PsyMem: Fine-grained Psychological Alignment and Explicit Memory Control for Advanced Role-Playing LLMs (2026.tacl-1)
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| Challenge: | Existing role-playing models rely on superficial textual descriptions or simplistic metrics, inadequately modeling both intrinsic and extrinsic character dimensions. |
| Approach: | They propose a framework that integrates fine-grained psychological attributes and explicit memory control for role-playing. |
| Outcome: | The proposed framework outperforms baseline models in human-likeness and character fidelity. |
Music Audio-Visual Question Answering Requires Specialized Multimodal Designs (2026.findings-acl)
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Wenhao You, Xingjian Diao, Wenjun Huang, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Tingxuan Wu, Ming Cheng, Soroush Vosoughi, Jiang Gui
| Challenge: | Music audio-visual question answering presents unique challenges with dense audio-visual content, intricate temporal dynamics, and the need for domain-specific knowledge. |
| Approach: | They analyze Music AVQA datasets and analyze their results to identify key design patterns . they propose concrete future directions for incorporating musical priors . |
| Outcome: | The proposed architectures are critical for success in Music AVQA, the authors argue . they suggest concrete future directions for incorporating musical priors . |
Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) excel at straightforward reasoning tasks, but struggle when faced with complex multi-step reasoning. |
| Approach: | They propose a framework that converts unstructured text into a graph and instructs LLMs to navigate this graph using task-specific strategies. |
| Outcome: | The proposed framework improves the multi-step reasoning capabilities of Large Language Models in a zero-shot setting. |
How do autoregressive transformers solve full addition? (2025.emnlp-main)
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| Challenge: | Large pre-trained language models have demonstrated impressive capabilities, but there is still much to learn about how they operate. |
| Approach: | They investigate the ability of the autoregressive transformer to perform basic addition operations by using causal analysis to find that a few different attention heads in the middle layers control the addition carry . they found that due to the lack of global focus on the sequence within these attention heads, the model struggles to handle long-sequence addition tasks. |
| Outcome: | The model performs basic addition tasks, but it still faces challenges with length generalization. |
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)
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Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao
| Challenge: | Existing methods focus on knowledge and linguistic patterns of characters. |
| Approach: | They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits. |
| Outcome: | The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits. |
Event-Content-Oriented Dialogue Generation in Short Video (2024.naacl-long)
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| Challenge: | Existing multi-modal dialogue models are limited to incapacity of reading visual information and multi-dimensional interactions. |
| Approach: | They propose a novel event-oriented video-dialogue dataset called SportsVD to overcome these challenges by generating human-like response according to event contents in the video and related external knowledge. |
| Outcome: | The proposed method outperforms existing methods on SportsVD and other baselines under several automatic metrics. |
QuoteR: A Benchmark of Quote Recommendation for Writing (2022.acl-long)
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| Challenge: | Existing methods to recommend quotes are evaluated on unpublished datasets . |
| Approach: | They propose to build a dataset that is open and contains three parts including English, standard Chinese and classical Chinese. |
| Outcome: | The proposed model outperforms existing methods on all three parts of QuoteR. |
MultiAgentBench : Evaluating the Collaboration and Competition of LLM agents (2025.acl-long)
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Kunlun Zhu, Hongyi Du, Zhaochen Hong, Xiaocheng Yang, Shuyi Guo, Zhe Wang, Zhenhailong Wang, Cheng Qian, Robert Tang, Heng Ji, Jiaxuan You
| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents, yet existing benchmarks focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition. |
| Approach: | They propose a benchmark to evaluate LLM-based multi-agent systems across diverse, interactive scenarios. |
| Outcome: | The proposed framework measures task completion and quality of collaboration and competition using novel, milestone-based key performance indicators. |
Hidden Ghost Hand: Unveiling Backdoor Vulnerabilities in MLLM-Powered Mobile GUI Agents (2025.findings-emnlp)
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Pengzhou Cheng, Haowen Hu, Zheng Wu, Zongru Wu, Tianjie Ju, Daizong Ding, Zhuosheng Zhang, Gongshen Liu
| Challenge: | MLLM-powered GUI agents expose multiple interaction-level triggers, causing backdoor attacks . backdoor injection maximizes feature difference across sample classes, improving flexibility . |
| Approach: | They propose a framework for red-teaming backdoor attacks using MLLMs . they construct composite triggers by combining goal and interaction levels . |
| Outcome: | The proposed framework is effective and stealthy for red-teaming backdoor attacks. |
On the Impact of Cross-Domain Data on German Language Models (2023.findings-emnlp)
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Amin Dada, Aokun Chen, Cheng Peng, Kaleb Smith, Ahmad Idrissi-Yaghir, Constantin Seibold, Jianning Li, Lars Heiliger, Christoph Friedrich, Daniel Truhn, Jan Egger, Jiang Bian, Jens Kleesiek, Yonghui Wu
| Challenge: | Traditionally, large language models have been trained on general web crawls or domain-specific data. |
| Approach: | They present a German dataset and a dataset aimed at containing high-quality data to examine the importance of data diversity over quality. |
| Outcome: | The proposed model outperforms models trained on quality data on multiple downstream tasks. |
Implicit Argument Prediction with Event Knowledge (N18-1)
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| Challenge: | Existing work on identifying implicit arguments has been limited due to large number of features and small datasets . a neural model that uses narrative coherence and entity salience is used to train implicit arguments . |
| Approach: | They propose to train models for implicit argument prediction on a simple cloze task . they use narrative coherence and entity salience to build a neural model . |
| Outcome: | The proposed model performs better on synthetic and natural data. |
AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents (2025.acl-long)
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Yifan Xu, Xiao Liu, Xueqiao Sun, Siyi Cheng, Hao Yu, Hanyu Lai, Shudan Zhang, Dan Zhang, Jie Tang, Yuxiao Dong
| Challenge: | Existing studies on Android agents lack systematic research on open-source and closed-source models. |
| Approach: | They propose a framework for Android agents that includes an operation environment and a reproducible benchmark. |
| Outcome: | The proposed framework lifts the success rate of open-source LLMs and LMMs from 4.59% to 21.50% for LLM and 1.93% to 13.28% for LMM. |
“Not Aligned” is Not “Malicious”: Being Careful about Hallucinations of Large Language Models’ Jailbreak (2025.coling-main)
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| Challenge: | “Jailbreak” is a major safety concern of Large Language Models (LLMs). |
| Approach: | They propose a benchmarking framework to evaluate "jailbreak" outputs . they propose specialized validation framework to ensure outputs are useful malicious instructions . |
| Outcome: | The proposed framework enhances existing benchmarks to ensure outputs are useful . it also aims to evaluate the true potential of jailbroken outputs to cause harm to human society. |
Linking Knowledge to Care: Knowledge Graph-Augmented Medical Follow-Up Question Generation (2026.findings-eacl)
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| Challenge: | Existing large language models (LLMs) fail to identify information gaps across diverse symptoms. |
| Approach: | They propose a Knowledge Graph-augmented LLM with active in-context learning to generate relevant and important follow-up questions. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by 5% - 8% on relevant benchmarks. |
Semantic Structure Enhanced Event Causality Identification (2023.acl-long)
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| Challenge: | Existing methods for Event Causality Identification (ECI) capture implicit associations between events, which are difficult because they lack the ability to understand the associations between two events. |
| Approach: | They propose a model that captures the implicit associations between two events and integrates the event-centric structure information into a GNN-based event aggregator. |
| Outcome: | The proposed model improves on three widely used datasets showing that it integrates event-centric and event-associated semantic elements and captures event associations. |
Do LLMs Play Dice? Exploring Probability Distribution Sampling in Large Language Models for Behavioral Simulation (2025.coling-main)
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| Challenge: | LLMs are used to emulate sequential decision-making processes of humans . however, their ability to perform probabilistic sampling is limited . |
| Approach: | They propose to use large language models (LLMs) as agents to emulate the sequential decision-making processes of humans represented as Markov decision-makers (MDPs). |
| Outcome: | The proposed models can understand probabilities, but struggle with sampling precision . integrating coding tools can improve sampling precision, but this level of sampling precision still makes it difficult to simulate human behavior as agents. |
MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing (2026.acl-demo)
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| Challenge: | Large language model-based multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. |
| Approach: | They propose a graph-centric framework for orchestrating large language model-based multi-agent systems . they compile a user's natural-language intent into an editable workflow specification and then into an executable graph . |
| Outcome: | The proposed framework compiles natural-language intent into an executable graph and then compile and executes it at runtime. |
Leveraging Language-based Representations for Better Solving Symbol-related Problems with Large Language Models (2025.coling-main)
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| Challenge: | Symbols are used in abstract reasoning, chemical property prediction, and tabular question-answering. |
| Approach: | They propose a method that converts symbols to language-based representations to improve their accuracy. |
| Outcome: | The proposed method improves the accuracy of symbols in language-based models. |
Contrastive Token-Wise Meta-Learning for Unseen Performer Visual Temporal-Aligned Translation (2023.findings-acl)
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| Challenge: | a novel generalization framework for visual temporal-aligned translation is proposed to transfer recognition skills to unseen performers . ambiguity in the visual sequence can hinder current methods for visual language translation . |
| Approach: | They propose a generalizable framework to transfer recognition skills to unseen performers . they use visual temporal-aligned translation to generate multiple words autoregressively . |
| Outcome: | The proposed framework is generalized to transfer recognition skills to unseen performers . it is compared with existing methods on lipreading and fingerspelling datasets . |
Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction (2025.emnlp-main)
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| Challenge: | Event Extraction (EE) is a long-standing target, but lacks an efficient and effective annotation framework to construct the corresponding datasets. |
| Approach: | They propose an LLM-based collaborative annotation framework that refines annotations of triggers from distant supervision and carries out argument annotation. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on the largest EE dataset to date . it achieves the F1 scores of 90% and 85.3% on the human-annotated test set . |
Speculative Safety-Aware Decoding (2025.emnlp-main)
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| Challenge: | Speculative Safety-Aware Decoding (SSD) equips large language models with desired safety property while accelerating inference. |
| Approach: | They propose a lightweight decoding-time approach that equips large models with the desired safety property while accelerating inference. |
| Outcome: | Experimental results show that a small language model has the desired safety property while accelerating inference. |
Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.acl-long)
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| Challenge: | Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections. |
| Approach: | They propose a co-temporal Question Answering benchmark that contains four co-time scenarios with 4,748 samples for evaluating the co-timing abilities of large language models. |
| Outcome: | The proposed benchmarks show that current LLMs struggle on CoTempQA tasks even when enhanced with Chain of Thought methodologies. |
LLMEdgeRefine: Enhancing Text Clustering with LLM-Based Boundary Point Refinement (2024.emnlp-main)
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| Challenge: | LLMEdgeRefine is an iterative clustering method enhanced by large language models . existing clustering methods struggle with domain-specific fine-tuning and outliers . |
| Approach: | They propose an iterative clustering method enhanced by large language models focusing on edge points refinement . authors propose to use LLMs to iterate clusters and iterating to improve semantic coherence . |
| Outcome: | The proposed method outperforms state-of-the-art methods and offers robustness, adaptability, and cost-efficiency for diverse text clustering applications. |
Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs (2025.emnlp-main)
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Hexiang Tan, Fei Sun, Sha Liu, Du Su, Qi Cao, Xin Chen, Jingang Wang, Xunliang Cai, Yuanzhuo Wang, Huawei Shen, Xueqi Cheng
| Challenge: | Existing detection methods fail to account for **self-consistent error** . study identifies self-consistency errors and evaluates them . |
| Approach: | They propose a method that fuses hidden state evidence from an external verifier LLM to detect self-consistent errors. |
| Outcome: | The proposed method significantly enhances performance on self-consistent errors across three LLM families. |
ImCoref-CeS: An Improved Lightweight Pipeline for Coreference Resolution with LLM-based Checker-Splitter Refinement (2026.acl-long)
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Kangyang Luo, Yuzhuo Bai, Shuzheng Si, Cheng Gao, Zhitong Wang, Yingli Shen, Wenhao Li, Zhu Liu, Yufeng Han, Jiayi Wu, Cunliang Kong, Maosong Sun
| Challenge: | Existing supervised neural methods for coreference resolution are underexplored . current methods rely on small language models, but their potential is underexploited . |
| Approach: | They propose a framework that integrates an enhanced supervised model with LLM-based reasoning. |
| Outcome: | The proposed method surpasses existing state-of-the-art methods in coreference resolution. |
Large Language Model Agents in Finance: A Survey Bridging Research, Practice, and Real-World Deployment (2025.findings-emnlp)
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Yifei Dong, Fengyi Wu, Kunlin Zhang, Yilong Dai, Sanjian Zhang, Wanghao Ye, Sihan Chen, Zhi-Qi Cheng
| Challenge: | a systematic review of large language models (LLMs) is conducted to better align their capabilities with real-world demands. |
| Approach: | They propose a functional taxonomy mapping financial domains to tasks, datasets, and institutional constraints. they catalog over 30 financial benchmarks and 20 representative models. |
| Outcome: | The proposed model frameworks are bridging financial practice and LLM research. |
EDSD: Entropy-Driven Design for Faster Speculative Decoding (2026.acl-long)
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| Challenge: | Existing methods for speculative decoding incur substantial training overhead to mitigate information misalignment between autoregressive draft model training and decoding. |
| Approach: | They propose an Entropy-Driven Speculative Decoding framework that uses entropy as a unified, interpretable signal for both draft model training and architectural design. |
| Outcome: | Experiments on seven large language models show that EDSD improves training efficiency by 24.8% and increases acceptance length by 4.0% compared to state-of-the-art methods. |
Modeling Human-Like Cognition for Stance Detection: Integrating Intuitive Judgment and Analytical Reasoning (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have revolutionized stance detection, enabling complex reasoning strategies such as chain-of-thought prompting. |
| Approach: | They propose Cognitive-Driven Stance Detection (CDSD) that integrates fast intuitive judgment and analytical reasoning enhanced by three key modules: attention-based cognitive alignment to compare system focus, uncertainty-aware belief update using Bayesian inference, and self-doubt-triggered counterfactual reasoning for re-evaluation under low consistency or high uncertainty. |
| Outcome: | The proposed method outperforms state-of-the-art methods on SEM16, P-Stance, and VAST. |
Auto-Stega: An Agent-Driven System for Lifelong Strategy Evolution in LLM-Based Text Steganography (2026.findings-acl)
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| Challenge: | prevailing methods rely on hand-crafted or pre-specified strategies and struggle to balance efficiency, imperceptibility, and security, particularly at high embedding rates. |
| Approach: | They propose an agent-driven self-evolving framework that is the first to realize self-changing steganographic strategies by automatically discovering, composing, and adapting strategies at inference time. |
| Outcome: | The proposed framework achieves 42.2% perplexity and 1.6% anti-steganalysis performance over SOTA methods at high embedding rates. |
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 . |
Semantically Comprehensive Token Pruning in LVLMs via Maximizing Concept Coverage (2026.acl-long)
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| Challenge: | Existing visual token pruning methods leverage simple metrics derived from human experience, such as attention or similarity, to rank and select tokens within a highly entangled feature space. |
| Approach: | They propose a novel visual token pruning method that uses a concept-driven paradigm to quantify the Marginal Semantic Gain of each token's contribution to uncovered concepts. |
| Outcome: | The proposed method outperforms state-of-the-art methods in a concept-driven model while maintaining semantic completeness. |
SDAR-VL: Stable and Efficient Block-wise Diffusion for Vision-Language Understanding (2026.acl-long)
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| Challenge: | Existing block-wise discrete diffusion models lack robust autoregressive (AR) decoders. |
| Approach: | They propose a block-wise discrete diffusion framework for large-scale vision-language understanding with a progressive beta noise curriculum. |
| Outcome: | The proposed framework improves training efficiency, convergence stability, and task performance over conventional block diffusion. |
CircuitSynth: Reliable Synthetic Data Generation (2026.findings-acl)
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| Challenge: | Existing approaches lack mechanisms to balance linguistic expressivity with formal guarantees regarding validity and coverage. |
| Approach: | They propose a neuro-symbolic framework that decouples semantic reasoning from surface realization. |
| Outcome: | The proposed framework achieves 100% Schema Validity even in complex logic puzzles where unconstrained baselines fail (12.4%) while outperforming state-of-the-art methods in rare-combination coverage. |
PCAD: Towards ASR-Robust Spoken Language Understanding via Prototype Calibration and Asymmetric Decoupling (2024.acl-long)
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| Challenge: | Spoken language understanding (SLU) suffers from error propagation from automatic speech recognition (ASR) in actual scenarios. |
| Approach: | They propose a framework which calibrates bias and errors and achieves adaptive-balanced decoupling training by a prototype-based loss model. |
| Outcome: | The proposed framework outperforms existing approaches and achieves state-of-the-art performance on three datasets. |
Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts? (2024.acl-long)
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| Challenge: | Recent advances in augmenting Large Language Models (LLMs) with auxiliary information have significantly revolutionized their efficacy in knowledge-intensive tasks. |
| Approach: | They propose a systematic framework to identify whether LLMs’ responses are attributed to either generated or retrieved contexts. |
| Outcome: | The proposed framework identifies whether LLMs’ responses are attributed to either generated or retrieved contexts. |
Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios. |
| Approach: | They propose a framework that internalizes domain knowledge through internal-external knowledge self-selection and selective supervised fine-tuning. |
| Outcome: | The proposed framework outperforms existing methods and matches domain-pretrained LLMs with significantly lower cost. |
DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLMs Jailbreakers (2024.findings-emnlp)
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| Challenge: | Existing jailbreaking methods view a malicious prompt as a whole but they are not effective at reducing LLMs’ attention on combinations of words with malice. |
| Approach: | They propose an automatic prompt Decomposition and Reconstruction framework for jailbreaking Attack that decomposes a malicious prompt into separate sub-prompts and reassembles them implicitly by In-Context Learning. |
| Outcome: | The proposed framework reduces LLMs' attention on malice words by presenting them to LLM in a fragmented form, addressing these limitations and improving attack effectiveness. |
Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants (2022.emnlp-industry)
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| Challenge: | Out of Scope (OOS) detection is a problem with chatbots that cannot make sense of a query . a real-world solution to this problem is to identify out-of-domain queries . |
| Approach: | They propose a simple yet effective OOS detection method that outperforms standard methods . they propose analyzing data from an enterprise virtual assistant platform to test the method . |
| Outcome: | The proposed method outperforms standard OOS detection methods in a real-world deployment of virtual assistants. |
Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering (2025.acl-long)
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| Challenge: | Existing studies focus on prompt engineering to encode the full semantics of a sentence into the embedding of the last token. |
| Approach: | They propose a technique that introduces an extra auxiliary prompt to elicit better sentence embedding . they propose to use the hidden state of the token as the sentence embedded in LLMs . |
| Outcome: | The proposed technique can improve performance of existing prompt-based methods on STS tasks and downstream classification tasks. |
Simple or Complex? Complexity-controllable Question Generation with Soft Templates and Deep Mixture of Experts Model (2021.findings-emnlp)
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| Challenge: | Existing work on complex questions does not consider controlling complexity of generated questions. |
| Approach: | They propose an end-to-end neural complexity-controllable question generation model that incorporates a mixture of experts as the selector of soft templates to capture question similarity while avoiding the expensive construction of actual templates. |
| Outcome: | The proposed model is superior to state-of-the-art methods in both automatic and manual evaluations on two benchmark QA datasets. |
A Generative Model for Joint Natural Language Understanding and Generation (2020.acl-main)
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| Challenge: | Natural language understanding (NLU) and natural language generation (NLG) have opposite goals. |
| Approach: | They propose a generative model which couples NLU and NLG through a shared latent variable. |
| Outcome: | The proposed model achieves state-of-the-art performance on two dialogue datasets with flat and tree-structured formal representations. |
VP-MEL: Visual Prompts Guided Multimodal Entity Linking (2025.findings-acl)
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| Challenge: | Existing methods for multimodal entity linking rely on mention words as retrieval cues, which limits their ability to effectively utilize information from both images and text. |
| Approach: | They propose a visual prompt-guided multimodal entity linking task for a text-image pair . they propose VPWiki to facilitate this task and a framework to capture latent information. |
| Outcome: | The proposed framework outperforms baseline methods on a VPWiki dataset. |
TrustAgent: Towards Safe and Trustworthy LLM-based Agents (2024.findings-emnlp)
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| Challenge: | Existing LLMs are primarily used for simple text-related tasks, but LLM-based agents can undertake more complex tasks that require planning and interaction with the physical world and humans. |
| Approach: | They propose an Agent-Constitution-based agent framework with a particular focus on improving the LLM-based agents' safety. |
| Outcome: | The proposed framework can enhance an LLM agent’s safety across multiple domains by identifying and mitigating potential dangers during the planning process. |
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. |
MTAVG-Bench: A Diagnostic Benchmark for Multi-Talker Dialogue-Centric Audio-Video Generation (2026.acl-long)
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Yanghao Zhou, Haitian Li, Rexar Lin, Heyan Huang, Jinxing Zhou, Changsen Yuan, Tian Lan, Ziqin Zhou, Yudong Li, Jiajun Xu, Jingyun Liao, YiMing Cheng, Xuefeng Chen, Xian-Ling Mao, Yousheng Feng
| Challenge: | Existing evaluation benchmarks for text-to-audio-video (T2AV) generation are largely designed for human-recorded videos or single-speaker settings. |
| Approach: | They propose a failure-driven diagnostic benchmark for multi-talker dialogue-centric audio-video generation. |
| Outcome: | The benchmark evaluates multi-speaker dialogue generation at four levels: audio-visual signal fidelity, temporal attribute consistency, social interaction, and cinematic expression. |
G2S: A General-to-Specific Learning Framework for Temporal Knowledge Graph Forecasting with Large Language Models (2025.findings-acl)
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| Challenge: | Recent studies have introduced Large Language Models (LLMs) for this task to enhance the models’ generalization abilities. |
| Approach: | They propose a General-to-Specific learning framework that disentangles the learning processes of two kinds of knowledge in a temporal temporal structure. |
| Outcome: | The proposed framework disentangles the learning processes of the above two kinds of knowledge and improves their generalization abilities. |
TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation (2024.findings-acl)
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Xize Cheng, Rongjie Huang, Linjun Li, Zehan Wang, Tao Jin, Aoxiong Yin, Chen Feiyang, Xinyu Duan, Baoxing Huai, Zhou Zhao
| Challenge: | Existing methods for talking head translation rely on cascading, resulting in delays and cascadic errors. |
| Approach: | They propose a model for talking head translation, TransFace, which can translate audio-visual speech into audio-visual speech in other languages. |
| Outcome: | The proposed model can translate audio-visual speech into audio-visual speech in other languages. |
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation (2022.acl-long)
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Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, Jian-Guang Lou, Dongmei Zhang
| Challenge: | Existing studies on table reasoning focus on flat tables and hierarchical tables . a new dataset, HiTab, aims to examine numerical reasoning over hierarchic tables based on hierarchically structured tables - a strong challenge for existing baselines and a valuable benchmark for future research. |
| Approach: | They propose a hierarchical question answering and natural language generation dataset to study hierarchic tables. |
| Outcome: | The proposed model shows that it is effective in QA and natural language generation over hierarchical tables. |
Stop Hardening Everything: A Training-Free Neuron-Level Defense for Neural Ranking Models (2026.acl-long)
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| Challenge: | Existing defenses for neural ranking models are data-centric and require retraining and adversarial data generation. |
| Approach: | They propose a model-centric defense that addresses vulnerability at its architectural source without costly retraining or adversarial data generation. |
| Outcome: | The proposed approach outperforms state-of-the-art models on MS MARCO and TREC 19 while maintaining strong performance on clean data. |
DyePack: Provably Flagging Test Set Contamination in LLMs Using Backdoors (2025.emnlp-main)
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| Challenge: | Open benchmarks are essential for evaluating large language models, but their accessibility makes them likely targets of test set contamination. |
| Approach: | They propose a framework that leverages backdoor attacks to flag models that used benchmark test sets during training. |
| Outcome: | The proposed framework detects models that trained on benchmark test sets without loss of logits or internal details . it can prevent false accusations while providing strong evidence for every detected case of contamination. |
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning (2020.findings-emnlp)
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| Challenge: | Existing models with independent classifiers for each TLINK category hinder from using the whole data. |
| Approach: | They propose a temporal relation classification model that manages dynamic event representations across multiple TLINKs using multi-task learning to leverage the full size of data. |
| Outcome: | The proposed model outperforms state-of-the-art models and two strong transfer learning baselines on English and Japanese data. |
Look, Compare, Decide: Alleviating Hallucination in Large Vision-Language Models via Multi-View Multi-Path Reasoning (2025.coling-main)
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| Challenge: | Large Vision-Language Models (LVLMs) have impressive capabilities in multi-modal context comprehension, but they still suffer from hallucination problems due to inconsistent outputs with the image content. |
| Approach: | They propose a training-free framework MVP to reduce hallucinations in Large Vision-Language Models . they propose multi-view information-seeking strategy to perceive the comprehensive information in the image . |
| Outcome: | The proposed framework reduces hallucinations in large vision-language models by combining multi-view multi-path reasoning with multi-vision multi-path reasoning. |
SAM3-I: Segment Anything with Instructions (2026.acl-long)
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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng
| Challenge: | Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering. |
| Approach: | They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework. |
| Outcome: | Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability. |
Q-Mamba: Towards more efficient Mamba models via post-training quantization (2025.findings-acl)
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| Challenge: | Existing studies show that Mamba architectures have room for further optimization in linear projections and state caches. |
| Approach: | They propose a decoupled scale quantization scheme to mitigate outliers in states and channels by applying separate quantization scales. |
| Outcome: | The proposed method reduces memory consumption by 50% across various quantization settings, model sizes, and generation and zero-shot tasks. |
RepoShapley: Shapley-Enhanced Context Filtering for Repository-Level Code Completion (2026.findings-acl)
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| Challenge: | Large language models have strong reasoning, coding, and generation capabilities, but retrieval-augmented generation remains difficult under fixed context budgets. |
| Approach: | They propose a coalition-aware context filtering framework supervised by Shapley-style marginal contributions that captures sign effects via teacher-forced probing and computes exact Shaply values for small retrieval sets. |
| Outcome: | Experiments show that RepoShapley improves completion quality while reducing harmful context and unnecessary retrieval. |
Measuring Uncertainty in Neural Machine Translation with Similarity-Sensitive Entropy (2024.eacl-long)
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| Challenge: | Uncertainty estimation is an important diagnostic tool for statistical models. |
| Approach: | They propose to adapt similarity-sensitive Shannon entropy (S3E) for NMT by incorporating a concept borrowed from theoretical ecology. |
| Outcome: | The proposed framework improves quality estimation and named entity recall, and improves translation quality. |
Mem2Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation (2026.acl-long)
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Zihao Cheng, Zeming Liu, Yingyu Shan, Xinyi Wang, Xiangrong Zhu, Yunpu Ma, Hongru Wang, Yuhang Guo, Wei Lin, Yunhong Wang
| Challenge: | Existing frameworks that focus on static tools and static assets are ineffective for self-evolving agents. |
| Approach: | They propose a paradigm of co-evolutionary Capability Expansion and Experience Distillation that leverages accumulated experience to guide dynamic creation of assets. |
| Outcome: | The proposed framework improves performance in single-task and cross-task settings by 18.53% over standard LLMs, 11.80% over agents evolving solely through experience, and 6.46% over those evolving solelly through asset creation. |
UnitedQA: A Hybrid Approach for Open Domain Question Answering (2021.acl-long)
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| Challenge: | Recent work on open-domain question answering focuses on either extractive or generative readers exclusively. |
| Approach: | They propose a hybrid approach to extractive and generative readers that leverages both models. |
| Outcome: | The proposed approach outperforms state-of-the-art models on NaturalQuestions and TriviaQA respectively. |
TurnBack: A Geospatial Route Cognition Benchmark for Large Language Models through Reverse Route (2025.emnlp-main)
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Hongyi Luo, Qing Cheng, Daniel Matos, Hari Krishna Gadi, Yanfeng Zhang, Lu Liu, Yongliang Wang, Niclas Zeller, Daniel Cremers, Liqiu Meng
| Challenge: | Existing studies on large language models have limited evaluation of their geospatial cognition . a unified framework for evaluating geospcial cognition in LLMs remains absent . |
| Approach: | They propose a benchmark to evaluate the geospatial route cognition of Large Language Models . they propose 'pathbuilder' tool for converting natural language instructions into navigation routes . |
| Outcome: | The proposed framework and metrics evaluate 9 state-of-the-art LLMs on route reversal task. |
Debiasing Word Embeddings with Nonlinear Geometry (2022.coling-1)
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| Challenge: | Existing methods for debiasing word embeddings are limited to individual social categories . however, real-world corpora typically present multiple social categories that may correlate or intersect with each other. |
| Approach: | They propose a method to debias word embeddings using nonlinear geometry of individual biases. |
| Outcome: | Empirical results show that the proposed method mitigates biases associated with individual social categories and treats each category in isolation. |
When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Models (2025.emnlp-main)
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| Challenge: | Large Audio-Language Models (LALMs) are augmented with the ability to perceive audio, but their reliability when faced with conflicting inputs remains largely unexplored. |
| Approach: | They examine how LALMs prioritize information when presented with inconsistent audio-text pairs. |
| Outcome: | The proposed models display a significant bias toward textual input when presented with inconsistent audio-text pairs. |
Watch Every Step! LLM Agent Learning via Iterative Step-level Process Refinement (2024.emnlp-main)
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| Challenge: | Recent approaches to enhance agent performance focus on outcome rewards, which may lead to errors or suboptimal actions due to the absence of process supervision signals. |
| Approach: | They propose a step-level framework that provides detailed step-by-step guidance to enhance agent training by using Monte Carlo methods. |
| Outcome: | The proposed framework outperforms strong baselines on three tasks and shows that it is effective in augmenting efficiency and its applicability to diverse models. |
Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion (2024.acl-long)
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| Challenge: | Existing methods to generate correct code completions in private repositories are insufficiently relevant. |
| Approach: | They propose a dataflow-guided retrieval augmentation approach for repository-level code completion . they parses a private repository into code entities and establishes their relations through an extended dataflow analysis . |
| Outcome: | The proposed method improves code exact match and identifier F1-score by 3.43% compared to the state-of-the-art approach. |
OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)
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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu
| Challenge: | Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024). |
| Approach: | They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers. |
| Outcome: | OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. |
InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration (2024.findings-emnlp)
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| Challenge: | Large Language Models have exceptional capabilities in open generation, yet they encounter difficulties with tasks that require intensive knowledge. |
| Approach: | They propose a framework that integrates unknown knowledge into LLMs without overlap . they propose integrating domain-specific knowledge graphs into Llms to reduce knowledge forgetting . |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in integrating new knowledge into LLMs. |
LLM can Achieve Self-Regulation via Hyperparameter Aware Generation (2024.findings-acl)
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Siyin Wang, Shimin Li, Tianxiang Sun, Jinlan Fu, Qinyuan Cheng, Jiasheng Ye, Junjie Ye, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing decoding strategies and hyperparameters may not be optimal for each sample. |
| Approach: | They propose a model that auto-regulates decoding strategies and hyperparameters . this approach eliminates the need for extensive manual tuning, they argue . |
| Outcome: | The proposed model eliminates the need for extensive manual tuning, offering a more autonomous, self-regulate model behavior. |
Phonotomizer: A Compact, Unsupervised, Online Training Approach to Real-Time, Multilingual Phonetic Segmentation (2025.acl-long)
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| Challenge: | Existing approaches to phonetic segmentation are hierarchical and end-to-end . many mistakes in final output stem from subtle segmenter perturbations . |
| Approach: | They propose a phonetic segmentation system that trains on raw sound files alone . it can modulate computational exactness and reduce acoustic model size, they argue . |
| Outcome: | The proposed method reduces the size of the acoustic model and training epochs. |
AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction (2025.naacl-srw)
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| Challenge: | Existing in-context learning methods for relation extraction often overlook entity relationships . Existing methods for RE prioritize language similarity over structural similarity . |
| Approach: | They propose an AMR-enhanced retrieval-based ICL method for relation extraction . their method retrieves in-context examples based on semantic structure similarity . |
| Outcome: | The proposed method outperforms baselines on four English RE datasets and in the more demanding unsupervised setting. |
G-Tuning: Improving Generalization of Pre-trained Language Models with Generative Adversarial Network (2023.findings-acl)
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| Challenge: | Empirical evaluations on the GLUE benchmark demonstrate that fine-tuning can enhance the generalization performance of pre-trained language models (PLMs) in downstream tasks. |
| Approach: | They propose a fine-tuning framework that transforms the latent representation of pre-trained language models from a universal space to a target space and integrates a generative adversarial network into the fine-untun process. |
| Outcome: | Empirical evaluations on the GLUE benchmark and two additional demanding scenarios show that the proposed framework can improve the generalization performance of pre-trained language models (PLMs) in downstream tasks. |
Investigating Neurons and Heads in Transformer-based LLMs for Typographical Errors (2025.emnlp-main)
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| Challenge: | Existing studies have focused on surface-level display of performance degradation due to typos. |
| Approach: | They propose a method to identify typo neurons and typo heads that work actively when inputs contain typos. |
| Outcome: | The proposed method identifies typo neurons and typo heads that work actively when inputs contain typos. |
Overcoming Catastrophic Forgetting in Massively Multilingual Continual Learning (2023.findings-acl)
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Genta Winata, Lingjue Xie, Karthik Radhakrishnan, Shijie Wu, Xisen Jin, Pengxiang Cheng, Mayank Kulkarni, Daniel Preotiuc-Pietro
| Challenge: | Existing methods to handle catastrophic forgetting fail to retain knowledge learnt in the past when sudden shifts occur in training data distributions. |
| Approach: | They propose a learning rate scheduling method that preserves new information without strongly overwriting past knowledge. |
| Outcome: | The proposed method preserves new information without overwriting past knowledge in a multilingual continuous learning framework. |
On the Correspondence between Compositionality and Imitation in Emergent Neural Communication (2023.findings-acl)
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| Challenge: | a study examining compositionality and imitation learning in a Lewis game demonstrates that it is difficult to imitate compositional languages. |
| Approach: | They explore the link between compositionality and imitation in a Lewis game . they show that the learning algorithm used to imitate is crucial . |
| Outcome: | The proposed model improves compositionality and imitation in a Lewis game . the study shows that compositional languages are easier to imitate . |
Towards Multi-modal Sarcasm Detection via Disentangled Multi-grained Multi-modal Distilling (2024.lrec-main)
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| Challenge: | Existing approaches to sarcasm detection focus on textual and intra-modal incongruity . mainstream approaches process input of each modality in a holistic manner, resulting in redundant and unrefined information. |
| Approach: | They propose a framework for multi-modal sarcasm detection that disentangles modality representations into latent spaces and conducts multi-grained knowledge distilling. |
| Outcome: | The proposed framework overpowers existing methods on a common benchmark. |
Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator (2022.findings-emnlp)
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| Challenge: | Task-oriented dialogue systems are drawing more attention in recent studies . current evaluation methods use annotated utterances in multi-turn dialogue sessions . |
| Approach: | They propose an interactive evaluation framework for task-oriented dialogue (TOD) they build a goal-oriented user simulator based on pre-trained models and use it to generate dialogues . |
| Outcome: | The proposed evaluation framework achieves 98% inform and success rates in the multi-woz dataset and the proposed scores measure the response quality besides the inform and successful rates. |
Question Directed Graph Attention Network for Numerical Reasoning over Text (2020.emnlp-main)
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Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu
| Challenge: | Numerical reasoning requires both natural language understanding and arithmetic computation. |
| Approach: | They propose a graph representation for the context of the passage and question needed for numerical reasoning. |
| Outcome: | The proposed model achieves remarkable results in benchmark datasets such as DROP. |
Language-aware Interlingua for Multilingual Neural Machine Translation (2020.acl-main)
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| Challenge: | Existing multilingual neural machine translation models fail to capture diversity and specificity of different languages, resulting in inferior performance against individual models that are sufficiently trained. |
| Approach: | They propose to integrate a language-aware interlingua into an Encoder-Decoder architecture to learn a semantic representation from the semantic spaces of different languages while allowing for language-specific specialization of a particular language pair. |
| Outcome: | The proposed model achieves remarkable improvements over state-of-the-art multilingual NMT models and produces comparable performance with strong individual models. |
Mitigating Spurious Correlations via Counterfactual Contrastive Learning (2025.findings-emnlp)
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Fengxiang Cheng, Chuan Zhou, Xiang Li, Alina Leidinger, Haoxuan Li, Mingming Gong, Fenrong Liu, Robert Van Rooij
| Challenge: | Existing methods to distinguish causally related words from spurious correlations are limited by the number of causally correlated words in a sentence. |
| Approach: | They propose to use probabilistic probability of necessity and probability of sufficiency to identify causal relationships rather than spurious correlations between words and class labels. |
| Outcome: | The proposed method is based on a contrastive learning approach name CPNS and is validated on public datasets. |
Acquiring Clean Language Models from Backdoor Poisoned Datasets by Downscaling Frequency Space (2024.acl-long)
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| Challenge: | Prior work attempts to mitigate backdoor learning during training LMs on poisoned datasets . backdoor attack poisons a small portion of training data by implanting specific text patterns . |
| Approach: | They propose a multi-scale low-rank adaptive model that prioritizes learning of clean mapping . they propose radial scalings to reduce the success rate of diverse backdoor attacks . |
| Outcome: | The proposed model outperforms baselines significantly in the frequency space . it reduces the success rate of diverse backdoor attacks to below 15% across datasets . |
PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models (2025.acl-long)
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| Challenge: | Recent large language models (LLMs) have achieved significant performance in complex reasoning tasks such as mathematics and code generation. |
| Approach: | They propose a process-level benchmark specifically designed to assess the fine-grained error detection capabilities of PRMs. |
| Outcome: | The proposed model measures the accuracy, soundness, and sensitivity of 25 models across open-source and closed-source large language models. |
Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention (P18-2)
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| Challenge: | Existing methods for event detection use sentence-level contextual information. |
| Approach: | They propose a document embedding enhanced bi-RNN model to detect events in sentences . they use hierarchical and supervised attention based RNN to learn document embeds . |
| Outcome: | The proposed model compares with state-of-the-art models on a ACE-2005 dataset. |
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models (2024.acl-demos)
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Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. |
| Approach: | They propose an easy-to-use knowledge editing framework for Large Language Models that allows users to easily edit updated knowledge and adjust undesired behavior while minimizing the impact on unrelated inputs. |
| Outcome: | The proposed framework surpasses traditional fine-tuning in terms of reliability and generalization. |
Learning to Control the Specificity in Neural Response Generation (P18-1)
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| Challenge: | Existing generative conversational models tend to favor general and trivial responses which appear frequently. |
| Approach: | They propose a controlled response generation mechanism to handle different utterance-response relationships in terms of specificity. |
| Outcome: | The proposed model outperforms state-of-the-art models under automatic and human evaluations. |
Large-Scale Corpus Construction and Retrieval-Augmented Generation for Ancient Chinese Poetry: New Method and Data Insights (2025.findings-naacl)
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| Challenge: | Ancient Chinese poetry presents unique challenges for Large Language Models due to data scarcity and limited ability of general LLMs when dealing with ACP. |
| Approach: | They propose a specialized Retrieval-Augmented Generation framework to improve LLMs' performance . they use 1.1 million ancient poems and 990K related texts to address hallucination issues . |
| Outcome: | The proposed framework improves performance of LLMs in ancient Chinese poetry domain from 49.2% to 89.0%. |
From Objectives to Questions: A Planning-based Framework for Educational Mathematical Question Generation (2025.acl-long)
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| Challenge: | Traditional generation methods focus primarily on textual quality, but they fail to meet complex, multifaceted educational requirements. |
| Approach: | They propose a method for automatic generating high-quality mathematical problems that align with educational objectives using a dataset of 16k mathematical questions with multi-dimensional educational objectives. |
| Outcome: | The proposed method improves generating high-quality mathematical questions that meet multi-dimensional educational objectives. |
Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers (2023.emnlp-main)
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| Challenge: | valence variability was significantly lower in the control group compared to ADHD, depression, bipolar disorder, MDD, PTSD, and OCD but not PPD. |
| Approach: | They study the relationship between tweet emotion dynamics and mental health disorders by using a user-disclosed diagnosis. |
| Outcome: | The results show that the measures varied by the user's self-disclosed diagnosis. |
ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation (2024.findings-emnlp)
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| Challenge: | Existing approaches to radiology report generation lack inter-report consistency, exhibiting biases towards common patterns and susceptibility to lesion variants. |
| Approach: | They propose a method which improves the inter-report consistency of radiology report generation by extracting lesions from input images and examining their characteristics. |
| Outcome: | The proposed system captures similarities in semantically equivalent lesions and can be used to generate reports for two semantically identical cases. |
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks (2021.emnlp-main)
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| Challenge: | Existing methods for rumor detection are limited to the strict relation of user responses or oversimplify the conversation structure. |
| Approach: | They propose a method that reinforces interaction of user opinions while reducing negative impact imposed by irrelevant posts. |
| Outcome: | The proposed method improves performance on three Twitter datasets and can detect rumors at early stages. |
The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse (2024.findings-acl)
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| Challenge: | Even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks. |
| Approach: | They propose to use perplexity as a surrogate metric to determine whether an edited model's performance is affected by a single edit. |
| Outcome: | The proposed method shows that even a single edit can cause model collapse, manifesting as significant performance degradation in various benchmark tasks. |
Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog (P19-1)
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| Challenge: | Existing models for visual dialog infer the answer through multiple reasoning steps. |
| Approach: | They propose a model for visual dialog that uses multi-step reasoning to answer questions about an image. |
| Outcome: | The proposed model achieves a new state-of-the-art of 64.47% on the VisDial v1.0 dataset . |
Position Really Matters: Towards a Holistic Approach for Prompt Tuning (2025.findings-naacl)
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| Challenge: | Prompt tuning is effective in extracting knowledge from foundation models, but its effectiveness is uncertain. |
| Approach: | They propose a parametric prompt tuning strategy that dynamically determines different factors of prompts based on specific tasks or instances. |
| Outcome: | The proposed approach improves performance across a wide range of tasks including NLP, vision recognition, and vision-language tasks. |
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. |
Perceive the Passage of Time: A Systematic Evaluation of Large Language Model in Temporal Relativity (2025.coling-main)
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| Challenge: | Temporal perception is crucial for Large Language Models to understand the world. |
| Approach: | They propose a temporal-relative ability benchmark to evaluate LLMs' temporal perception . they conduct extensive experiments on popular LLM GPT-4 scenarios . |
| Outcome: | The proposed benchmarks show a significant performance gap between LLMs and humans in temporal-relative capability. |
StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models (2024.findings-acl)
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Zhicheng Guo, Sijie Cheng, Hao Wang, Shihao Liang, Yujia Qin, Peng Li, Zhiyuan Liu, Maosong Sun, Yang Liu
| Challenge: | Large Language Models (LLMs) have witnessed remarkable advancements in recent years, prompting the exploration of tool learning. |
| Approach: | They propose a virtual API server and stable evaluation system to assess the stability of large-scale real-time APIs. |
| Outcome: | The proposed benchmarks demonstrate the stability of the proposed system and its caching system. |
A Bayesian Optimization Approach to Machine Translation Reranking (2025.naacl-long)
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| Challenge: | reranking is a method of improving prediction quality but can add computational cost. |
| Approach: | They propose to score a list of prediction candidates with an external scoring model and return the highest-scoring candidate. |
| Outcome: | The proposed method achieves the same CometKiwi score using 70 evaluations on average compared to scoring a subset of 180 candidates. |
Where Am I From? Identifying Origin of LLM-generated Content (2024.emnlp-main)
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| Challenge: | Generative models have produced high-quality content, but they pose security risks . a new framework for deep learning systems enables the tracing of AI-generated content back to its source . |
| Approach: | They propose a digital forensics framework that embeds a secret watermark into the generated output and a "depth watermark" this watermark strengthens the link between content and generator, enabling accurate tracing while maintaining the quality of the generated content. |
| Outcome: | The proposed framework ensures accurate tracing while maintaining quality of generated content. |
Investigating Hallucinations in Simultaneous Machine Translation: Knowledge Distillation Solution and Components Analysis (2025.naacl-long)
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| Challenge: | Existing methods to mitigate hallucinations in siMT generate fluency but unfaithful translation. |
| Approach: | They propose a method that utilizes the OMT model to mitigate hallucinations in SiMT. |
| Outcome: | The proposed method reduces hallucinations and improves the SiMT performance. |
Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge (2022.findings-emnlp)
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| Challenge: | Existing open-domain question answering methods rely on the retriever to gather all evidence in isolation, but our approach uses an intermediary module to perform a chain of reasoning over the retrieved set. |
| Approach: | They propose a new open-domain question answering framework that integrates an intermediary module into the current retriever-reader pipeline and integrates it into the model. |
| Outcome: | The proposed framework outperforms the state-of-the-art on two OTT-QA datasets with an exact match score of 47.3 (45% relative gain). |
Tricking Retrievers with Influential Tokens: An Efficient Black-Box Corpus Poisoning Attack (2025.naacl-long)
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| Challenge: | Existing methods for crafting adversarial passages are slow and computationally expensive, requiring either access to retriever’s gradients or large computational resources. |
| Approach: | They propose a method that leverages two key characteristics of retrievers: insensitivity to token order and bias towards influential tokens to generate effective adversarial passages. |
| Outcome: | The proposed method achieves superior efficiency and scalability compared to existing methods while maintaining comparable or better attack success rates across multiple datasets. |
Machine Unlearning of Pre-trained Large Language Models (2024.acl-long)
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| Challenge: | Using curated datasets, we establish a robust benchmark for unlearning performance, demonstrating that these methods are over 105 times more computationally efficient than retraining. |
| Approach: | They propose a framework for machine unlearning in pre-trained LLMs and integrate gradient ascent with gradient descent on in-distribution data to achieve robustness. |
| Outcome: | The proposed framework is over 105 times more efficient than retraining on in-distribution data and provides detailed guidelines for efficient hyperparameter tuning in the unlearning process. |
Open-ended Commonsense Reasoning with Unrestricted Answer Candidates (2023.findings-emnlp)
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Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao
| Challenge: | Current approaches to commonsense reasoning are limited due to limited answer scope. |
| Approach: | They propose to solve a commonsense question without a pre-defined answer scope . they leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base . |
| Outcome: | The proposed method achieves better performance on two commonsense benchmark datasets. |
Improving Referring Ability for Biomedical Language Models (2024.findings-emnlp)
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| Challenge: | Existing auto-regressive large language models (LLMs) are primarily trained using documents from general domains. |
| Approach: | They propose to use citation network to improve the pre-training of auto-regressive large language models (LLMs) in the biomedical domain. |
| Outcome: | Empirical studies show that the proposed method improves both the intra-sample and inter-sammple referring abilities of auto-regressive large language models in the biomedical domain. |
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. |
VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning (2024.acl-demos)
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Cheng Niu, Yang Guan, Yuanhao Wu, Juno Zhu, Juntong Song, Randy Zhong, Kaihua Zhu, Siliang Xu, Shizhe Diao, Tong Zhang
| Challenge: | generative artificial intelligence has exacerbated the challenge of distinguishing genuine news from fabricated stories. |
| Approach: | They propose a retrieval-augmented system that extracts the core facts from a given piece of news and conducts an internet-wide search to identify corroborating or conflicting reports. |
| Outcome: | The proposed system has demonstrated state-of-the-art accuracy in the realm of fake news detection. |
LLMDet: A Third Party Large Language Models Generated Text Detection Tool (2023.findings-emnlp)
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| Challenge: | Existing detection tools rely on access to LLMs and can only distinguish between machine-generated and human-authored text. |
| Approach: | They propose a model-specific, secure, efficient, and extendable detection tool that can source text from specific LLMs. |
| Outcome: | The proposed tool can source text from specific LLMs, such as GPT-2, OPT, LLaMA, and others. |
Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs (2025.findings-acl)
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Runchu Tian, Yanghao Li, Yuepeng Fu, Siyang Deng, Qinyu Luo, Cheng Qian, Shuo Wang, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Huadong Wang, Xiaojiang Liu
| Challenge: | Positional biases in large language models hinder their ability to process long inputs. |
| Approach: | They propose a benchmark to assess positional bias in large language models involving multiple pieces of relevant information. |
| Outcome: | The proposed benchmark assesses the performance of long-context language models by examining their models with different input lengths and tasks. |
Retrieval-Augmented Generation with Hierarchical Knowledge (2025.findings-emnlp)
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Haoyu Huang, Yongfeng Huang, Yang Junjie, Zhenyu Pan, Yongqiang Chen, Kaili Ma, Hongzhi Chen, James Cheng
| Challenge: | Existing RAG methods do not utilize hierarchical knowledge in human cognition, which limits the capabilities of RAG systems. |
| Approach: | They propose a graph-based approach that utilizes hierarchical knowledge to enhance the semantic understanding and structure capturing capabilities of RAG systems. |
| Outcome: | The proposed approach achieves significant performance improvements over the state-of-the-art methods. |
A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking (2020.acl-main)
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| Challenge: | Existing methods for dialogue state tracking ignore the slot imbalance problem and treat all slots indiscriminately, which limits the learning of hard slots. |
| Approach: | They propose to employ a contextual hierarchical attention network to enhance the DST by learning contextual representations. |
| Outcome: | The proposed approach achieves 52.68% and 58.55% joint accuracy on multiWOZ 2.0 and MultiWOZ 2.1 datasets and significantly improves performance (+1.24% and +5.98%) |
LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review (2025.acl-demo)
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| Challenge: | Large language models (LLMs) are capable of generating inaccurate discharge summary content or fabricating information without valid sources. |
| Approach: | They propose a tool for empowering LLMs with Logic-Controlled Discharge Summary generation. |
| Outcome: | The proposed tool identifies the writing logic of discharge summaries and integrates it with EMRs to generate silver discharge summararies. |
LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval (2026.acl-long)
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He Cheng, Yifu Wu, Saksham Khatwani, Maya Kruse, Dmitriy Dligach, Timothy A. Miller, Majid Afshar, Yanjun Gao
| Challenge: | Existing systems struggle to balance efficiency, scalability, and interpretability. |
| Approach: | They propose a hardware-aligned framework that enables scalable and interpretable k-hop retrieval on large KGs. |
| Outcome: | The proposed framework scales to billion-edge graphs without loss of retrieval fidelity. |
Learning Global Controller in Latent Space for Parameter-Efficient Fine-Tuning (2024.acl-long)
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Zeqi Tan, Yongliang Shen, Xiaoxia Cheng, Chang Zong, Wenqi Zhang, Jian Shao, Weiming Lu, Yueting Zhuang
| Challenge: | Large language models (LLMs) have shown remarkable performance, but their training costs are exorbitant. |
| Approach: | They propose a parameter-efficient method for exploring optimal solutions within latent space by using latent units to extract input representations from LLMs. |
| Outcome: | The proposed method improves performance on a range of natural language processing tasks. |
Zero-Shot Spoken Language Understanding via Large Language Models: A Preliminary Study (2024.lrec-main)
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| Challenge: | Recent advances in large language models (LLMs) have shown promising results in zero-shot settings, which motivates us to explore prompt-based methods. |
| Approach: | They propose a two-stage framework which transforms the SLU task into a question-answering problem by directly prompting LLMs. |
| Outcome: | The proposed framework can be built by directly prompting LLMs to understand user needs without training data. |
On Controlling Fallback Responses for Grounded Dialogue Generation (2022.findings-acl)
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| Challenge: | Existing knowledge grounded dialogue frameworks assume that the user intention is always answerable. |
| Approach: | They propose a framework that automatically generates a control token with the generator to bias the succeeding response towards informativeness for answerable contexts and fallback for unanswerable context. |
| Outcome: | The proposed framework incorporates fallback responses to respond to unanswerable contexts in an informative manner while retaining informativeness for answerable context. |
Language Model Based Text-to-Audio Generation: Anti-Causally Aligned Collaborative Residual Transformers (2025.emnlp-main)
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| Challenge: | Autoregressive language models excel in text-to-audio generation, but lag behind diffusion models by a non-trivial margin. |
| Approach: | They propose a framework that integrates multiple isolated transformers with causal conditioning and anti-causal alignment via reinforcement learning. |
| Outcome: | The proposed framework outperforms existing LM-based and diffusion-based systems in audio synthesis. |
Query-based Instance Discrimination Network for Relational Triple Extraction (2022.emnlp-main)
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| Challenge: | Recent approaches to extract relational triples from open domain texts suffer from error propagation, relation redundancy and lack of high-level connections. |
| Approach: | They propose a query-based approach to construct instance-level representations for relational triples . they use query embeddings and token embeddables to extract all types of triples in one step . |
| Outcome: | The proposed method achieves state-of-the-art on five widely used benchmarks. |
Rethinking the Multimodal Correlation of Multimodal Sequential Learning via Generalizable Attentional Results Alignment (2024.acl-long)
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| Challenge: | Existing studies have focused on the alignment of multimodal sequential learning using transformers. |
| Approach: | They propose a constrained scheme to align the multiple attentional results from both local and global perspectives. |
| Outcome: | The proposed scheme could align the multiple attentional results from both local and global perspectives, making the information capture more efficient. |
RecMem: Recurrence-based Memory Consolidation for Efficient and Effective Long-Running LLM Agents (2026.findings-acl)
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| Challenge: | Existing memory systems invoke LLMs to extract episodic and semantic memory, and this leads to substantial token consumption. |
| Approach: | They propose a method that stores incoming interactions in a subconscious memory layer and encodes them using lightweight embedding models for retrieval. |
| Outcome: | Experiments show that RecMem reduces the memory construction token cost of three SOTA memory systems by up to 87% while exceeding their accuracy. |
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method (2024.naacl-long)
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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin
| Challenge: | Recent literature reveals that Large Language Models (LLMs) hallucinate intermittently, which impedes their reliability for further utilization. |
| Approach: | They propose a self-detection method to detect which questions an LLM does not know by combining the two components to identify whether the model generates a non-factual response to the question. |
| Outcome: | The proposed method can detect which questions an LLM does not know across factoid question-answering, arithmetic reasoning, and commonsense reasoning tasks. |
Confidence is not Timeless: Modeling Temporal Validity for Rule-based Temporal Knowledge Graph Forecasting (2024.acl-long)
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| Challenge: | Existing literature on temporal knowledge Graph Forecasting lacks in-depth investigation into how confidence evolves with time. |
| Approach: | They propose a framework to model the temporal validity of rules for Temporal Knowledge Graph Forecasting (TKGF) they propose rule-adversarial negative sampling and time-aware negative sampling strategies to facilitate TempValid learning. |
| Outcome: | The proposed framework outperforms state-of-the-art (SOTA) rule-based methods on six TKGF datasets. |
A Symmetric Local Search Network for Emotion-Cause Pair Extraction (2020.coling-main)
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| Challenge: | Existing methods for Emotion-cause pair extraction are not effective because of their lack of annotation. |
| Approach: | They propose a new task which aims at extracting the potential clause pairs of emotions and corresponding causes in a document. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods on the ECPE corpus. |
SeaPO: Strategic Error Amplification for Robust Preference Optimization of Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods for preference optimization of large language models use pairs of positive and negative samples, but the quality of positive samples may become similar during training, complicating preference learning. |
| Approach: | SeaPO introduces error types commonly occurring in large language models to improve preference learning. |
| Outcome: | SeaPO introduces error types into model Preference Optimization to improve model performance . negative samples are more erroneous than positive samples, and preference-based training mitigates errors . |
Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents (2026.findings-acl)
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Miao Su, Yucan Guo, Zhongni Hou, Long Bai, Zixuan Li, Yufei Zhang, Guojun Yin, Wei Lin, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing methods focus on point-wise memory, losing durative information that captures persistent states and evolving patterns. |
| Approach: | They propose a memory framework that models semantic time for point-wise memory and supports the construction and utilization of durative memory. |
| Outcome: | Experiments on LongMemEval and LoCoMo show that the proposed method outperforms existing methods and achieves up to 12.2% improvement in accuracy. |
AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching (2023.acl-industry)
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| Challenge: | In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise. |
| Approach: | They present a transformer-based pairwise sentence classification model that analyzes call transcripts to determine which calls are most relevant for coaching purposes. |
| Outcome: | The proposed model can determine which calls are most relevant for coaching purposes based on quality assurance queries/questions asked by managers or supervisors . |
UltraIF: Advancing Instruction Following from the Wild (2025.emnlp-main)
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| Challenge: | a lack of transparency has resulted in a gap between research community and leading companies . large language models have demonstrated remarkable capabilities in following complex instructions . |
| Approach: | They propose a method to build large language models that can follow complex instructions with open-source data. |
| Outcome: | The proposed approach can synergize complex instructions and filter responses with evaluation questions. |
Optimizing Length Compression in Large Reasoning Models (2026.acl-long)
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| Challenge: | Large Reasoning Models suffer from producing unnecessary and verbose reasoning chains. |
| Approach: | They propose a post-training method that uses a Length Reward and a Compress Reward to remove the invalid portion of the thinking process. |
| Outcome: | The proposed method reduces sequence length by 50% with only a marginal (2%) drop in accuracy. |
AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents (2025.emnlp-industry)
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| Challenge: | Existing knowledge base is time-consuming and deters the adoption of conversational AI systems in contact centers. |
| Approach: | They propose a system that extracts knowledge in the form of question-answer (QA) pairs from historical customeragent conversations to automatically build a knowledge base. |
| Outcome: | The proposed system outperforms larger closed-source LLMs on internal data and achieves above 90% accuracy in answering informationseeking questions. |
Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chains (2025.acl-long)
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| Challenge: | Existing vision-language models struggle to disentangle information scattered across complex visual inputs, leading to performance degradation. |
| Approach: | They propose a focus-centric visual chain paradigm that enhances VLMs’ perception, comprehension, and reasoning abilities in multi-image scenarios. |
| Outcome: | The proposed approach achieves average performance gains of 3.16% and 2.24% across two distinct model architectures, without compromising the general vision-language capabilities. |
MCLF: A Multi-grained Contrastive Learning Framework for ASR-robust Spoken Language Understanding (2023.findings-emnlp)
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| Challenge: | Trending ASR-robust SLU systems have seen impressive improvements through global contrastive learning, but they can easily lead to severe semantic changes. |
| Approach: | They propose a two-stage multi-grained contrastive learning framework to improve ASR robustness . they first adapt pre-trained language models to downstream SLU datasets and then fine-tune it on the corresponding dataset. |
| Outcome: | The proposed framework improves on four datasets and four BERT-like backbone models. |
R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning (2025.findings-emnlp)
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Yuan Li, Qi Luo, Xiaonan Li, Bufan Li, Qinyuan Cheng, Bo Wang, Yining Zheng, Yuxin Wang, Zhangyue Yin, Xipeng Qiu
| Challenge: | RAG systems that integrate external knowledge with Large Language Models often become bottlenecks due to their limited parameters compared to LLMs and their inability to perform step-by-step reasoning. |
| Approach: | They propose a model that integrates external knowledge with Large Language Models to enhance factual correctness and mitigate hallucination. |
| Outcome: | The proposed model outperforms baselines and can transfer well to different retrievers. |
DecisionFlow: Advancing Large Language Model as Principled Decision Maker (2025.findings-emnlp)
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| Challenge: | Current language models lack the structured deliberation needed for high-stakes tasks such as healthcare and finance. |
| Approach: | They propose a decision-making framework that guides models to reason over structured representations of actions, attributes, and constraints. |
| Outcome: | The proposed framework achieves up to 30% accuracy gains over strong prompting baselines and enhances alignment in outcomes. |
VocalNet: Speech LLMs with Multi-Token Prediction for Faster and High-Quality Generation (2025.emnlp-main)
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| Challenge: | Experimental results show VocalNet outperforms existing open-source speech LLMs despite limited training data. |
| Approach: | They propose a scalable and model-agnostic training framework and a novel multi-token prediction paradigm for speech generation. |
| Outcome: | The proposed model outperforms open-source speech LLMs while outperforming existing open-sourced models. |
HumT DumT: Measuring and controlling human-like language in LLMs (2025.acl-long)
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| Challenge: | Human-like language might improve user experience, but might also lead to deception, overreliance, and stereotyping. |
| Approach: | They introduce HumT and SocioT, metrics for human-like tone in LLM outputs . HumT measures human-type tone and other dimensions of social perceptions in text data . |
| Outcome: | The proposed method reduces human-like tone while preserving model performance. |
Integrating Deep Event-Level and Script-Level Information for Script Event Prediction (2021.emnlp-main)
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| Challenge: | Existing studies only consider a single event sequence corresponding to one common protagonist. |
| Approach: | They propose a Transformer-based model which integrates deep event-level and script-level information for script event prediction. |
| Outcome: | The proposed model is superior to existing models on the New York Times corpus . it utilizes rich information in the text to obtain more comprehensive representations . |
Uplift-RAG: Uplift-Driven Knowledge Preference Alignment for Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Existing efforts to estimate document utility rely on downstream generation performance, which conflates the influence of external documents with the intrinsic knowledge of the LLM. |
| Approach: | They propose an uplift-based definition of document utility that quantifies each document’s marginal benefit over the LLM’s internal knowledge. |
| Outcome: | The proposed framework improves the performance of the LLM by incorporating external retrieved documents into the model. |
Enabling Agents to Communicate Entirely in Latent Space (2026.acl-long)
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Zhuoyun Du, Runze Wang, Huiyu Bai, Zouying Cao, Xiaoyong Zhu, Yu Cheng, Bo Zheng, Wei Chen, Haochao Ying
| Challenge: | Natural language is the de facto communication medium for LLM-based agents, but it presents a fundamental constraint . natural language downsampling limits the depth and nuance of information that can be transmitted . et al.: inter-agent latent space communication is a promising paradigm for solving complex tasks . |
| Approach: | They propose a paradigm that leverages the last hidden states of an LLM as a representation of its thought for direct communication. |
| Outcome: | The proposed paradigm outperforms fine-tuned chain-of-thought prompting and single-agent baselines even across heterogeneous models. |
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models (2023.acl-long)
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| Challenge: | Conventional fine-tuning works through updating all of the parameters in the pre-trained model, but as the size of pre-train models grows, it can be time-consuming and computationally expensive. |
| Approach: | They propose a framework for resource- and parameter-efficient fine-tuning by leveraging the sparsity prior in both weight updates and the final model weights. |
| Outcome: | The proposed framework saves 25% inference FLOPs while maintaining competitive downstream performance. |
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction (2020.findings-emnlp)
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Ranran Haoran Zhang, Qianying Liu, Aysa Xuemo Fan, Heng Ji, Daojian Zeng, Fei Cheng, Daisuke Kawahara, Sadao Kurohashi
| Challenge: | Existing methods to extract relation triplets from plain text introduce exposure bias . prior work has focused on pipeline methods that ignore intrinsic interactions between subtasks and propagate classification errors through the tasks. |
| Approach: | They propose a model that reduces the decoding length to three within a triplet and removes the order among triplets. |
| Outcome: | The proposed model overfits to both datasets while showing better generalization. |
When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions (2021.findings-emnlp)
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| Challenge: | Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question contextualized by a long scenario description. |
| Approach: | They propose a model where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. |
| Outcome: | The proposed model outperforms strong baselines on multiple-choice questions in three datasets. |
Bridging Information-Theoretic and Geometric Compression in Language Models (2023.emnlp-main)
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| Challenge: | Current language models (LMs) encode training data into finitely many variables that allow generalization to infinitely many grammatical utterances. |
| Approach: | They propose to analyze compression in language models from geometric and information-theoretic perspectives. |
| Outcome: | The proposed model can model human language in a relatively small dimension. |
JaMIE: A Pipeline Japanese Medical Information Extraction System with Novel Relation Annotation (2022.lrec-1)
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| Challenge: | Existing tools for analyzing medical information extraction are limited . empirical results show satisfactory analyzing performance . |
| Approach: | They propose a relation annotation schema for investigating medical and temporal relations in Japanese medical reports. |
| Outcome: | The proposed schema shows that it performs better than existing models and is feasible for high-accuracy applications. |
MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking (2024.lrec-main)
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| Challenge: | Existing zero-shot dialogue state tracking models suffer from domain transferring and partial prediction problems. |
| Approach: | They propose to establish connections between similar slots in different domains to improve model transfer performance in unseen domains. |
| Outcome: | Empirical results show that the proposed model achieves the goal accuracy of 57.13% on MultiWOZ2.1 and 55.4. |
CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents (2026.acl-long)
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| Challenge: | Existing evaluations of Large Language Models (LLMs) focus on task completion, but neglect a crucial capability: the ability to devise and adjust cost-optimal plans in response to changing environments. |
| Approach: | They propose a scalable, cost-centric benchmark to evaluate agents’ economic reasoning and replanning abilities. |
| Outcome: | Evaluating leading open-sourced and proprietary models on CostBench reveals a substantial gap in cost-aware planning . |
MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries (2024.findings-acl)
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| Challenge: | Medical decisions directly impact individuals’ health and well-being. |
| Approach: | They propose to use a dataset to jointly extract and classify medical decisions within clinical notes. |
| Outcome: | The proposed dataset contains clinical notes of eleven different phenotypes (diseases) annotated by ten types of medical decisions. |
LLM-FP4: 4-Bit Floating-Point Quantized Transformers (2023.emnlp-main)
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| Challenge: | Existing quantization solutions are integer-based and struggle with bit widths below 8 bits. |
| Approach: | They propose a method for quantizing weights and activations in large language models down to 4-bit floating-point values in a post-training manner. |
| Outcome: | The proposed method outperforms existing methods on common sense zero-shot reasoning tasks by 12.7 points. |
Clues Before Answers: Generation-Enhanced Multiple-Choice QA (2022.naacl-main)
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| Challenge: | Multiple-choice question answering (MCQA) uses text-to-text framework . but, there is an under-utilization of the decoder and knowledge that can be decoded . |
| Approach: | They propose a generative multiple-choice question answering model which generates a clue from the question and leverages it to enhance a reader for MCQA. |
| Outcome: | The proposed model outperforms text-to-text models on multiple MCQA datasets. |
Unlocking Continual Learning Abilities in Language Models (2024.findings-emnlp)
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| Challenge: | Existing approaches to learning models (LMs) incorporate old task data or task-wise inductive bias into LMs, but old data and accurate task information are often unavailable or costly to collect. |
| Approach: | They propose a rehearsal-free method that updates model parameters with large magnitudes . they found that the L1-normalized magnitude distribution is different when different task data is used . |
| Outcome: | The proposed method improves accuracy and performance on four CL benchmarks. |
Class-Incremental Few-Shot Event Detection (2024.lrec-main)
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| Challenge: | Existing methods to deal with new class of events with only a few labeled instances are challenging . old knowledge forgetting and new class overfitting are two problems in this task. |
| Approach: | They propose a task called class-incremental few-shot event detection to solve old knowledge forgetting and new class overfitting problems. |
| Outcome: | The proposed method reduces old knowledge forgetting and new class overfitting problems on two benchmark datasets. |
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)
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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li
| Challenge: | Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications. |
| Approach: | They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) . |
| Outcome: | The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents. |
Correcting Language Model Bias for Text Classification in True Zero-Shot Learning (2024.lrec-main)
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| Challenge: | Experimental results show that pre-trained language models outperform standard prompt learning in zero-shot settings. |
| Approach: | They propose a pipeline for annotating and filtering examples from unlabeled examples . they propose 'model bias validation' method that utilizes unlabed examples as validation set . |
| Outcome: | The proposed approach outperforms standard prompt learning on six text classification tasks. |
Effective Synthetic Data and Test-Time Adaptation for OCR Correction (2024.emnlp-main)
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| Challenge: | Recent research has framed the post-OCR task as a Seq2Seq Neural Machine Translation (NMT) task. |
| Approach: | They propose a method for constructing post-OCR synthetic data with different noise levels using weak supervision. |
| Outcome: | The proposed method reduces CER by 68.67% without relying on manual annotations. |
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization (2022.findings-acl)
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| Challenge: | Existing methods for few-shot text classification are limited by labeled data. |
| Approach: | They propose to use consistency regularization to improve few-shot text classification by generating pseudo-labels from weakly-augmented and strongly-augmented views. |
| Outcome: | The proposed method achieves competitive performance with 16 labeled examples with prompt and verbalizer. |
Self-Detoxifying Language Models via Toxification Reversal (2023.emnlp-main)
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| Challenge: | Existing methods to generate toxic content in pretrained language models are resource-intensive and require additional components. |
| Approach: | They propose a method that enables the PLM itself to achieve "self-detoxification" they identify the toxification direction from the normal generation process to the one prompted with the negative prefix and then steer the generation to the reverse direction by manipulating the information movement within the attention layers. |
| Outcome: | The proposed method can achieve comparable performance with state-of-the-art methods without any fine-tuning or extra components. |
Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering (2025.acl-long)
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Shuzheng Si, Haozhe Zhao, Gang Chen, Cheng Gao, Yuzhuo Bai, Zhitong Wang, Kaikai An, Kangyang Luo, Chen Qian, Fanchao Qi, Baobao Chang, Maosong Sun
| Challenge: | Existing studies show that training LLMs on data containing unfamiliar knowledge during instruction tuning can encourage hallucinations. |
| Approach: | They propose a framework that measures how familiar the LLM is with instruction data and introduce an expert-aligned reward model to ensure the quality of selected samples. |
| Outcome: | The proposed framework reduces hallucinations while maintaining a competitive ability to follow instructions. |
GPT-RE: In-context Learning for Relation Extraction using Large Language Models (2023.emnlp-main)
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| Challenge: | Existing approaches to in-context learning (ICL) are lacking in relation extraction (RE) . emergence of large language models (LLMs) such as GPT-3 represents a significant advancement in natural language processing. |
| Approach: | They propose to incorporate task-aware representations into demonstration retrieval and enrich the demonstrations with gold label-induced reasoning logic. |
| Outcome: | The proposed model achieves SOTA and competitive performances on the Semeval and SciERC datasets. |
LoRaDA: Low-Rank Direct Attention Adaptation for Efficient LLM Fine-tuning (2025.findings-emnlp)
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| Challenge: | Recent advances in parameter-efficient fine-tuning techniques allow for adjustments to only a minor fraction of the parameters of language models. |
| Approach: | They propose a low-rank direct attention adapted method for efficient LLM fine-tuning . they propose LMAM, which can bring negative attention to self-attention modules . |
| Outcome: | The proposed method outperforms the full fine-tuning method by 2.1% on GLUE benchmark. |
Inductive Reasoning on Few-Shot Knowledge Graphs with Task-Aware Language Models (2025.findings-emnlp)
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| Challenge: | Current knowledge graph reasoning methods rely on extensive structural information to perform in these few-shot scenarios. |
| Approach: | They propose a task-aware language model that activates the language model’s in-context learning ability for structured KG tasks. |
| Outcome: | The proposed method achieves state-of-the-art in few-shot scenarios while reducing the inference time required by previous methods. |
Can ChatGPT’s Performance be Improved on Verb Metaphor Detection Tasks? Bootstrapping and Combining Tacit Knowledge (2024.acl-long)
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| Challenge: | Existing methods for supervised metaphor detection are limited by their performance. |
| Approach: | They propose to use ChatGPT to detect most prevalent verb metaphors among metaphors . they use literal collocations of target verbs and subject-object pairs of verbs to detect them . |
| Outcome: | The proposed method achieves the best performance on the unsupervised verb metaphors detection task compared to existing unsupervised methods or direct prediction using ChatGPT. |
Analyzing Modality Robustness in Multimodal Sentiment Analysis (2022.naacl-main)
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| Challenge: | despite its importance, little attention has been paid to improving the robustness of multimodal models. |
| Approach: | They propose simple diagnostic checks for modality robustness in a trained multimodal model . they find MSA models highly sensitive to a single modality, which creates issues . |
| Outcome: | The proposed checks show that models are highly sensitive to a single modality, which creates issues in their robustness. |
M3TQA: Massively Multilingual Multitask Table Question Answering (2026.findings-acl)
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Daixin Shu, Jian Yang, Zhenhe Wu, Xianjie Wu, Xianfu Cheng, Guan Xiangyuan, Yanghai Wang, Pengfei Wu, Tingyang Yang, Hualei Zhu, Wei Zhang, Ge Zhang, Jiaheng Liu, Zhoujun Li
| Challenge: | Existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis. |
| Approach: | They propose a framework for massively multilingual table question answering that includes tables expanded to 97 languages from Chinese and English sources. |
| Outcome: | Experiments on state-of-the-art LLMs show that synthetically generated training data significantly boosts performance, especially for low-resource languages. |
RoBGuard: Enhancing LLMs to Assess Risk of Bias in Clinical Trial Documents (2025.coling-main)
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Changkai Ji, Bowen Zhao, Zhuoyao Wang, Yingwen Wang, Yuejie Zhang, Ying Cheng, Rui Feng, Xiaobo Zhang
| Challenge: | Existing approaches to assess the risk of bias in RCTs focus on manually crafted prompts and a restricted set of simple questions, limiting their accuracy and generalizability. |
| Approach: | They propose a framework for enhancing Large Language Models to assess the risk of bias in RCTs by reformulation, document parsing and multi-expert collaboration. |
| Outcome: | The proposed framework outperforms existing methods on the RoB-Item and RoB domains. |
Large Language Model-Based Event Relation Extraction with Rationales (2025.coling-main)
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| Challenge: | Existing methods for ERE rely on large language models, but they face limitations. |
| Approach: | They propose an LLM-based approach with rationales for the ERE task . LLMERE transforms ERE into a question-and-answer task that may have multiple answers . |
| Outcome: | Experimental results show that LLMERE improves over existing methods. |
Con-ReCall: Detecting Pre-training Data in LLMs via Contrastive Decoding (2025.coling-main)
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| Challenge: | Existing methods analyze training data with member and non-member contexts, overlooking potential insights from both member and not-member. |
| Approach: | They propose a method that leverages asymmetric distributional shifts induced by member and non-member contexts through contrastive decoding to enhance membership inference. |
| Outcome: | The proposed approach outperforms the current state-of-the-art on the WikiMIA benchmark and is robust against various text manipulation techniques. |
Unsupervised Contrast-Consistent Ranking with Language Models (2024.eacl-long)
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| Challenge: | Language models contain ranking-based knowledge and are powerful solvers of in-context ranking tasks. |
| Approach: | They propose to use a model to elicit language models' ranking knowledge without supervision by using a pairwise, pointwise and listwise prompting method. |
| Outcome: | The proposed method is inspired by an unsupervised probing method called Contrast-Consistent Search (CCS). |
MacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent Space (2023.findings-emnlp)
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| Challenge: | Existing approaches to multi-aspect controllable text generation require expensive iteration / searching within the discrete text space during the decoding stage, resulting in a degradation of text quality due to the domain discrepancies between different aspects. |
| Approach: | They propose a framework that estimates compact latent space for multiple aspects and performs efficient Sampling with a fast sampler to eliminate domain discrepancies. |
| Outcome: | The proposed framework outperforms baselines on attribute relevance and textual quality while maintaining a high inference speed. |
Your LLM Agents are Temporally Blind: The Misalignment Between Tool Use Decisions and Human Time Perception (2026.findings-acl)
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Yize Cheng, Arshia Soltani Moakhar, Chenrui Fan, Parsa Hosseini, Kazem Faghih, Zahra Sodagar, Wenxiao Wang, Soheil Feizi
| Challenge: | Large language model agents assume a stationary context, failing to account for real-world time elapsed between messages. |
| Approach: | They construct a dataset of multi-turn user–agent message trajectories across 76 scenarios . they collect human preferences between "calling a tool" and "directly answering" they also examine whether existing models lack human temporal perception . |
| Outcome: | The results show that existing models display poor alignment with human temporal perception . the findings provide insights to foster the development of more time-aware and human-aligned agents. |
SimOAP: Improve Coherence and Consistency in Persona-based Dialogue Generation via Over-sampling and Post-evaluation (2023.acl-long)
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| Challenge: | Existing work on large-scale corpora-based language models is limited and hard to generalize to all types of pre-trained language models. |
| Approach: | They propose a two-stage SimOAP strategy that over-samples and post-evaluates large-scale responses from existing models and selects a good response based on multiple evaluation metrics. |
| Outcome: | The proposed strategy outperforms baseline and automatic evaluation strategies in both automatic and human evaluations. |
ModelingAgent: Bridging LLMs and Mathematical Modeling for Real-World Challenges (2025.findings-emnlp)
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Cheng Qian, Hongyi Du, Hongru Wang, Xiusi Chen, Yuji Zhang, Avirup Sil, ChengXiang Zhai, Kathleen McKeown, Heng Ji
| Challenge: | Existing benchmarks for large language models fail to reflect real-world complexity . existing benchmarks often fail to capture real-life problems . |
| Approach: | They propose a benchmark that features real-world-inspired, open-ended problems from competitions . they propose 'ModelingBench' that supports multiple valid solutions . |
| Outcome: | The proposed framework outperforms baselines and produces well-grounded, creative solutions. |
Adaptive Textual Label Noise Learning based on Pre-trained Models (2023.findings-emnlp)
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| Challenge: | Existing approaches to learning with noisy labels are limited due to the time and labor costs involved. |
| Approach: | They propose an adaptive warm-up and hybrid training frameworks to learn with noisy labels based on pre-trained models. |
| Outcome: | The proposed approach performs comparable or even surpasses state-of-the-art methods in various noise scenarios, including scenarios with the mixture of multiple types of noise. |
AnchorMem: Anchored Facts with Associative Contexts for Building Memory in Large Language Models (2026.findings-acl)
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| Challenge: | Existing memory systems rely on summarization to preserve contextual nuances and obscuring key retrieval features. |
| Approach: | They propose a method that decouples the retrieval unit from the generation context. |
| Outcome: | The proposed method outperforms baseline models on the LoCoMo benchmark. |
Projecting Out the Malice: A Global Subspace Approach to LLM Detoxification (2026.acl-long)
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Zenghao Duan, Zhiyi Yin, Zhichao Shi, Liang Pang, Shaoling Jing, Zihe Huang, Jiayi Wu, Yu Yan, Jingcheng Deng, Huawei Shen, Xueqi Cheng
| Challenge: | Large language models (LLMs) exhibit exceptional performance but pose inherent risks of generating toxic content. |
| Approach: | They propose a method that removes toxic subspaces from FFN parameters . they propose to use a lightweight method to eliminate toxic subespaces . |
| Outcome: | The proposed method achieves SOTA detoxification while preserving general capabilities without large-scale retraining. |
Efficient Beam Search for Large Language Models Using Trie-Based Decoding (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) face memorybound performance bottlenecks due to their high memory requirements. |
| Approach: | They propose a trie-based parallel decoding method that shares a single KV cache across beams with common prefixes to dramatically reduce memory usage and enables efficient decoding. |
| Outcome: | The proposed method significantly reduces memory usage and enables efficient decoding without compromising generation quality. |
Faster Minimum Bayes Risk Decoding with Confidence-based Pruning (2023.emnlp-main)
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| Challenge: | Minimum Bayes risk (MBR) decoding is a decision rule for conditional sequence generation tasks. |
| Approach: | They propose an algorithm which grows the number of samples used to estimate utility . it prunes hypotheses that are unlikely to have the highest utility based on bootstrap sampling . |
| Outcome: | The proposed method outperforms beam search in conditional language generation and neural machine translation tasks while being statistically indistinguishable from other proposed methods. |
ControlSpeech: Towards Simultaneous and Independent Zero-shot Speaker Cloning and Zero-shot Language Style Control (2025.acl-long)
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Shengpeng Ji, Qian Chen, Wen Wang, Jialong Zuo, Minghui Fang, Ziyue Jiang, Hai Huang, Zehan Wang, Xize Cheng, Siqi Zheng, Zhou Zhao
| Challenge: | Prior zero-shot TTS models only mimic the speaker’s voice without further control and adjustment capabilities while prior controllable TTS systems cannot perform speaker-specific voice generation. |
| Approach: | They propose a style control module that captures codec representations corresponding to timbre, content, and style in a discrete decoupling codec space. |
| Outcome: | The proposed system can fully clone the speaker's voice and perform speech-specific adjustment and control functions. |
Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning (2026.acl-long)
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| Challenge: | Chain-of-Thought reasoning has driven recent gains of large language models (LLMs) on reasoning-intensive tasks by externalizing intermediate steps. |
| Approach: | They propose a training-free framework that adaptively determines when to stop reasoning to mitigate overthinking. |
| Outcome: | The proposed framework reduces token usage by 20-55% while maintaining or improving accuracy compared to standard CoT prompting. |
Experiential Co-Learning of Software-Developing Agents (2024.acl-long)
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Chen Qian, Yufan Dang, Jiahao Li, Wei Liu, Zihao Xie, YiFei Wang, Weize Chen, Cheng Yang, Xin Cong, Xiaoyin Che, Zhiyuan Liu, Maosong Sun
| Challenge: | Recent advances in large language models (LLMs) have brought significant changes to various domains, especially through autonomous agents. |
| Approach: | They propose a framework that lets agents learn shortcuts from their past tasks and use them for future task execution. |
| Outcome: | The proposed framework enables agents to tackle unseen software-developing tasks more effectively. |
ConvTrans: Transforming Web Search Sessions for Conversational Dense Retrieval (2022.emnlp-main)
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| Challenge: | Recent studies show that conversational dense retrieval is a promising technique for realizing conversational search, but its implementation is severely hindered by the lack of data. |
| Approach: | They propose a method that transforms easily-accessible web search sessions into conversational search sessions to alleviate the data scarcity problem. |
| Outcome: | The proposed method can transform easily-accessible web search sessions into conversational search sessions. |
Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning (2022.emnlp-main)
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Xingwei He, Yeyun Gong, A-Long Jin, Weizhen Qi, Hang Zhang, Jian Jiao, Bartuer Zhou, Biao Cheng, Sm Yiu, Nan Duan
| Challenge: | Existing work on commonsense generation requires models to have relational reasoning and compositional generalization capabilities. |
| Approach: | They propose a metric distillation rule to distill knowledge from a standard metric to a ranker and transfer it to re-ranking a retriever. |
| Outcome: | The proposed method surpasses the previous SOTA. |
Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition (2025.acl-long)
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Kehua Feng, Keyan Ding, Tan Hongzhi, Kede Ma, Zhihua Wang, Shuangquan Guo, Cheng Yuzhou, Ge Sun, Guozhou Zheng, Qiang Zhang, Huajun Chen
| Challenge: | Existing methods for evaluation of large language models are inefficient and inefficient due to inaccuracy of standard metrics in human perception of text quality and inefficiency in sampling informative test examples. |
| Approach: | They propose a sample-efficient human evaluation method for large language models based on the principle of MAximum Discrepancy (MAD) competition. |
| Outcome: | The proposed method achieves the “golden” ranking of LLMs with a minimum set of input instructions, which in turn reveal their relative strengths and weaknesses. |
UniTranSeR: A Unified Transformer Semantic Representation Framework for Multimodal Task-Oriented Dialog System (2022.acl-long)
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| Challenge: | Existing studies on multimodal task-oriented dialog systems follow the pipeline to learn intra-modal features separately and then conduct simple feature concatenation or attention-based feature fusion to generate responses. |
| Approach: | They propose a Unified Transformer Semantic Representation framework with feature alignment and intention reasoning for multimodal dialog systems that embed multimodal features into a unified Transformer semantic space to prompt inter-modal interactions. |
| Outcome: | The proposed framework significantly outperforms state-of-the-art approaches on the representative MMD dataset. |
GraDaSE: Graph-Based Dataset Search with Examples (2025.emnlp-main)
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| Challenge: | Existing methods address ad hoc dataset search, but dataset search presents in diverse and complex forms. |
| Approach: | They propose a graph-based approach to retrieve relevant datasets from textual queries . they identify provenance-based and topic-based relationships to construct a diagram . |
| Outcome: | The proposed approach outperforms strong baselines on two test collections. |
RAR2: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrieval (2025.findings-emnlp)
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| Challenge: | Existing methods focus on refining queries without modeling the reasoning process, limiting their ability to retrieve and integrate clinically relevant knowledge. |
| Approach: | They propose a joint learning framework that improves Reasoning-Augmented Retrieval and Retri-Agmented Reasoning. |
| Outcome: | The proposed model outperforms RAG baselines on biomedical question answering datasets. |
SHARE: An SLM-based Hierarchical Action CorREction Assistant for Text-to-SQL (2025.acl-long)
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| Challenge: | Existing approaches to self-correct text-to-SQL fail to demonstrate underlying reasoning path . authors propose **SHARE**, a self-revolution assistant for text-based error correction . |
| Approach: | They propose a "SHARE" assistant that enables LLMs to perform more precise error localization and efficient correction. |
| Outcome: | The proposed assistant performs more precise error localization and efficient correction for monolithic SQL queries. |
Multimodal Instruction Tuning with Conditional Mixture of LoRA (2024.acl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) have demonstrated proficiency in diverse tasks across different domains. |
| Approach: | They propose a method that integrates multimodal instruction tuning with Conditional Mixture-of-LoRA. |
| Outcome: | Experimental results show that MixLoRA outperforms LoRA with the same or higher ranks . MLLMs have demonstrated remarkable proficiency in diverse tasks across domains . |
Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models (2025.acl-long)
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| Challenge: | Existing methods to fine-tune Large Language Models without human annotations are lacking in the field of natural language training. |
| Approach: | They propose an environment-guided neural-symbolic self-training framework to overcome two main challenges: the scarcity of symbolic data and the limited proficiency of LLMs in processing symbolic language. |
| Outcome: | The proposed framework overcomes two main challenges: the scarcity of symbolic data, and the limited proficiency of LLMs in processing symbolic language. |
Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding (2024.acl-long)
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| Challenge: | Conversational dense retrieval models lack interpretability, hindering intuitive understanding of model behaviors . a major limitation of conversational dense search is their lack of interpretability . |
| Approach: | They propose to transform opaque session embeddings into explicit interpretable text . they propose to incorporate external interpretable query rewrites into the transformation process . |
| Outcome: | The proposed approach yields more interpretable text and preserves original retrieval performance over baselines. |
EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation. |
| Approach: | They propose a synthetic data generation framework that leverages models’ established English-to-x (en2x) capabilities by extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy. |
| Outcome: | The proposed framework achieves significant improvement across 72 x2x directions while generalizing to enhance en2x performance. |
ATAP: Automatic Template-Augmented Commonsense Knowledge Graph Completion via Pre-Trained Language Models (2024.emnlp-main)
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| Challenge: | Commosense knowledge graphs (CKGC) are powerful representations of real-world commonsense knowledge. |
| Approach: | They propose a framework that uses automatically generated prompt templates combined with pre-trained language models to improve CKGC performance. |
| Outcome: | The proposed framework mitigates the long-tail problem and improves CKGC performance on a large dataset. |
Chain-of-Thought Compression Should Not Be Blind: V-Skip for Efficient Multimodal Reasoning via Dual-Path Anchoring (2026.acl-long)
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| Challenge: | Existing efforts to mitigate this via token compression fail due to its autoregressive nature . linguistically redundant tokens are erroneously pruned, leading to hallucinations . |
| Approach: | They propose a method that reformulates token pruning as a Visual-Anchored Information Bottleneck (VA-IB) optimization problem. |
| Outcome: | Experiments on Qwen2-VL and Llama-3.2 families show that the proposed model achieves a speedup with negligible accuracy loss. |
Thinking beyond the anthropomorphic paradigm benefits LLM research (2026.acl-long)
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| Challenge: | anthropomorphism is an automatic and unconscious response that occurs even in advanced technical expertise. |
| Approach: | They argue that anthropomorphism is an automatic and unconscious response . they identify and examine five assumptions that shape research across the LLM development lifecycle . |
| Outcome: | The proposed framework challenges assumptions that shape research across the LLM development lifecycle and offers promising directions for LLMs. |
MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting (2023.acl-short)
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| Challenge: | Recent studies have focused on using a single external tool to solve a problem with large language models and have not addressed different problems together. |
| Approach: | They propose a framework that leverages chain-of-thought prompting to incorporate multiple external tools into the reasoning process. |
| Outcome: | The proposed framework outperforms baselines and achieves state-of-the-art performance on a task that requires both numerical reasoning and domain-specific knowledge. |
Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement (D18-1)
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| Challenge: | Automatic Chinese poetry generation is one of the first attempts towards computer writing. |
| Approach: | They propose a model which requires no supervised style labeling to generate stylistic poems . they incorporate mutual information, a concept in information theory, into modeling . |
| Outcome: | The proposed model generates stylistic poems without losing fluency and coherency . it is based on mutual information, a concept in information theory . |
LongSafety: Evaluating Long-Context Safety of Large Language Models (2025.acl-long)
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Yida Lu, Jiale Cheng, Zhexin Zhang, Shiyao Cui, Cunxiang Wang, Xiaotao Gu, Yuxiao Dong, Jie Tang, Hongning Wang, Minlie Huang
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating long sequences. |
| Approach: | They propose a benchmark to evaluate LLM safety in open-ended long-context tasks . they find that relevant context and extended input sequences can exacerbate safety risks . |
| Outcome: | The proposed benchmark identifies significant safety vulnerabilities in 16 LLMs . strong safety performance in short-context scenarios does not correlate with safety in long-contact tasks . |
CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards (2025.emnlp-main)
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| Challenge: | Existing approaches to role-playing language models rely on prompt engineering or supervised fine-tuning to emulate character behaviors but neglect the underlying cognitive mechanisms driving these behaviors. |
| Approach: | They propose a novel RPLA adopting a cognize-then-respond reasoning paradigm that leverages dual cognition for more contextually grounded and psychologically coherent responses. |
| Outcome: | The proposed RPLA outperforms baselines and generalizes effectively across diverse role-playing tasks. |
HalluLens: LLM Hallucination Benchmark (2025.acl-long)
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Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, Pascale Fung
| Challenge: | Large language models (LLMs) generate responses that deviate from user input or training data, a phenomenon known as "hallucination" . |
| Approach: | They propose a hallucination benchmark HalluLens that includes both extrinsic and intrinsic evaluation tasks to distinguish between extrindic and intrinsic hallucines. |
| Outcome: | The proposed framework disentangles LLM hallucination from "factuality" and distinguishes between extrinsic and intrinsic hallucines to promote consistency and facilitate research. |
Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation (2026.findings-acl)
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Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi, Zheming Yuan, Xuxin Cheng, Cao Liu, Ke Zeng, Fengying Xie, Zhiguo Jiang, Haopeng Zhang
| Challenge: | Large vision–language models suffer from object-existence hallucinations when multi-step deliberation decouples from visual evidence. |
| Approach: | They propose a framework that allocates visual computation by uncertainty . they propose highlighting retains global context, while selective zoom-in performs local verification. |
| Outcome: | The proposed framework reduces the complexity of multimodal reasoning by minimizing the operator trade-off. |
Will This Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora (2020.findings-emnlp)
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| Challenge: | Existing research on knowledge transfer focuses on documents as unit of analysis and follow their transfer into practice for a specific scientific domain. |
| Approach: | They analyze scientific concepts from corpora and use them to predict knowledge transfer . they find that only a small proportion of these ideas will be used in inventions . |
| Outcome: | The proposed model predicts the use of scientific concepts in clinical trials and inventions. |
Robustness Testing of Language Understanding in Task-Oriented Dialog (2021.acl-long)
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Jiexi Liu, Ryuichi Takanobu, Jiaxin Wen, Dazhen Wan, Hongguang Li, Weiran Nie, Cheng Li, Wei Peng, Minlie Huang
| Challenge: | a lack of systematic studies on the robustness of language understanding models in task-oriented dialog systems is limiting . authors propose a model-agnostic toolkit LAUG to approximate natural language perturbations . |
| Approach: | They propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness of language understanding models in task-oriented dialog systems. |
| Outcome: | The proposed toolkit reveals critical robustness issues in state-of-the-art models. |
Visual Named Entity Linking: A New Dataset and A Baseline (2022.findings-emnlp)
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| Challenge: | Existing tasks in Visual Entity Linking (VEL) rely on textual data to complement multi-modal linking or only link objects with general entities. |
| Approach: | They propose a task to link regions of images with corresponding entities in Knowledge Bases . they propose three sub-tasks, based on a human-annotated visual person dataset . |
| Outcome: | The proposed task is based on a human-annotated visual person linking dataset . the proposed sub-tasks are validated on the WIKIPerson dataset based upon the proposed methods . |
mReFinED: An Efficient End-to-End Multilingual Entity Linking System (2023.findings-emnlp)
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| Challenge: | Existing work assumed that entity mentions were given and skipped the entity mention detection step due to a lack of high-quality multilingual training corpora. |
| Approach: | They propose a bootstrapping mention detection framework that enhances the quality of training corpora. |
| Outcome: | The proposed framework outperforms existing work in the end-to-end MEL task while being 44 times faster. |
Learning Musical Representations for Music Performance Question Answering (2024.findings-emnlp)
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| Challenge: | Existing methods for audio-visual learning fail to consider the distinctive characteristics of instruments and music. |
| Approach: | They propose to integrate multimodal interactions within the context of music data and annotate and release rhythmic and music sources in the current music datasets to enable the model to learn music characteristics. |
| Outcome: | The proposed model can learn music characteristics from the current music datasets and align its predictions with the temporal dimension. |
Improving Distantly-Supervised Relation Extraction with Joint Label Embedding (D19-1)
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| Challenge: | Existing methods for relation extraction treat labels as independent and meaningless one-hot vectors, which cause a loss of potential label information for selecting valid instances. |
| Approach: | They propose a multi-layer attention-based model to improve relation extraction with joint label embedding by gating integration and using the embeddable entities as an atten- tion. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods in relation extraction with joint label embedding. |
Related Knowledge Perturbation Matters: Rethinking Multiple Pieces of Knowledge Editing in Same-Subject (2025.naacl-short)
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Zenghao Duan, Wenbin Duan, Zhiyi Yin, Yinghan Shen, Shaoling Jing, Jie Zhang, Huawei Shen, Xueqi Cheng
| Challenge: | Existing knowledge editing methods struggle when tasked with editing multiple related knowledge pieces for the same subject. |
| Approach: | They propose a benchmark to assess the effectiveness of knowledge editing methods . they use same-subject edits to ensure comprehensive updates to entity-centric knowledge . |
| Outcome: | The proposed method over-relys on subject information, neglecting other critical factors, resulting in reduced editing effectiveness. |
Don’t Eclipse Your Arts Due to Small Discrepancies: Boundary Repositioning with a Pointer Network for Aspect Extraction (2020.acl-main)
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| Challenge: | Existing aspect extraction methods suffer from boundary errors, but they hurt performance severely. |
| Approach: | They propose to use a pointer network to reposition the boundaries of extracted aspects . they conduct experiments on laptop and restaurant benchmark datasets . |
| Outcome: | The proposed method outperforms state-of-the-art methods on benchmark datasets . it achieves substantial improvements over baseline and outperformed existing methods . |
A Survey on Detection of LLMs-Generated Content (2024.findings-emnlp)
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Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Petzold, William Yang Wang, Wei Cheng
| Challenge: | Recent advances in large language models have led to an increase in synthetic content generation . the ability to detect LLMs-generated content has become of paramount importance . |
| Approach: | They propose to provide a detailed overview of existing detection strategies and benchmarks, scrutinizing their differences and advocating for more adaptable and robust models to enhance detection accuracy. |
| Outcome: | The proposed model will be able to detect human-written content in real time. |
APo-VAE: Text Generation in Hyperbolic Space (2021.naacl-main)
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| Challenge: | Existing models that learn embeddings only in Euclidean vector space do not account for such structural property of language. |
| Approach: | They propose a Poincare Variational Autoencoder to capture latent hierarchies in hyperbolic space . they propose enabling adversarial learning procedures to empower robust model training . |
| Outcome: | The proposed model outperforms existing models in a hyperbolic latent space . it captures latent language hierarchies in hyperbolical space and is robust to training . |
OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs (2024.findings-emnlp)
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Jintian Zhang, Cheng Peng, Mengshu Sun, Xiang Chen, Lei Liang, Zhiqiang Zhang, Jun Zhou, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in Large Language Models (LLMs) have significantly enhanced the generative capabilities for various NLP tasks, but they still suffer from hallucinations due to their exclusive reliance on parametric knowledge. |
| Approach: | They propose a framework that integrates retrieval tokens generated autoregressively into a single LLM to handle both tasks simultaneously in a unified forward pass. |
| Outcome: | The proposed framework bridges the traditionally separate training approaches for generation and retrieval by incorporating retrieval tokens generated autoregressively. |
Sci-LoRA: Mixture of Scientific LoRAs for Cross-Domain Lay Paraphrasing (2025.findings-acl)
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| Challenge: | Lay paraphrasing aims to make scientific information accessible to non-experts . existing studies focus on a single domain, such as biomedicine . |
| Approach: | a new lay paraphrasing model leverages a mixture of LoRAs fine-tuned on multiple scientific domains. |
| Outcome: | a new model outperforms state-of-the-art large language models in lay paraphrasing . the model can adjust the impact of different domains without explicit labels . |
Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs (2024.emnlp-main)
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| Challenge: | Existing studies focus on case-to-case retrieval using lengthy queries, which does not match real-world scenarios. |
| Approach: | They propose a method to construct query-candidate pairs and build the largest LCR dataset to date, LEAD. |
| Outcome: | Experimental results show that the method can provide ample training signals for LCR models. |
Beyond Binary Preferences: Semi-Online Label-Free GRACE-KTO with Group-Wise Adaptive Calibration for High-Quality Long-Text Generation (2025.findings-emnlp)
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| Challenge: | Generating high-quality long-text remains challenging for Large Language Models (LLMs), as conventional supervised fine-tuning fails to ensure overall quality due to its teacher-forcing nature. |
| Approach: | They propose a semi-online framework that transforms KTO’s binary signals into dynamically calibrated intra-group rewards. |
| Outcome: | The proposed framework transforms binary signals into dynamically calibrated intra-group rewards. |
Merely Judging Metaphor is Not Enough: Research on Reasonable Metaphor Detection (2024.findings-emnlp)
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| Challenge: | Current metaphor detection tasks only provide labels without interpreting how to understand them. |
| Approach: | They propose to improve the current metaphor detection task by using mainstream Large Language Models. |
| Outcome: | The proposed model is based on the original sentence, target word, and usage . the model is then evaluated using manual evaluation . |
Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication (2020.coling-main)
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| Challenge: | Existing methods for visual storytelling construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. |
| Approach: | They propose a topic description task to detect the global semantic context of an image stream and a story is then constructed with the guidance of the topic description. |
| Outcome: | The proposed framework can generate stories with higher quality compared to state-of-the-art methods on a VIST dataset. |
TLUE: A Tibetan Language Understanding Evaluation Benchmark (2025.emnlp-main)
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Fan Gao, Cheng Huang, Yutong Liu, Nyima Tashi, Xiangxiang Wang, Thupten Tsering, Ban Ma-bao, Renzeng Duojie, Gadeng Luosang, Rinchen Dongrub, Dorje Tashi, Xiao Feng Cd, Yongbin Yu, Hao Wang
| Challenge: | Low-resource languages, like Tibetan, remain underrepresented in large language models' evaluations. |
| Approach: | They propose a Tibetan Language Understanding Evaluation Benchmark to assess LLMs' proficiency in Tibetan . they use a multi-task understanding benchmark and a safety benchmark to evaluate models . |
| Outcome: | The proposed benchmark shows that most large language models perform below the random baseline, especially in Tibetan language processing. |
Stochastic Chameleons: Irrelevant Context Hallucinations Reveal Class-Based (Mis)Generalization in LLMs (2025.acl-long)
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| Challenge: | Existing studies have shown that LLMs reproduce training artifacts, exploit spurious correlations, and fail when faced with distribution shifts. |
| Approach: | They examine irrelevant context hallucinations in which models integrate misleading contextual cues into their predictions. |
| Outcome: | The proposed model errors are reflected in the model's internal computations, and they are consistent with previous studies. |
Hanfu-Bench: A Multimodal Benchmark on Cross-Temporal Cultural Understanding and Transcreation (2025.emnlp-main)
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| Challenge: | Existing studies on cultural understanding with vision-language models primarily emphasize geographic diversity, often overlooking the critical temporal dimensions. |
| Approach: | They propose a multimodal vision-language model that examines temporal features and cultural image transcreation. |
| Outcome: | The novel model performs better than non-experts on visual cutural understanding but falls short to human experts on cultural image transcreation task. |
DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router (2026.findings-eacl)
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Minghao Guo, Qingcheng Zeng, Xujiang Zhao, Yanchi Liu, Wenchao Yu, Mengnan Du, Haifeng Chen, Wei Cheng
| Challenge: | Existing RAG methods lack fine-grained control over query and source sides, resulting in noisy retrieval and shallow reasoning. |
| Approach: | They propose an agentic RAG framework that integrates information sieving via LLM-as-a-knowledge-router. |
| Outcome: | Experiments on multi-hop QA tasks across heterogeneous sources demonstrate improved reasoning depth, retrieval precision, and interpretability over conventional approaches. |
Beyond Language: Learning Commonsense from Images for Reasoning (2020.findings-emnlp)
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| Challenge: | Existing commonsense reasoning methods use raw texts to perform data representation and answer prediction tasks. |
| Approach: | They propose a novel approach to learn commonsense from images instead of limited raw texts or costly knowledge bases. |
| Outcome: | The proposed approach outperforms language-based methods on commonsense reasoning problems on two commonsence reasoning problems. |
Inductive Link Prediction in N-ary Knowledge Graphs (2025.coling-main)
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| Challenge: | Existing methods to predict missing elements in NKGs are fixed and therefore cannot be used in real-world situations. |
| Approach: | They propose a task to predict missing elements in unseen facts involving unseent entities and roles in emerging NKGs by embedding unseense entities and role-encoding neural networks. |
| Outcome: | The proposed task outperforms representative models across all datasets. |
GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection (2026.eacl-long)
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| Challenge: | Existing methods focus on layer-selective and data-selectory fine-tuning, but ignore the fact that different data points contribute varying degrees to distinct model layers. |
| Approach: | They propose a method that performs selective fine-tuning at both data and layer dimensions as integral components of a unified optimization strategy. |
| Outcome: | Experiments show that the proposed method outperforms baseline methods in terms of performance and performance. |
Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs (D19-57)
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| Challenge: | In this paper, we describe our approach for the Bacteria Biotopes relation extraction subtask in the BioNLP Shared Task 2019 . |
| Approach: | They propose a novel approach for dependency graph construction based on lexical chains . they then propose 'neuro network' model which uses short-term memories and syntax information . |
| Outcome: | The proposed approach achieves the best F1 (66.3%) in the official evaluation participated by 7 teams. |
EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs (2024.lrec-main)
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| Challenge: | Existing frameworks for leveraging background knowledge of narratives are limited. |
| Approach: | They propose a framework to ground free-texts to eventuality-centric KGs for narrative reasoning . their framework is based on a set of probabilistic probabilistic models that are grounded in the real world . |
| Outcome: | The proposed framework outperforms baseline models while providing interpretable evidence. |
OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows (2026.acl-long)
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Qiushi Sun, Mukai Li, Zhoumianze Liu, Zhihui Xie, Fangzhi Xu, Zhangyue Yin, Kanzhi Cheng, Zehao Li, Zichen Ding, Qi Liu, Zhiyong Wu, Zhuosheng Zhang, Ben Kao, Lingpeng Kong
| Challenge: | Existing methods for detecting unsafe mobile GUI agents are underexplored. |
| Approach: | They propose a mobile agent safety detection framework that integrates a formal verifier and a VLM-based contextual judge to detect system-level violations. |
| Outcome: | The proposed framework achieves 10%–30% improvements over existing approaches across multiple metrics. |
Serial Lifelong Editing via Mixture of Knowledge Experts (2025.acl-long)
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| Challenge: | Existing Lifelong Knowledge Editing methods struggle to overwrite outdated knowledge with the latest one. |
| Approach: | They propose a new Mixture-of-Knowledge-Experts scheme with an ARM . ARM ensures that each update completely overwrites old information with the latest one . Experimental results show that ARM performs favorably against SOTA knowledge editing methods . |
| Outcome: | The proposed scheme overwrites old knowledge with the latest data on a benchmark . it performs favorably against existing knowledge editing methods on the same concept . |
When 20 Agents Fail to Sort: The Distributed Sorting Benchmark for Scalable Multi-Agent Systems (2026.findings-acl)
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| Challenge: | MAS-BENCH isolates coordination under explicit communication constraints . CAMOC significantly improves coordination success and efficiency across backends . |
| Approach: | They propose a distributed-sorting benchmark that isolates coordination under explicit communication constraints. |
| Outcome: | MAS-BENCH improves coordination success and efficiency across backends . CAMOC significantly improves efficiency under shared-state interaction . |
Rotation Control Unlearning: Quantifying and Controlling Continuous Unlearning for LLM with The Cognitive Rotation Space (2026.findings-acl)
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| Challenge: | Existing methods to remove undesirable data from Large Language Models suffer from cumulative catastrophic utility loss under continuous unlearning requests. |
| Approach: | They propose a method that leverages the rotational salience weight of RCU to quantify and control the unlearning degree in the continuous unlearning process. |
| Outcome: | The proposed method achieves SOTA performance without a retained dataset. |
Conversational Semantic Parsing for Dialog State Tracking (2020.emnlp-main)
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Jianpeng Cheng, Devang Agrawal, Héctor Martínez Alonso, Shruti Bhargava, Joris Driesen, Federico Flego, Dain Kaplan, Dimitri Kartsaklis, Lin Li, Dhivya Piraviperumal, Jason D. Williams, Hong Yu, Diarmuid Ó Séaghdha, Anders Johannsen
| Challenge: | Language understanding for task-based dialog systems is often termed "dialog state tracking" (DST) whereas semantic parsing is the task of converting a single-turn utterance to a graphstructured meaning representation, DST is more complex. |
| Approach: | They propose a framework for dialog state tracking that incorporates semantic compositionality, cross-domain knowledge sharing and co-reference. |
| Outcome: | The proposed framework improves on state-of-the-art approaches for dialog state tracking (DST) it incorporates semantic compositionality, cross-domain knowledge sharing and co-reference. |
Fine-Grained Features-based Code Search for Precise Query-Code Matching (2025.coling-main)
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| Challenge: | Existing methods to locate code snippets from databases represent the semantics of code and query by averaging the features of each token and word. |
| Approach: | They propose a fine-grained code search model that consists of a cross-modal encoder, mapping layer and classification layer to capture fine-granular interactions between code and query. |
| Outcome: | The proposed model significantly outperforms existing methods across multiple programming language datasets. |
SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions (2024.emnlp-industry)
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Zhi-Qi Cheng, Yifei Dong, Aike Shi, Wei Liu, Yuzhi Hu, Jason O’Connor, Alexander Hauptmann, Kate Whitefoot
| Challenge: | EV battery supply chain is vulnerable to disruptions caused by natural disasters and geopolitical tensions. |
| Approach: | They propose a system integrating Large Language Models with domain expertise for EV supply chain risk assessment. |
| Outcome: | Evaluated on 12,070 paragraphs from 365 sources (2022-2023), SHIELD outperforms baseline GCNs and LLM+prompt methods in disruption prediction. |
Uni-Dubbing: Zero-Shot Speech Synthesis from Visual Articulation (2024.acl-long)
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Songju Lei, Xize Cheng, Mengjiao Lyu, Jianqiao Hu, Jintao Tan, Runlin Liu, Lingyu Xiong, Tao Jin, Xiandong Li, Zhou Zhao
| Challenge: | Multimodal speech synthesis is a key challenge due to the scarcity of datasets that pair audio with corresponding video. |
| Approach: | They propose a method that incorporates modality alignment during the pre-training phase on multimodal datasets and freezes the video modality extraction component and the encoder module within the pretrained weights. |
| Outcome: | The proposed method achieves a reduced word error rate (WER) of 31.73%, surpassing the previous best of 33.9% with single-modality audio. |
Improving Query Graph Generation for Complex Question Answering over Knowledge Base (2021.emnlp-main)
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| Challenge: | Existing Knowledge-based Question Answering methods use a query graph to find the answer to a question. |
| Approach: | They propose a method that starts with the entire knowledge base and gradually shrinks it to the desired query graph. |
| Outcome: | Experimental results show that the proposed method achieves state-of-the-art performance on ComplexWebQuestion dataset. |
LLM Knows Body Language, Too: Translating Speech Voices into Human Gestures (2024.acl-long)
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| Challenge: | despite advances in the generation of realistic human gestures, the process often includes unintended, meaningless, or non-realistic gestures. |
| Approach: | They propose a framework that leverages large language models to generate human gestures . the primary stage employs a transformer-based auto-encoder network to encode human gesture into discrete symbols . |
| Outcome: | The proposed framework has demonstrated state-of-the-art performance on public TED and TED-Expressive datasets. |
FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter (2025.findings-emnlp)
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| Challenge: | Existing methods for detecting fake news videos fall short due to lack of knowledge to verify the news is real or not. |
| Approach: | They propose a VLM-based framework for detecting fake news on short video platforms . they design four experts tailored to handle each scenario and integrate them into VLM . |
| Outcome: | The proposed framework outperforms current state-of-the-art models on two benchmark datasets. |
Domain Adaptive Text Style Transfer (D19-1)
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| Challenge: | Text style transfer without parallel data is a promising method for learning, but in the scenario where less data is available, it may yield poor performance. |
| Approach: | They propose to leverage available data to learn domain-adaptive text style transfer models . they evaluate two style transfer tasks where only limited non-parallel data is available . |
| Outcome: | The proposed models learn from the source domain to: (i) distinguish stylized information and generic content information; (ii) maximally preserve content information and (iv) adaptively transfer the styles in a domain-aware manner. |
Biomedical Concept Normalization by Leveraging Hypernyms (2021.emnlp-main)
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| Challenge: | Biomedical Concept Normalization (BCN) is widely used in biomedical text processing . despite numerous surface variants of biomedically-defined concepts, it remains challenging and unsolved. |
| Approach: | They propose a framework that uses hypernyms and synonyms to facilitate BCN . they use list-wise training to make use of both hypernies and synonym entities . |
| Outcome: | The proposed framework outperforms the state-of-the-art model on the NCBI dataset. |
Learning to Conceal Risk: Controllable Multi-turn Red Teaming for LLMs in the Financial Domain (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are increasingly deployed in high-stakes domains such as finance where unsafe behavior can lead to serious regulatory risks. |
| Approach: | They propose a black-box multi-turn risk-concealed redteaming framework that progressively conceals surface-level risk while exploiting regulatory-violating behaviors. |
| Outcome: | Experiments on nine widely used LLMs show that the proposed framework achieves 93.19% average attack success rate (ASR) and improves the average ASR to 95.00%. |
Dynamic Evaluation for Oversensitivity in LLMs (2025.findings-emnlp)
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| Challenge: | Existing benchmarks rely on static datasets that degrade over time as models evolve, leading to data contamination and diminished evaluative power. |
| Approach: | They construct a framework that generates model-specific challenging datasets and aggregates them across diverse LLM families. |
| Outcome: | The framework captures emerging defensive patterns and aligns with each model’s unique behavior. |
Improving Domain Generalization for Prompt-Aware Essay Scoring via Disentangled Representation Learning (2023.acl-long)
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| Challenge: | Existing AES models are either prompt-specific or prompt-adaptive and cannot generalize well on “unseen” prompts. |
| Approach: | They propose a prompt-aware neural AES model to extract comprehensive representation for essay scoring, including both prompt-invariant and prompt-specific features. |
| Outcome: | The proposed model extracts comprehensive representation for essay scoring, including both prompt-invariant and prompt-specific features. |
Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction (2021.findings-emnlp)
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| Challenge: | Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. |
| Approach: | They propose a tightly coupled two-stage approach to extract latent user sentiments and item properties from reviews and an Attention-Property-aware Rating Estimator (APRE). |
| Outcome: | Extensive experiments on seven real-world Amazon review datasets show that the proposed approach extracts the latent user sentiments, item properties, and the complicated interactions between the two components. |
Low-Entropy Watermark Detection via Bayes’ Rule Derived Detector (2025.findings-acl)
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| Challenge: | Existing methods for text watermarking ignore strong evidences embedded in low-entropy tokens, causing statistical measures to falsely indicate the absence of a watermark. |
| Approach: | They propose a Bayes' Rule derived watermark Detector which exploits watermark information from every token by leveraging the posterior probability of watermark’s presence. |
| Outcome: | The proposed method achieves 50% and 70% relative improvements over baselines in code generation and math problem-solving tasks. |
R-TeaFor: Regularized Teacher-Forcing for Abstractive Summarization (2022.emnlp-main)
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| Challenge: | Existing studies on teacher-forcing do not consider the pairwise relationship between training data and modified data, which provides more information during training. |
| Approach: | They propose to use teacher-forcing to address exposure bias problem by modifying training data to simulate model-generated results. |
| Outcome: | Empirically, the proposed method outperforms state-of-the-art models and can be generalized to different pre-trained models. |
Instruction Pre-Training: Language Models are Supervised Multitask Learners (2024.emnlp-main)
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| Challenge: | Unsupervised multitask pre-training has been the key to the success of language models (LMs) however, scaling it in the post-training stage trends towards better generalization. |
| Approach: | They propose a framework that augments massive raw corpora with instruction-response pairs to pre-train LMs. |
| Outcome: | The proposed framework augments massive raw corpora with instruction-response pairs to pre-train LMs. |
TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data (2025.acl-long)
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| Challenge: | Existing methods for semantic parsing rely on extensive manually annotated datasets and limited generalization capability to unseen examples. |
| Approach: | They propose a framework that generates high-relevance synthetic data without manual annotation . they generate queries for the queries and use them as demonstrations for in-context learning . |
| Outcome: | The proposed framework outperforms non-fine-tuned methods on KBQA datasets and shows superior sample efficiency, robustness, and generalization capabilities under non-I.I.D. settings. |
Bootstrapped Pre-training with Dynamic Identifier Prediction for Generative Retrieval (2024.findings-acl)
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| Challenge: | Existing methods for document retrieval rely on static document identifiers . experimental results show that generative retrieval is outperforms dense retrieval in document retrievals. |
| Approach: | They propose a bootstrapped pre-training method that dynamically adjusts document identifiers during pre-train to accommodate the continuing memorization of the corpus. |
| Outcome: | The proposed method significantly outperforms existing pre-training generative retrieval baselines and performs well even in zero-shot settings. |
bert2BERT: Towards Reusable Pretrained Language Models (2022.acl-long)
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Cheng Chen, Yichun Yin, Lifeng Shang, Xin Jiang, Yujia Qin, Fengyu Wang, Zhi Wang, Xiao Chen, Zhiyuan Liu, Qun Liu
| Challenge: | Pre-training large language models can be expensive and wasteful. |
| Approach: | They propose a method which can transfer the knowledge of an existing smaller pre-trained model to a large model through parameter initialization and a two-stage learning method to further accelerate the pre-training. |
| Outcome: | The proposed method can transfer the knowledge of an existing smaller pre-trained model to a large model through parameter initialization and significantly improve the pre-training efficiency of the large model. |
A Fully Probabilistic Perspective on Large Language Model Unlearning: Evaluation and Optimization (2025.emnlp-main)
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| Challenge: | Large Language Model Unlearning (LLMU) is a promising way to remove private or sensitive information from large language models. |
| Approach: | They propose a Fully Probabilistic Evaluation framework that incorporates input and output distributions in LLMU evaluation. |
| Outcome: | The proposed framework improves unlearning effectiveness by 50.1% and robustness by 37.2% on Llama-2-7B. |
HYPERTTS: Parameter Efficient Adaptation in Text to Speech Using Hypernetworks (2024.lrec-main)
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| Challenge: | Neural text-to-speech (TTS) systems limited to predefined speaker styles or specific sets of speaker IDs. |
| Approach: | They propose a network that can adapt adapter parameters to new speakers . they compare two domain adaptation settings and find it to be very efficient . |
| Outcome: | The proposed Adapters improve speech synthesis performance on two domains and compare them with baselines. |
CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers? (2025.findings-emnlp)
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Jiefu Ou, William Gantt Walden, Kate Sanders, Zhengping Jiang, Kaiser Sun, Jeffrey Cheng, William Jurayj, Miriam Wanner, Shaobo Liang, Candice Morgan, Seunghoon Han, Weiqi Wang, Chandler May, Hannah Recknor, Daniel Khashabi, Benjamin Van Durme
| Challenge: | CLAIMCHECK is an annotated dataset of NeurIPS 2023 and 2024 submissions and reviews from OpenReview. |
| Approach: | They annotate NeurIPS 2023 and 2024 submissions and reviews for weaknesses and dispute them for fine-grained labels of validity, objectivity, and type of the identified weaknesses. |
| Outcome: | The proposed dataset is richly annotated by ML experts for weaknesses statements in the reviews and the claims that they dispute, as well as fine-grained labels of validity, objectivity, and type of the identified weaknesses. |
Uncertainty Quantification for In-Context Learning of Large Language Models (2024.naacl-long)
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Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen
| Challenge: | Existing studies on in-context learning have focused on quantifying the uncertainty associated with the model's response, but they neglect the complexity of the LLM and the uniqueness of in-constitut learning. |
| Approach: | They propose a method to quantify the uncertainty associated with in-context learning and propose corresponding estimation method to quantify both types of uncertainties. |
| Outcome: | The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion. |
Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States (2025.acl-long)
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| Challenge: | Existing benchmarks assess basic Theory of Mind abilities but neglect temporal evolution of mental states in real-world social contexts. |
| Approach: | They propose a benchmark specifically designed to evaluate Large Language Models' ability to understand and track the temporal progression of mental states across interconnected scenarios. |
| Outcome: | The proposed benchmarks underperform humans by 44.7% and show that they can model the dynamic nature of human mental states better than existing models. |
Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents (2026.acl-long)
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| Challenge: | Existing Large language model agents rely on increasingly long interaction histories, resulting in high computational cost and limited scalability. |
| Approach: | They propose a hierarchical reinforcement learning framework that enables step-level learning by conditioning only on single-step transitions rather than full interaction histories. |
| Outcome: | The proposed framework outperforms baselines on ScienceWorld and ALFWorld benchmarks in terms of performance and generalization while reducing token usage. |
InteGround: On the Evaluation of Verification and Retrieval Planning in Integrative Grounding (2025.findings-emnlp)
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| Challenge: | Existing grounding approaches work well for simple queries, but many real-world information needs require synthesizing multiple pieces of evidence. |
| Approach: | They introduce "integrative grounding" to evaluate the ability to ground large language models in external knowledge sources. |
| Outcome: | The proposed approach is robust to redundant evidence, but rationalizes using internal knowledge when information is incomplete. |
Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference? (2023.emnlp-main)
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| Challenge: | Existing quantisation methods mainly focus on 8-bit LLMs . a lack of scaling offsets in the quantisation process limits the use of LLM inference. |
| Approach: | They propose to use block quantisations to reduce scaling offsets in Large language models . they find that the block quantizations reduce scaling only from an arithmetic perspective . |
| Outcome: | The proposed methods reduce scaling offsets solely from an arithmetic perspective without additional treatments in the computational path. |
SRM-LLM: Semantic Relationship Mining with LLMs for Temporal Knowledge Graph Extrapolation (2025.findings-emnlp)
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| Challenge: | Existing methods for temporal knowledge graph extrapolation neglect the complex semantic relationships between relations when modeling their dynamic evolution. |
| Approach: | They propose a method for extracting semantic relationships to achieve TKG extrapolation . they use large language models to analyze the types of relations in TKGs . |
| Outcome: | The proposed method improves on five TKG datasets and shows performance gains. |
GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level (D19-1)
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| Challenge: | SQA is an emerging application of NLP in the medical, geography, and legal domains. |
| Approach: | They propose a dataset of 1,981 scenarios and 4,110 multiple-choice questions in geography domain at high school level. |
| Outcome: | The proposed dataset consists of 1,981 scenarios and 4,110 multiple-choice questions in the geography domain at high school level. |
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance (2020.acl-main)
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Pengyu Cheng, Martin Renqiang Min, Dinghan Shen, Christopher Malon, Yizhe Zhang, Yitong Li, Lawrence Carin
| Challenge: | Disentangled representation learning (DRL) maps different aspects of data into distinct and independent low-dimensional latent vector spaces. |
| Approach: | They propose a method that manifests disentangled representations of text without supervision on semantics by minimizing the upper bound between style and content. |
| Outcome: | The proposed method improves on conditional text generation and text-style transfer tasks and improves style preservation. |
NeuronBlocks: Building Your NLP DNN Models Like Playing Lego (D19-3)
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| Challenge: | Deep Neural Networks (DNN) have been widely employed in industry to address various natural language processing tasks. |
| Approach: | They propose an NLP toolkit that encapsulates neural network modules as building blocks to construct various DNN models with complex architecture. |
| Outcome: | The proposed toolkit can build, train, and test various DNN models with complex architecture. |
Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows (2026.findings-acl)
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Haoyu Dong, Pengkun Zhang, Yan Gao, Xuanyu Dong, Yilin Cheng, Mingzhe Lu, Adina Yakefu, Shuxin Zheng
| Challenge: | FinWorkBench evaluates real-world enterprise-grade finance and accounting workflows . a human evaluation of GPT 5.1 Pro passes only 38.4% of workflows, a study finds . |
| Approach: | They propose a workflow construction process that combines LLM-assisted mining and expert annotation to build 172 composite workflows. |
| Outcome: | The proposed process combines expert annotation with LLM-assisted mining of workflows from authentic enterprise environments. |
Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation Model (2024.findings-emnlp)
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| Challenge: | Recent advances in text-to-image models have demonstrated remarkable capabilities in image synthesis. |
| Approach: | They analyze the critical role of caption precision and recall in text-to-image model training. |
| Outcome: | The proposed model trains with synthetic captions that show similar behavior to those trained on human-annotated captions. |
On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning (2026.acl-long)
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Changyu Liu, Yiyang Liu, Taowen Wang, Qiao Zhuang, James Chenhao Liang, Wenhao Yang, Renjing Xu, Qifan Wang, Dongfang Liu, Cheng Han
| Challenge: | Existing vision-language-action models are unsuitable for simulated or physical-world deployments . current methods fail when confronted with inherent real-world dynamic variability. |
| Approach: | They propose a test-time reinforcement learning framework that enables on-the-fly policy adaptation during inference. |
| Outcome: | Empirical results show that the proposed framework improves adaptability, stability and task success in dynamic, previously unseen scenarios. |
MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification (2022.coling-1)
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| Challenge: | Existing few-shot text classification methods lack labeled data in many scenarios. |
| Approach: | They propose a meta learning framework that obtains different learning rates for different tasks and neural network layers to enable the meta learner to quickly adapt to new training data. |
| Outcome: | The proposed framework can obtain different learning rates for different tasks and neural network layers so as to enable the meta learner to quickly adapt to new tasks. |
DiQAD: A Benchmark Dataset for Open-domain Dialogue Quality Assessment (2023.findings-emnlp)
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Yukun Zhao, Lingyong Yan, Weiwei Sun, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin
| Challenge: | Existing studies on dialogue quality assessment are uncapable of providing an end-to-end and human-epistemic assessment dataset . open-domain dialogue assessment is complicated and costly, but it can be done by recruiting human evaluators. |
| Approach: | They propose a large-scale dialogue quality assessment dataset for automatically assessing open-domain dialogue quality. |
| Outcome: | The proposed dataset is openly accessible at https://github.com/yukunZhao/Dialogue_quality_evaluation. |
FARSS: Fisher-Optimized Adaptive Low-Rank and Singular-Vector Selection for Knowledge-Preserving Fine-Tuning (2026.findings-acl)
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Renxing Chen, Ziwei Xiang, Peisong Wang, Hongjian Fang, Meng Li, Fanhu Zeng, Yanan Zhu, Peipei Yang, Xu-Yao Zhang, Jian Cheng
| Challenge: | Low-rank adaptation methods for large language models have limitations in preserving world knowledge and limiting updates to preserve world knowledge. |
| Approach: | They propose a Fisher-optimized adaptive low Rank and Singular-VectorSelection framework for knowledge-preserving fine-tuning that allows efficient and task-sensitive updates. |
| Outcome: | The proposed framework outperforms existing methods for knowledge-preserving fine-tuning. |
Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding (2022.findings-emnlp)
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Jiahao Zhu, Daizong Liu, Pan Zhou, Xing Di, Yu Cheng, Song Yang, Wenzheng Xu, Zichuan Xu, Yao Wan, Lichao Sun, Zeyu Xiong
| Challenge: | Existing methods for temporal sentence grounding ignore two crucial issues . 1) Boundary-bias: the video downsampling process may lose these two frames . 2) Reasoning-biases: such incorrect new boundary frames lead to the reasoning bias . |
| Approach: | They propose a siamese sampling mechanism to generate additional contextual frames . they use a reasoning strategy to learn the inter-relationship among these frames a . |
| Outcome: | Extensive experiments demonstrate the effectiveness of a new siamese sampling network on three challenging datasets. |
A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning (2026.acl-long)
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Tianyu Yang, Sihong Wu, Yilun Zhao, Zhenwen Liang, Lisen Dai, Chen Zhao, Minhao Cheng, Arman Cohan, Xiangliang Zhang
| Challenge: | Multimodal mathematical Reasoning (MMR) has attracted increasing attention for its ability to solve mathematical problems involving both textual and visual modalities. |
| Approach: | They review the theoretical frameworks of multimodal reasoning and examine the challenges they face in visual math tasks. |
| Outcome: | The proposed models can solve problems involving both textual and visual modalities. |
Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach (2024.emnlp-industry)
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| Challenge: | Recent LLMs like Gemini-1.5 and GPT-4 show exceptional capabilities to understand long contexts directly. |
| Approach: | They propose a method that routes queries to RAG or LC based on model self-reflection. |
| Outcome: | The proposed method significantly reduces the computation cost while maintaining a comparable performance to RAG. |
Won’t Get Fooled Again: Answering Questions with False Premises (2023.acl-long)
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| Challenge: | Pre-trained language models (PLMs) are often easily deceived by tricky questions such as “How many eyes does the sun have?” . |
| Approach: | They annotate a FalseQA dataset containing 2365 human-written FPQs and find that PLMs are capable of discriminating FPqs by fine-tuning on moderate numbers. |
| Outcome: | The proposed model can discriminate on FPQs by fine-tuning on moderate numbers of examples and generate reasonable explanations for false premise questions. |
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question Answering (2021.emnlp-main)
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| Challenge: | Existing methods address this issue by introducing an auxiliary task such as visual grounding, cycle consistency, or debiasing. |
| Approach: | They propose a data augmentation pipeline to turn “known” knowledge into training examples for VQA. |
| Outcome: | The proposed model can handle multi-modal information and is based on human-annotated examples. |
ODL-TempLLM: Ontology-Guided and Description Logic-Reasoned Temporal Reasoning with LLMs (2026.acl-long)
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| Challenge: | Temporal reasoning is crucial for large language models to understand event concurrency and complex temporal interactions in natural language. |
| Approach: | They propose an ontology-guided and description logic–constrained temporal reasoning paradigm that shifts focus from internal inference to the explicit modeling of temporal structure. |
| Outcome: | The proposed method outperforms state-of-the-art methods by 2.07–31.83 F1 points and 1.00–30.73 EM points, exhibiting strong generalization and highlighting the potential of explicit temporal reasoning. |
IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory (2025.acl-long)
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| Challenge: | Large language models have demonstrated exceptional performance across a wide range of tasks . however, selecting the optimal LLM to respond to a user query often necessitates a delicate balance between performance and cost. |
| Approach: | They propose a multi-LLM routing framework that efficiently routes user queries to the most suitable LLM. |
| Outcome: | The proposed framework outperforms baseline methods in terms of effectiveness and interpretability. |
On Domain-Adaptive Post-Training for Multimodal Large Language Models (2025.findings-emnlp)
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Daixuan Cheng, Shaohan Huang, Ziyu Zhu, Xintong Zhang, Xin Zhao, Zhongzhi Luan, Bo Dai, Zhenliang Zhang
| Challenge: | Adapting general multimodal large language models to specific domains is important for practical applications. |
| Approach: | They investigate domain adaptation of multimodal large language models via post-training . they develop a generate-then-filter pipeline that curates diverse visual instruction tasks . |
| Outcome: | The proposed model outperforms existing models in domain adaptation by combining data from open-source models with training pipelines. |
JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning (2024.acl-demos)
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| Challenge: | Large Language Models (LLMs) face significant memory constraints when fine-tuning large prompt sequences. |
| Approach: | They propose a framework for PEFT-compatible fine-tuning of large language models, leveraging distributed training. |
| Outcome: | The proposed framework improves performance 12x compared to Hugging Face/DeepSpeed implementation with four GPUs while consuming less than half the VRAM per GPU. |
Discourse Self-Attention for Discourse Element Identification in Argumentative Student Essays (2020.emnlp-main)
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| Challenge: | Despite its importance, discourse element identification is challenging due to the ambiguity of sentences . the number of elaboration sentences could be 10 times more than the number edna sentences. |
| Approach: | They propose to use sentence positional encodings to explicitly represent sentence positions and inter-sentence attentions to capture sentence interactions and enhance sentence representation. |
| Outcome: | The proposed model improves on a Chinese and English dataset. |
SeaLLMs - Large Language Models for Southeast Asia (2024.acl-demos)
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Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing
| Challenge: | Existing large language models favor high-resource languages, such as English, at the expense of low-resourced and regional languages. |
| Approach: | They propose a series of language models that specifically focuses on Southeast Asian languages. |
| Outcome: | SeaLLM models outperform ChatGPT-3.5 in non-Latin languages by large margins . linguistic disparity impedes access to state-of-the-art AI technologies for non-English-speaking populations . |
GRASP: Replace Redundant Layers with Adaptive Singular Parameters for Efficient Model Compression (2025.emnlp-main)
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| Challenge: | Recent studies have demonstrated that many layers are functionally redundant in large language models (LLMs), enabling model compression by removing these layers to reduce inference cost. |
| Approach: | They propose a framework that removes redundant layers to reduce inference cost by preserving sensitivity-aware singular values. |
| Outcome: | The proposed framework outperforms existing methods in 90% of the original model under a 20% compression ratio. |
API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access (2024.findings-emnlp)
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| Challenge: | Existing methods for quantifying uncertainty in large language models with black-box API access are limited due to the complex data distributions and inner model mechanism. |
| Approach: | They propose a conformal prediction method that minimizes the size of prediction sets and ensures a statistical guarantee of the user-defined coverage. |
| Outcome: | The proposed method outperforms existing methods on close-ended and open-ended questions. |
Towards Robust Temporal Activity Localization Learning with Noisy Labels (2024.lrec-main)
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Daizong Liu, Xiaoye Qu, Xiang Fang, Jianfeng Dong, Pan Zhou, Guoshun Nan, Keke Tang, Wanlong Fang, Yu Cheng
| Challenge: | Existing methods for temporal activity localization are expensive and difficult to satisfy due to subjective labeling. |
| Approach: | They propose a new TAL setting where a TAL model should be robust to mixed training data with noisy moment boundaries. |
| Outcome: | The proposed method is significantly more robust to noisy training data than existing methods. |
Attention-Seeker: Dynamic Self-Attention Scoring for Unsupervised Keyphrase Extraction (2025.coling-main)
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| Challenge: | Unsupervised keyphrase extraction methods require large amounts of labeled data and are often domainspecific, limiting their practical applicability. |
| Approach: | They propose an unsupervised keyphrase extraction method that leverages self-attention maps from a Large Language Model to estimate the importance of candidate phrases. |
| Outcome: | The proposed method outperforms baseline models on four datasets and is highly efficient on three of four dataset. |
E-ABSA20K: A Dataset and Propose-and-Verify for Aspect-Based Sentiment Analysis in Long E-commerce Reviews (2026.findings-acl)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) is critical for extracting actionable product insights from e-commerce reviews. |
| Approach: | They propose a framework that decomposes ABSA into two stages to extract review-level quadruple reviews from 20K reviews from four product categories. |
| Outcome: | The proposed framework outperforms existing benchmarks and single-stage prompting and competitive ABSA extraction baselines. |
Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement (2026.findings-eacl)
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Wangyang Ying, Yanchi Liu, Xujiang Zhao, Wei Cheng, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen
| Challenge: | Recent advances in large language models (LLMs) show potential for graph extraction, but often yield ill-formed structures or misinterpret logical constructs such as gateways. |
| Approach: | They propose a framework that treats procedural graph extraction as a multi-round reasoning process with structural and logical refinement agents. |
| Outcome: | The proposed framework achieves significant improvements in structural correctness and logical consistency over strong baselines. |
TRANS-ZERO: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have reshaped machine translation, but multilingual MT still relies heavily on parallel data for supervised fine-tuning. |
| Approach: | They propose a framework that leverages only monolingual data and the intrinsic multilingual knowledge of Large Language Models (LLMs). |
| Outcome: | The proposed framework matches models trained on large-scale parallel data and excels in non-English translation directions. |
Unveiling Performance Challenges of Large Language Models in Low-Resource Healthcare: A Demographic Fairness Perspective (2025.coling-main)
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| Challenge: | Existing large language models (LLMs) are not effective in solving real-world healthcare tasks, but they are able to provide demographic information and provide biased health predictions. |
| Approach: | They evaluate state-of-the-art LLMs with three prevalent learning frameworks across six diverse healthcare tasks and find significant challenges in applying LLM to real-world healthcare tasks. |
| Outcome: | The proposed models perform poorly in real-world healthcare tasks and are inconsistent with existing learning frameworks. |
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)
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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang
| Challenge: | Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level. |
| Approach: | They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context. |
| Outcome: | The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set. |
Patient Knowledge Distillation for BERT Model Compression (D19-1)
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| Challenge: | Pre-trained language models such as BERT have proven to be highly effective for natural language processing tasks, but the high demand for computing resources hinders their application in practice. |
| Approach: | They propose to compress an original large model (teacher) into an equally-effective lightweight shallow network (student) Empirically, this translates into improved results on multiple NLP tasks with a significant gain in training efficiency, without sacrificing model accuracy. |
| Outcome: | The proposed model reduces the computational cost of training models using the teacher model into a lightweight shallow network. |
TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization (2026.findings-acl)
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Shichao Ma, Zhiyuan Ma, Ming Yang, Xiaofan Li, Xing Wu, Jintao Du, Yu Cheng, Weiqiang Wang, Qiliang Liu, Zhengyang Zhou, Yang Wang
| Challenge: | Large Language Models (LLMs) can solve complex tasks through iterative information retrieval. |
| Approach: | They propose a turn-level stage-aware policy optimization approach to solve this problem . they introduce a first-occurrence latent reward mechanism to allocate partial rewards . |
| Outcome: | Experiments show that TSPO outperforms state-of-the-art models on Qwen2.5-3B and 7B models. |
MPVStance: Mitigating Hallucinations in Stance Detection with Multi-Perspective Verification (2025.acl-long)
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| Challenge: | despite advances in large language models, challenges persist due to hallucination-models generating inaccurate content. |
| Approach: | They propose a framework that integrates multi-perspective verification with Retrieval-Augmented Generation to address these challenges. |
| Outcome: | The proposed method outperforms existing models on the SemEval-2016 and VAST datasets. |
AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models (2025.acl-long)
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| Challenge: | Existing benchmarks focus on basic abilities using nonverbal methods, such as yes-no and multiple-choice questions. |
| Approach: | They propose a benchmark that provides more nuanced evaluations of alignment capabilities for large Vision-Language Models (VLMs) they use a rule-calibrated evaluator that exceeds GPT-4's evaluation ability and a “alignment score” to assess the robustness and stability of models across diverse prompts. |
| Outcome: | The proposed benchmark covers 13 tasks across three categories and includes both single-turn and multi-turn dialogue scenarios. |
RoCEL: Advancing Table Entity Linking through Distinctive Row and Column Contexts (2024.emnlp-main)
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| Challenge: | Existing methods for table entity linking ignore row and column contexts . existing methods for TEL focus on understanding sequential text contexts, making it difficult to adapt to the row and columns structure of tables. |
| Approach: | They propose to leverage row and column contexts to enhance the semantics of mentions in entity disambiguation. |
| Outcome: | The proposed method outperforms the state-of-the-art (SOTA) baseline by 1.5% on the in-domain dataset and 3.7% on average across three out-of domain datasets. |
PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation (2022.emnlp-industry)
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Lianshang Cai, Linhao Zhang, Dehong Ma, Jun Fan, Daiting Shi, Yi Wu, Zhicong Cheng, Simiu Gu, Dawei Yin
| Challenge: | Pre-trained language models have been a key part of ranking systems . knowledge distillation is widely used to maintain high performance while keeping efficient computations. |
| Approach: | They propose an algorithm to combine knowledge from multi-teachers and label information to achieve competitive performance in offline and online experiments. |
| Outcome: | The proposed method has been deployed in a real-world commercial search system. |
Search-Oriented Conversational Query Editing (2023.findings-acl)
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| Challenge: | Existing CQR models are not learned toward improving the downstream search performance . existing models generate the rewrite token-by-token from scratch . |
| Approach: | They propose a text editing-based CQR model tailored for conversational search . they propose rewrite tokens are selected from the dialogue in a non-autoregressive fashion . |
| Outcome: | The proposed model outperforms state-of-the-art models on three conversational search benchmarks while having low rewriting latency. |
Adaptive Token Biaser: Knowledge Editing via Biasing Key Entities (2024.findings-emnlp)
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| Challenge: | Existing methods to update parametric knowledge of large language models (LLMs) are outdated and incontext editing (KE) is not effective due to the substantial cost associated with retraining. |
| Approach: | They propose a new decoding technique that enhances in-context editing (ICE) they propose to use parametric knowledge to update the models' knowledge . |
| Outcome: | The proposed technique improves ICE performance while incurring only half the latency. |
SemAttack: Natural Textual Attacks via Different Semantic Spaces (2022.findings-naacl)
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| Challenge: | Existing approaches to attack pre-trained language models suffer from low success rates or fail to search efficiently in the exponentially large perturbation space. |
| Approach: | They propose an efficient framework to generate natural adversarial text by constructing different semantic perturbation functions. |
| Outcome: | The proposed framework generates natural adversarial texts for different languages with high success rates. |
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning (2024.findings-acl)
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Zhiyang Xu, Chao Feng, Rulin Shao, Trevor Ashby, Ying Shen, Di Jin, Yu Cheng, Qifan Wang, Lifu Huang
| Challenge: | Recent vision-language models (VLMs) have shown impressive capabilities as general visual assistants, but there are two challenges to their performance: (1) lacking task diversity in pretraining and visual instruction tuning; (2) annotation error and bias in GPT-4 synthesized instruction tuning data. |
| Approach: | They propose a two-stage instruction tuning framework that fine tunes VLMs firstly and further tuned on GPT-4 synthesized data. |
| Outcome: | The proposed framework outperforms the traditional single-stage visual instruction tuning framework and achieves state-of-the-art performance across a wide range of multi-modal evaluation benchmarks. |
Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks (2026.acl-demo)
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Guangwei Zhang, Jianing Zhu, Cheng Qian, Neil Zhenqiang Gong, Rada Mihalcea, Zhaozhuo Xu, Jingrui He, Jiaqi W. Ma, Chaowei Xiao, Bo Li, Ahmed Abbasi, Dongwon Lee, Heng Ji, Denghui Zhang
| Challenge: | **Copyright Detective** is the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs. |
| Approach: | They propose a system that detects copyright infringements and visualizes them . they use content recall testing, paraphrase-level similarity analysis and persuasive jailbreak probing . |
| Outcome: | The proposed system detects, analyzes, and visualizes potential copyright risks in LLM outputs. |
Logic-Regularized Verifier Elicits Reasoning from LLMs (2025.acl-long)
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| Challenge: | Typical verifiers require resource-intensive supervised dataset construction, which is costly and faces limitations in data diversity. |
| Approach: | They propose an unsupervised verifier regularized by logical rules that uses internal activations and logical constraints on multiple reasoning paths. |
| Outcome: | Experiments on 10 datasets show that the proposed verifier outperforms baselines and is comparable to the supervised verifier. |
Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better (2024.acl-long)
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Shengchao Liu, Xiaoming Liu, Yichen Wang, Zehua Cheng, Chengzhengxu Li, Zhaohan Zhang, Yu Lan, Chao Shen
| Challenge: | Existing methods to detect MGT from human-written texts are inadequate . existing methods are fine-tuned and zero-shot metric-based, but they can be more accurate. |
| Approach: | They propose a novel fine-tuned detector that can detect MGT from human-written texts by contrastive learning on selective perturbation. |
| Outcome: | The proposed method outperforms the state-of-the-art by 1.20% on four public datasets. |
CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP (2025.acl-long)
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| Challenge: | MU has gained significant attention as a means to remove the influence of specific data from a trained model without requiring full retraining. |
| Approach: | They propose a novel approach that disentangles and selectively forgets both visual and textual associations, ensuring that unlearning does not compromise model performance. |
| Outcome: | Experiments on CIFAR-100, Flickr30K, and Conceptual 12M show that CLIPErase effectively removes designated associations from multimodal samples in downstream tasks while preserving model performance on retain set. |
Learning to Instruct: Fine-Tuning a Task-Aware Instruction Optimizer for Black-Box LLMs (2025.findings-emnlp)
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Yunzhe Qi, Jinjin Tian, Tianci Liu, Ruirui Li, Tianxin Wei, Hui Liu, Xianfeng Tang, Monica Xiao Cheng, Jingrui He
| Challenge: | Learning to Instruct is a new paradigm for black-box LLMs with inaccessible internal states. |
| Approach: | They propose a new paradigm that formulates instruction optimization as an LLM fine-tuning objective for a white-box “instruction engineer” LLM. |
| Outcome: | The proposed framework outperforms strong baselines in performance and efficiency. |
Intention Reasoning Network for Multi-Domain End-to-end Task-Oriented Dialogue (2021.emnlp-main)
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| Challenge: | Recent years has witnessed the remarkable success in end-to-end task-oriented dialog system, especially when incorporating external knowledge information. |
| Approach: | They propose a mechanism to model deterministic entity knowledge by using an intention reasoning network to obtain intention-aware representations of conceptual tokens. |
| Outcome: | The proposed mechanism captures concept shifts and generates accurate responses on two representative multi-domain dialog datasets. |
SCALE: Synergized Collaboration of Asymmetric Language Translation Engines (2024.findings-acl)
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| Challenge: | In this paper, we introduce SCALE, a collaborative framework that connects a compact Specialized Translation Model (STM) and a general-purpose Large Language Model (LLM) as one unified translation engine. |
| Approach: | They propose a collaborative framework that connects a Specialized Translation Model (STM) and a general-purpose Large Language Model (LLM) as one unified translation engine. |
| Outcome: | The proposed framework outperforms both LLMs and supervised models in high-resource or challenging low-resourced settings. |
The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis (2026.acl-long)
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Zihao Wei, Liang Pang, Jiahao Liu, Wenjie Shi, Jingcheng Deng, Shicheng Xu, Zenghao Duan, Jingang Wang, Fei Sun, Huawei Shen, Xueqi Cheng
| Challenge: | Explicit reasoning trajectories increase performance but often trigger overthinking . despite its importance, this study examines how each step of reasoning affects the final outcome . |
| Approach: | They propose a Reasoning Completion Point Detector that detects the RCP by monitoring rank dynamics of termination tokens. |
| Outcome: | The proposed method reduces token usage by up to 44% while preserving accuracy. |
Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts (2025.naacl-long)
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| Challenge: | Mixture-of-Experts (MoE) models are constrained by their fixed model capacities when the number of tasks grows in instruction tuning. |
| Approach: | They propose to combine all training tasks and apply fixed sampling weights without considering the importance of different tasks as the model training state changes. |
| Outcome: | The proposed method can be used on knowledge & reasoning tasks and open-ended queries with limited training budget. |
Syntax Matters: Towards Spoken Language Understanding via Syntax-Aware Attention (2023.findings-emnlp)
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| Challenge: | Existing studies on SLU systems have focused on integrating syntactic information into language models. |
| Approach: | They propose a model where attention scopes are constrained based on syntactic relationships. |
| Outcome: | The proposed model improves on three datasets and can be integrated into other language models to further boost their performance. |
Learning to Align Multi-Faceted Evaluation: A Unified and Robust Framework (2025.findings-acl)
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Kaishuai Xu, Tiezheng Yu, Yi Cheng, Wenjun Hou, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li
| Challenge: | Existing methods for fine-tuning open-source LLMs are limited to text-based analysis under predefined general criteria. |
| Approach: | They propose a framework that fine-tunes LLMs to replicate the evaluation explanations and judgments of proprietary models. |
| Outcome: | The proposed evaluation framework outperforms existing fine-tuned evaluation methods in effectiveness and robustness. |
CMIG: Conceptual Metaphor Theory-Inspired Framework for Metaphorical Image Generation (2026.findings-acl)
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| Challenge: | Existing text-to-image systems often produce visually plausible but semantically literal outputs. |
| Approach: | They propose a structured prompting framework inspired by Conceptual Metaphor Theory . they propose to identify source–target mappings, filter projectable source attributes and select a visual realization strategy in a reproducible reasoning workflow. |
| Outcome: | The proposed framework improves semantic alignment and controllability on metaphor prompts. |
Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing (2025.acl-long)
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| Challenge: | Dynamical systems theory provides a framework for understanding iterative processes and evolution over time. |
| Approach: | They propose to apply this perspective to large language models which iteratively map input text to output text and re-express meaning with linguistic variation. |
| Outcome: | The proposed model reveals that paraphrases re-express meaning with linguistic variation limiting linguistic diversity . |
Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models (2025.findings-emnlp)
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Haibo Wang, Zhiyang Xu, Yu Cheng, Shizhe Diao, Yufan Zhou, Yixin Cao, Qifan Wang, Weifeng Ge, Lifu Huang
| Challenge: | Video Large Language Models (VLMs) have been praised for their performance in coarse-grained video understanding but still face ineffective temporal grounding and inadequate timestamp representations. |
| Approach: | They propose a novel Video-LLM that senses and reasoned over specific video moments with fine-grained temporal precision. |
| Outcome: | The proposed model surpasses existing models in fine-grained video understanding tasks and exhibits strong potential as a general video understanding assistant. |
Uncertainty Propagation on LLM Agent (2025.acl-long)
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Qiwei Zhao, Dong Li, Yanchi Liu, Wei Cheng, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Huaxiu Yao, Chen Zhao, Haifeng Chen, Xujiang Zhao
| Challenge: | Existing methods for estimating uncertainty in large language models (LLMs) focus on final-step outputs, which fail to account for cumulative uncertainty over multi-step decision-making process and dynamic interactions between agents and their environments. |
| Approach: | They propose a framework that propagates uncertainty through each step of an LLM-based agent’s reasoning process. |
| Outcome: | Extensive experiments on benchmark datasets show that the proposed framework outperforms state-of-the-art methods by 20%. |
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)
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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang
| Challenge: | Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence. |
| Approach: | They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included . |
| Outcome: | The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model. |
Reading Comprehension with Graph-based Temporal-Casual Reasoning (C18-1)
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| Challenge: | Existing methods for reading comprehension tasks ignore semantic relations between sentences or use sliding window scanning over the words of the passage without sentence breaks. |
| Approach: | They propose a method to integrate information from multiple sentences to answer complex questions. |
| Outcome: | Experiments on RACE and MCTest show that the proposed approach improves state-of-the-art methods on simple factoid questions. |
RRInf: Efficient Influence Function Estimation via Ridge Regression for Large Language Models and Text-to-Image Diffusion Models (2025.emnlp-main)
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| Challenge: | Influence function estimates the impact of training data on model predictions . high computational cost has hindered their applicability in large-scale applications. |
| Approach: | They propose a method to quantify the impact of training data on model predictions . they use a ridge regression problem to transform the estimation into a problem . |
| Outcome: | The proposed method outperforms existing methods on noisy data detection and influential data identification tasks. |
Local Byte Fusion for Neural Machine Translation (2023.acl-long)
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| Challenge: | Existing NLP models rely on a pre-built subword tokenizer to tokenize a sentence . this can be rigid and subwords from low-resource languages are under-represented . |
| Approach: | They propose a method for byte-based machine translation that aggregates local semantic information. |
| Outcome: | The proposed method improves on multilingual translation and cross-lingual transfer . it is parameter-efficient and performs competitively to subword models, it is shown . |
From Local Perspective to Global Reasoning: A Neuro-Symbolic Framework for Zero-Shot Relation Extraction (2026.findings-acl)
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| Challenge: | Existing methods for zero-shot relationship extraction do not distinguish between unseen, semantically similar relations. |
| Approach: | They propose a framework to enable global reasoning across a set of predictions. |
| Outcome: | The proposed framework outperforms existing methods and establishes new state-of-the-art results on widely used datasets. |
RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios (2025.acl-long)
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| Challenge: | RuleArena assesses the ability of large language models (LLMs) to follow complex, real-world rules in reasoning. |
| Approach: | They propose a benchmark to evaluate the ability of large language models (LLMs) to follow complex, real-world rules in reasoning. |
| Outcome: | The proposed benchmark covers airline baggage fees, NBA transactions, and tax regulations. |
PACAR: Automated Fact-Checking with Planning and Customized Action Reasoning Using Large Language Models (2024.lrec-main)
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| Challenge: | Existing studies rely on idealized "gold" evidence for predictions, which is unrealistic due to its limited availability in real-world scenarios. |
| Approach: | They propose a fact-checking framework based on planning and customized action reasoning using LLMs. |
| Outcome: | The proposed framework outperforms baseline methods across three datasets and with varying complexity levels. |
Dialogue State Tracking with a Language Model using Schema-Driven Prompting (2021.emnlp-main)
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| Challenge: | Task-oriented conversational systems often use dialogue state tracking to represent the user’s intentions, which involves filling in values of pre-defined slots. |
| Approach: | They propose a schema-driven prompting approach that provides task-aware history encoding that is used for both categorical and non-categorical slots. |
| Outcome: | The proposed system achieves state-of-the-art performance on MultiWOZ 2.2 and competitive performance on two other benchmarks: MultiWOz 2.1 and M2M. |
QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback based Self-Correction (2024.acl-long)
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| Challenge: | Existing methods for semantic parsing fail when hallucinations are encountered . QueryAgent solves a question step-by-step and performs stepwise self-correction . |
| Approach: | They propose a framework that solves a query step-by-step and performs stepwise self-correction. |
| Outcome: | The proposed framework outperforms existing methods on GrailQA and GraphQ by 5.7 and 15.0 points. |
Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models (2026.acl-long)
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| Challenge: | Recent advances in reasoning-oriented models have demonstrated impressive capabilities in mathematical reasoning, but their ability to adhere to user directives remains underexplored. |
| Approach: | They propose a benchmark to evaluate instruction-following in mathematical reasoning tasks. |
| Outcome: | The proposed model degrades in instruction adherence when generation length increases, but can partially recover obedience, despite increasing generation length. |
Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)
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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge. |
| Approach: | They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. |
| Outcome: | The proposed model protects private data while enhancing the model's knowledge. |
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. |
DCR: Quantifying Data Contamination in LLMs Evaluation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) memorize evaluation data during training, inflating performance metrics and undermining genuine generalization assessment. |
| Approach: | They propose a framework to detect and quantify benchmark data contamination (BDC) by synthesizing contamination scores via a fuzzy inference system. |
| Outcome: | The proposed framework detects and quantifies BDC risk across semantic, informational, data, and label levels. |
HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models (2023.emnlp-main)
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| Challenge: | Existing large language models (LLMs) are prone to generate hallucinations . a recent study shows that LLMs are able to generate content that conflicts with the source or cannot be verified by factual knowledge. |
| Approach: | They propose a framework to evaluate the performance of large language models (LLMs) they propose to use a sample of generated and human-annotated hallucinated samples to evaluate their performance . |
| Outcome: | The proposed framework generates and annotates hallucinated samples from ChatGPT . the results show that existing LLMs face great challenges in recognizing hallucines . |
DAGS: A Dependency-Based Dual-Attention and Global Semantic Improvement Framework for Metaphor Recognition (2025.findings-acl)
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| Challenge: | Existing methods for metaphor recognition ignore interference caused by literal annotations . et al., 2018: Metaphor recognition plays an important role in cognition and communication . |
| Approach: | They propose a dependency-based Dual-Attention and Global Semantic Improvement framework to improve metaphor recognition. |
| Outcome: | The proposed framework can extract features from multiple information sources while improving on mainstream metaphor datasets. |
AnchorCoT: Anchors Pave the Way for Multi-hop Reasoning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated potential reasoning capabilities through prompt design, such as the Chain of Thought (CoT). |
| Approach: | They propose a new reasoning approach that predicts key entities which work as important “anchors” and employs a ranking algorithm to ensure the logical sequence of the predicted answers. |
| Outcome: | The proposed approach outperforms existing methods in multi-hop question reasoning and provides more accurate reasoning results in multihop question answering tasks. |
SURf: Teaching Large Vision-Language Models to Selectively Utilize Retrieved Information (2024.emnlp-main)
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| Challenge: | Existing studies focus on the text modality or are limited to specific tasks. |
| Approach: | They propose a framework to teach Large Vision-Language Models to selectively utilize retrieved information and improve their robustness against irrelevant or misleading references. |
| Outcome: | The proposed framework improves LVLMs’ ability to utilize retrieved multimodal references and their robustness against irrelevant or misleading information. |
Posterior Differential Regularization with f-divergence for Improving Model Robustness (2021.naacl-main)
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| Challenge: | Recent studies show that pre-trained models suffer catastrophic degradation in out-of-domain generalization to datasets with domain shift or adversarial scenarios. |
| Approach: | They propose to regularize the posterior difference between clean and noisy inputs by using a Jacobian regularization framework and a virtual adversarial training framework. |
| Outcome: | The proposed framework can improve model robustness in fully supervised and semi-supervised settings. |
Adaptive Information Seeking for Open-Domain Question Answering (2021.emnlp-main)
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| Challenge: | Existing iterative approaches to open-domain question answering use predefined strategies . e.g., BM25, DPR, and hyperlink are defined as actions . |
| Approach: | They propose a novel adaptive information-seeking strategy for open-domain question answering . they propose to use a partially observed Markov decision process to select a proper retrieval action . |
| Outcome: | Experiments on SQuAD Open and HotpotQA fullwiki show that AISO outperforms baseline methods with predefined strategies in retrieval and answer evaluations. |
Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition (2022.naacl-main)
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| Challenge: | Named Entity Recognition (NER) datasets annotate coarse-grained entities such as a continent, a country, or a city. |
| Approach: | They propose a dataset HarveyNER with fine-grained locations annotated in tweets that characterizes many complex and long location mentions in informal descriptions. |
| Outcome: | The proposed dataset outperforms existing systems on hard cases and improves on the heuristic curricula. |
Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation (2025.emnlp-main)
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| Challenge: | Existing RAG models are sensitive to the order in which evidence is presented, resulting in unstable performance and biased reasoning. |
| Approach: | They propose to quantify position bias in multimodal RAG systems by using position sensitivity index . they also develop a visualization framework to trace attention allocation patterns across decoder layers . |
| Outcome: | The proposed framework shows that multimodal interactions intensify position bias compared to unimodal settings and that this bias increases logarithmically with retrieval range. |
TAGPRIME: A Unified Framework for Relational Structure Extraction (2023.acl-long)
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| Challenge: | Existing models for natural language processing (NLP) do not address common tasks. |
| Approach: | They propose to take a unified view of all the tasks and introduce a model that appends priming words about the condition to the input text. |
| Outcome: | The proposed model is based on ten datasets across five different languages and covers ten tasks that cover ten languages. |
MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine Translation (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have shown their strong ability in the field of machine translation, yet they suffer from high computational cost and latency. |
| Approach: | They propose a framework which transfers knowledge from LLMs to existing MT models in a selective, comprehensive and proactive manner. |
| Outcome: | The proposed framework transfers knowledge from LLMs to existing MT models in a selective, comprehensive and proactive manner. |
Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach (2021.acl-long)
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| Challenge: | Existing efforts to enhance the performance of session-based cyberbullying detection have overlooked unintended social biases in existing datasets. |
| Approach: | They propose a model-agnostic debiasing strategy that leverages a reinforcement learning technique to mitigate unintended biases in existing datasets. |
| Outcome: | The proposed approach can mitigate unintended biases without impairing the detection performance. |
When Do LLMs Need Retrieval Augmentation? Mitigating LLMs’ Overconfidence Helps Retrieval Augmentation (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have difficulty knowing they do not possess certain knowledge and tend to provide specious answers in such cases. |
| Approach: | They propose to use Retrieval Augmentation to enhance LLMs' ability to perceive their knowledge boundaries to reduce overconfidence. |
| Outcome: | The proposed methods reduce overconfidence and improve accuracy in large language models with fewer retrieval calls. |
Spontaneous gestures encoded by hand positions improve language models: An Information-Theoretic motivated study (2023.findings-acl)
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| Challenge: | a key missing step is to explore whether the nonverbal information can be quantified. |
| Approach: | They explore whether incorporating gesture representations can improve the language model’s performance . they also examine whether spontaneous gestures demonstrate entropy rate constancy (ERC) . |
| Outcome: | The proposed model improves the performance of the mixed-modal language models against monologue video data. |
SeqPO-SiMT: Sequential Policy Optimization for Simultaneous Machine Translation (2025.findings-acl)
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| Challenge: | SeqPO-SiMT is a new policy optimization framework for simultaneous machine translation that combines a tailored reward with a single step task. |
| Approach: | They propose a new policy optimization framework that defines the simultaneous machine translation task as a sequential decision making problem with a tailored reward. |
| Outcome: | The proposed framework outperforms the supervised fine-tuning model by 1.13 points while reducing the Average Lagging by 6.17 in the NEWSTEST2021 En Zh dataset. |
Explainable Recommendation with Personalized Review Retrieval and Aspect Learning (2023.acl-long)
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| Challenge: | Recent years have witnessed a growing interest in the development of explainable recommendation models. |
| Approach: | They propose a model that combines prediction and generation tasks to produce more persuasive explanations by obtaining additional information from the training sets. |
| Outcome: | The proposed model outperforms state-of-the-art models on three datasets and shows that it is more persuasive than previous models. |
MixLLM: Dynamic Routing in Mixed Large Language Models (2025.naacl-long)
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Xinyuan Wang, Yanchi Liu, Wei Cheng, Xujiang Zhao, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen
| Challenge: | Large Language Models (LLMs) exhibit potential artificial generic intelligence, however, their usage is costly with high response latency. |
| Approach: | They develop a dynamic contextual-bandit-based routing system for query-LLM assignment that leverages query tags to enhance query embeddings. |
| Outcome: | The proposed model maximizes response quality and minimizes cost and latency. |
Modelling Long-distance Node Relations for KBQA with Global Dynamic Graph (2020.coling-main)
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| Challenge: | Existing studies rely on deep graph neural networks (GNNs) to capture rich structural information, but they lack the structural information needed for QA. |
| Approach: | They propose a framework which captures structural information from KBs and models long-distance node relations from two perspectives. |
| Outcome: | The proposed framework models long-distance node relations from two perspectives . it is based on two widely used multi-hop KBQA datasets . |
MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models (2026.findings-eacl)
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Siwei Wu, King Zhu, Yu Bai, Yiming Liang, Yizhi Li, Haoning Wu, Jiaheng Liu, Ruibo Liu, Xingwei Qu, Xuxin Cheng, Ge Zhang, Wenhao Huang, Chenghua Lin
| Challenge: | Current multimodal benchmarks focus on facts within individual images, but neglect associative relations among multiple images. |
| Approach: | They propose a multi-image relational association task and a MMRA benchmark to evaluate LVLMs. |
| Outcome: | The proposed benchmarks show that entity-level multi-image perception tasks pose greater challenges than image-level tasks. |
NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism (2024.acl-long)
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Miao Li, Ming-Bin Chen, Bo Tang, ShengbinHou ShengbinHou, Pengyu Wang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Keming Mao, Cheng Peng, Yi Luo
| Challenge: | a novel evaluation framework assesses the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. |
| Approach: | They propose to use a benchmark dataset to assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. |
| Outcome: | The proposed evaluation framework is based on a dataset of 1,267 test samples in 24 news domains. |
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. |
CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation (2024.acl-long)
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Pei Ke, Bosi Wen, Andrew Feng, Xiao Liu, Xuanyu Lei, Jiale Cheng, Shengyuan Wang, Aohan Zeng, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Existing models for NLP evaluations lack the ability to generate informative critiques in pointwise grading and pairwise comparison especially without references. |
| Approach: | They propose a method which can acquire pointwise grading critiques with pseudo references and revise these critiques via multi-path prompting to obtain informative evaluation data in different tasks and settings. |
| Outcome: | The proposed method outperforms all open-source models and even GPT-4 in system-level correlations of pointwise grading. |
SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check (2020.acl-main)
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| Challenge: | Existing methods to detect and correct spelling errors in Chinese take external input or just heuristic rules. |
| Approach: | They propose to incorporate phonological and visual similarity knowledge into Chinese language models by using a specialized graph convolutional network. |
| Outcome: | The proposed method outperforms existing models on three human-annotated datasets. |
MoE-SLU: Towards ASR-Robust Spoken Language Understanding via Mixture-of-Experts (2024.findings-acl)
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| Challenge: | Spoken language understanding (SLU) is a crucial task in task-oriented dialogue systems. |
| Approach: | They propose an ASR-Robust SLU framework based on the mixture-of-experts technique to generate additional transcripts from clean transcripts and use it to weigh the representations of the generated transcripts, ASR transcripts . |
| Outcome: | The proposed framework achieves state-of-the-art on three benchmark SLU datasets. |
QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning (2026.acl-long)
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Songxin Qu, Tai-Ping Sun, Yun-Jie Wang, Huan-Yu Liu, Cheng Xue, Xiao-Fan Xu, Han Fang, Yang Yang, Yu-Chun Wu, Guo-Ping Guo, Zhao-Yun Chen
| Challenge: | Large language models lack reliability in scientific domains that require strict adherence to physical constraints. |
| Approach: | They propose a large-scale dataset constructed via a task-adaptive strategy and a hybrid verification protocol that combines deterministic solvers with semantic auditing to guarantee scientific rigor. |
| Outcome: | The proposed model outperforms baselines and general-purpose preference models and is competitive with proprietary models. |
Batch Prompting: Efficient Inference with Large Language Model APIs (2023.emnlp-industry)
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| Challenge: | Performing inference on large volumes of samples can be computationally and financially costly. |
| Approach: | They propose a prompting approach that enables large language models to run inference in batches instead of one sample at a time. |
| Outcome: | The proposed prompting reduces both token and time costs while retaining downstream performance. |
UEGP: Unified Expert-Guided Pre-training for Knowledge Rekindle (2024.findings-naacl)
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Yutao Mou, Kexiang Wang, Jianhe Lin, Dehong Ma, Jun Fan, Daiting Shi, Zhicong Cheng, Gu Simiu, Dawei Yin, Weiran Xu
| Challenge: | Existing paradigms for pre-training and fine-tuning have limitations . knowledge rekindle aims to break through performance upper bounds of experts without introducing additional annotated data. |
| Approach: | They propose a new paradigm for pre-training and fine-tuning that aims to re-incorporate the fine- tuned expert model into the training cycle and break through performance upper bounds of experts. |
| Outcome: | The proposed model breaks through performance upper bounds of experts without additional annotated data. |
Randomized Smoothing Meets Vision-Language Models (2025.emnlp-main)
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| Challenge: | Randomized smoothing (RS) is a well-known technique to ensure the correctness of machine learning models. |
| Approach: | They propose a randomized smoothing technique that can be used to ensure correctness of machine learning models. |
| Outcome: | The proposed method can be used to classify oracle outputs as harmful vs. harmless . the results are validated against recent jailbreak-style adversarial attacks . |
MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval (2025.emnlp-main)
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| Challenge: | Document Understanding is a foundational AI capability with broad applications . Large Vision-Language Models (LLMs) can't handle multi-page document comprehension . a logic-aware retrieval framework for multi-modal, multi- page document understanding is proposed . |
| Approach: | They propose a logic-aware retrieval framework for multi-modal, multi-page document understanding . MoLoRAG uses semantic and logical relevance to deliver more accurate retrieval . |
| Outcome: | The proposed framework improves on four DocQA datasets and demonstrates 9.68% accuracy improvement over existing methods. |
Following the Autoregressive Nature of LLM Embeddings via Compression and Alignment (2025.emnlp-main)
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Jingcheng Deng, Zhongtao Jiang, Liang Pang, Zihao Wei, Liwei Chen, Kun Xu, Yang Song, Huawei Shen, Xueqi Cheng
| Challenge: | Experimental results demonstrate that our method significantly outperforms traditional contrastive learning approaches when using the same amount of data. |
| Approach: | They propose a new contrastive learning method built on embedding conditional probability distributions that integrates two tasks: information compression and conditional distribution alignment. |
| Outcome: | The proposed method outperforms traditional contrastive learning approaches and achieves comparable performance to state-of-the-art models when using the same amount of data. |
Calibrating Imbalanced Classifiers with Focal Loss: An Empirical Study (2022.emnlp-industry)
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| Challenge: | Imbalanced data distributions can cause models to overfit to majority classes and output unreliable (mostly overconfident) predictions. |
| Approach: | They propose to streamline the model development and deployment using focal loss to address imbalanced data distributions. |
| Outcome: | The proposed model training with focal loss improves calibration and accuracy compared to standard cross-entropy loss. |
HookMoE: A learnable performance compensation strategy of Mixture-of-Experts for LLM inference acceleration (2025.emnlp-main)
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| Challenge: | Mixture of Experts (MoE) models have been a promising paradigm for scaling model capacity through top-k routing mechanisms. |
| Approach: | They propose a plug-and-play single-layer compensation framework that strategically inserts a lightweight trainable Hook module immediately preceding selected transformer blocks. |
| Outcome: | The proposed framework reduces the number of activated experts by more than 50% and achieves a 1.42 inference speed-up during the prefill stage. |
ToneCraft: Cantonese Lyrics Generation with Harmony of Tones and Pitches (2025.emnlp-main)
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| Challenge: | Current research has not addressed the challenge of generating harmonious Cantonese lyrics. |
| Approach: | They propose a framework for generating Cantonese lyrics that ensures tonal and melodic harmony. |
| Outcome: | The proposed framework ensures tonal and melodic harmony while preserving character count and quality. |
Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System (P19-3)
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Guo Zhipeng, Xiaoyuan Yi, Maosong Sun, Wenhao Li, Cheng Yang, Jiannan Liang, Huimin Chen, Yuhui Zhang, Ruoyu Li
| Challenge: | Existing systems for automatic poetry generation are model-oriented, resulting in poor user participation. |
| Approach: | They propose a human-machine collaborative Chinese classical poetry generation system called Jiuge . Jiuge allows users to revise unsatisfied parts of a generated poem draft repeatedly . |
| Outcome: | The proposed system allows users to revise unsatisfied parts of a generated poem draft repeatedly. |
Persona Jailbreaking in Large Language Models (2026.findings-eacl)
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| Challenge: | Existing studies focus on narrative or role-playing tasks and overlook how adversarial conversational history alone can reshape induced personas. |
| Approach: | They propose a framework that embeds semantically loaded cues into user queries to gradually induce reverse personas. |
| Outcome: | The proposed framework predictably shifts personas, triggers collateral changes in correlated traits, and exhibits stronger effects in multi-turn settings. |
Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR (2026.acl-long)
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Haobo Xu, Sirui Chen, Ruizhong Qiu, Yuchen Yan, Chen Luo, Monica Xiao Cheng, Jingrui He, Hanghang Tong
| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) has improved reasoning capabilities of Large Language Models (LLMs). |
| Approach: | They propose an online pruning method that prunes rollouts while steering correct ones to enhance learning signals. |
| Outcome: | The proposed method improves average accuracy by +2.30 to +2.99 across GRPO and DAPO on Qwen-3 and LLaMA-3.2 models. |
From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition (2025.emnlp-main)
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| Challenge: | Recent advances in Automatic Speech Recognition (ASR) have been fueled by massive speech corpora, but extending coverage to diverse languages with limited resources remains a formidable challenge. |
| Approach: | They propose a pipeline that converts large-scale text corpora into synthetic speech using off-the-shelf text-to-speech (TTS) models. |
| Outcome: | The proposed pipeline generates 500,000 hours of synthetic speech in ten languages and achieves transcription error reductions of over 30%. |
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech (2022.acl-long)
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| Challenge: | Experimental results show that the proposed model improves naturalness and prosody diversity with clear margins. |
| Approach: | They propose a cross-utterance conditional VAE to estimate posterior probability distribution of latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features from past and future sentences. |
| Outcome: | The proposed model improves naturalness and prosody diversity with clear margins. |
3DRP-Net: 3D Relative Position-aware Network for 3D Visual Grounding (2023.emnlp-main)
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| Challenge: | 3D visual grounding aims to localize the desired objects in a 3D point cloud by a free-form language description. |
| Approach: | They propose a relation-aware framework which captures relative spatial relationships between objects and enhances object attributes. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three benchmarks . it captures relative spatial relationships between objects and enhances object attributes . |
Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation (2023.emnlp-main)
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| Challenge: | a recent study defines a conversation target from the system side to proactively steer conversations toward predefined targets or accomplish specific system-side goals. |
| Approach: | They propose a dataset curation framework that automatically curations a large-scale personalized dialogue dataset using a role-playing approach. |
| Outcome: | The proposed dataset is of high quality and could contribute to exploring personalized target-oriented dialogue. |
Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning (2026.acl-long)
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| Challenge: | Recent advances in model distillation show that data from advanced reasoning models can effectively train smaller student models. |
| Approach: | They propose a method to use both positive and negative distilled reasoning traces to maximize LLM reasoning performance in offline settings. |
| Outcome: | The proposed model outperforms existing methods in the distillation context. |
LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)
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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen
| Challenge: | a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented. |
| Approach: | They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs). |
| Outcome: | The proposed library is based on extensive experiments in a variety of evaluation settings. |
Varta: A Large-Scale Headline-Generation Dataset for Indic Languages (2023.findings-acl)
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| Challenge: | Varta dataset includes more than 41 million pairs of headlines and articles in 14 different Indic languages (and English) |
| Approach: | They present a large-scale multilingual dataset for headline generation in Indic languages. |
| Outcome: | The Varta dataset includes more than 41 million pairs of headlines and articles in 14 different Indic languages (and English) the data can be used to train strong language models that outperform competitive baselines in both NLU and NLG benchmarks. |
Localization of Fake News Detection via Multitask Transfer Learning (2020.lrec-1)
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| Challenge: | Existing methods for detecting fake news require large labeled datasets and expert-curated corpora, which low-resource languages may not have. |
| Approach: | They construct a benchmark dataset for fake news detection in Filipino using curated corpora and transfer learning techniques. |
| Outcome: | The proposed method can achieve 91% accuracy on a fake news dataset, reducing error by 14% compared to established baselines. |
PRISM-MCTS: Learning from Reasoning Trajectories with Metacognitive Reflection (2026.findings-acl)
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| Challenge: | Existing reasoning models are limited by inefficiency and computational redundancy . PRISM-MCTS integrates a process reward model with a dynamic shared memory . |
| Approach: | They propose a reasoning framework that integrates a process reward model with a dynamic shared memory. |
| Outcome: | PRISM-MCTS integrates a process reward model with a dynamic shared memory . it halves trajectory requirements on GPQA while surpassing MCTS-RAG and Search-o1 . |