Papers by Ming Chen
Incentivizing In-depth Reasoning over Long Contexts with Process Advantage Shaping (2026.findings-acl)
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| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing LLMs’ short-context reasoning but falters in long-contemporal scenarios requiring precise grounding and multi-hop reasoning. |
| Approach: | They propose a framework that constructs high-difficulty, multi-hop long-context QA pairs with inherent reasoning chains to overcome this bottleneck. |
| Outcome: | The proposed framework outperforms RLVR baselines and matches frontier LLMs while using far fewer parameters. |
Muse: Towards Reproducible Long-Form Song Generation with Fine-Grained Style Control (2026.findings-acl)
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Changhao Jiang, Jiahao Chen, Zhenghao Xiang, Zhixiong Yang, Hanchen Wang, Jiabao Zhuang, Xinmeng Che, Jiajun Sun, Hui Li, Yifei Cao, Shihan Dou, Ming Zhang, Junjie Ye, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Recent commercial systems such as Suno demonstrate strong capabilities in long-form song generation, but academic research remains non-reproducible due to the lack of publicly available training data. |
| Approach: | They propose a system for long-form song generation with fine-grained style conditioning that includes a licensed synthetic dataset and a song generation model, Muse. |
| Outcome: | The proposed system achieves competitive performance on phoneme error rate, text–music style similarity, and audio aesthetic quality while enabling controllable segment-level generation across different musical structures. |
Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification (2025.acl-long)
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Chengwu Liu, Ye Yuan, Yichun Yin, Yan Xu, Xin Xu, Zaoyu Chen, Yasheng Wang, Lifeng Shang, Qun Liu, Ming Zhang
| Challenge: | Chain-of-Thought prompting is a de facto method to elicit reasoning capabilities from large language models (LLMs). |
| Approach: | They propose a step-aware formal verification framework Safe to address hallucinations in CoT prompting . they propose 'formal step' as a benchmark for step correctness theorem proving with 30,809 formal statements. |
| Outcome: | The proposed framework shows significant performance improvement while offering interpretable and verifiable evidence. |
Model Composition for Multimodal Large Language Models (2024.acl-long)
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Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
| Challenge: | Existing methods for creating versatile MLLMs rely on joint training with paired instruction data, which is resource-intensive and challenging to extend to new modalities. |
| Approach: | They propose a new paradigm for multimodal large language models by reusing modality encoders and merging LLM parameters. |
| Outcome: | The proposed model retains the modal understanding capabilities of each original model. |
WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types (2022.acl-long)
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| Challenge: | Multimodal Entity Linking (MEL) is an essential task for many multimodal applications. |
| Approach: | They propose to use a human-annotated Wikipedia-based multimodal entity linking dataset to improve the quality of existing MEL models. |
| Outcome: | The proposed model uses the visual information of images more effectively than existing models. |
Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements (2024.findings-acl)
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| Challenge: | Existing LLMs lack sufficient controllability to generate statements supporting diverse or even controversial perspectives. |
| Approach: | They develop a pipeline that fine tunes LLMs to generate statements generated via debate. |
| Outcome: | The proposed pipeline improves the controllability of LLMs in generating statements supporting an argument the user defined in the prompt. |
Evaluating and Enhancing the Robustness of Code Pre-trained Models through Structure-Aware Adversarial Samples Generation (2023.findings-emnlp)
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| Challenge: | Pre-trained code models have made significant strides in the field of neural code intelligence, but they are susceptible to adversarial attacks that subtly modify the input sequence and can impair generalization. |
| Approach: | They propose a set of novel robustness evaluation methods based on the intrinsic structure of the code to explore the impact of imperceptible perturbation. |
| Outcome: | The proposed methods have demonstrated their effectiveness across a wide range of models and tasks, and are able to predict the performance of perturbed models. |
Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)
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Yongqi Li, Shen Zhou, Xiaohu Li, Xin Miao, Jintao Wen, Mayi Xu, Jianhao Chen, Birong Pan, Hankun Kang, Yuanyuan Zhu, Ming Zhong, Tieyun Qian
| Challenge: | Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation. |
| Approach: | They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved. |
| Outcome: | The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment. |
SAC: Neural Speech Codec with Semantic-Acoustic Dual-Stream Quantization (2026.acl-long)
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Wenxi Chen, Ruiqi Yan, Yushen Chen, Zhikang Niu, Ziyang Ma, Xiquan Li, Yuzhe Liang, null Wenhanlin, Shunshun Yin, Ming Tao, Xinsheng Wang, Xie Chen
| Challenge: | Existing speech codecs struggle to balance high-quality reconstruction with semantically rich representations, limiting their effectiveness in both generative and understanding tasks. |
| Approach: | They propose a neural speech codec with semantic-acoustic dual-stream quantization that disentangles semantic and acousian modeling into two dedicated streams. |
| Outcome: | The proposed codec outperforms state-of-the-art speech tokenizers in auto-propagating text-to-speech models. |
Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have shown increasing power on NLP tasks. however, tuning these models for downstream tasks usually requires exorbitant costs. |
| Approach: | They propose a black-box tuning technique that optimizes task-specific prompts without accessing gradients and hidden representations. |
| Outcome: | The proposed method improves performance under few-shot learning scenarios. |
ActionStudio: A Lightweight Framework for Data and Training of Large Action Models (2025.emnlp-main)
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Jianguo Zhang, Thai Quoc Hoang, Ming Zhu, Zuxin Liu, Shiyu Wang, Tulika Manoj Awalgaonkar, Akshara Prabhakar, Haolin Chen, Weiran Yao, Zhiwei Liu, Juntao Tan, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Existing infrastructure for efficient agentic data processing and model training remains underdeveloped. |
| Approach: | They propose a lightweight and extensible data and training framework for large action models . they propose to unify diverse agent trajectories using Unified Format 2.0 . |
| Outcome: | The proposed framework shows 9 higher throughput than existing frameworks and performs well across public and realistic agent benchmarks. |
SocialBench: Sociality Evaluation of Role-Playing Conversational Agents (2024.findings-acl)
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Hongzhan Chen, Hehong Chen, Ming Yan, Wenshen Xu, Gao Xing, Weizhou Shen, Xiaojun Quan, Chenliang Li, Ji Zhang, Fei Huang
| Challenge: | Existing studies on role-playing agents have focused on enhancing their conversational capability, role-specific knowledge and style, but there has been a gap in assessing their social intelligence. |
| Approach: | They propose a benchmark to evaluate the sociality of role-playing agents using LLMs. |
| Outcome: | The proposed benchmark is constructed from various sources and covers a wide range of 500 characters and over 6,000 question prompts and 30,800 multi-turn role-playing utterances. |
RubricBench: Aligning Model-Generated Rubrics with Human Standards (2026.acl-long)
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Junyi Zhou, Qiyuan Zhang, Yufei Wang, Fuyuan Lyu, Yidong Ming, Can Xu, Qingfeng Sun, Kai Zheng, Peng Kang, Xue Liu, Chen Ma
| Challenge: | Existing benchmarks lack discriminative complexity and ground-truth rubric annotations required for rigorous evaluation. |
| Approach: | They propose a curated benchmark with 1,147 pairwise comparisons to assess the reliability of rubric-based evaluation. |
| Outcome: | The proposed benchmarks show that they support diverse domains, exhibit discriminative ability, provide high-quality annotations, and include human-authored rubrics. |
Aligning Large Language Models with Implicit Preferences from User-Generated Content (2025.acl-long)
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Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Hyokun Yun, Ming Zeng, Pei Chen, Zhihan Zhang, Yifan Gao, Ruijie Wang, Priyanka Nigam, Bing Yin, Meng Jiang
| Challenge: | Existing preference learning methods rely heavily on curated data from humans or advanced LLMs, which is costly and difficult to scale. |
| Approach: | They propose a framework that leverages implicit preferences in unlabeled user-generated content to generate preference data. |
| Outcome: | The proposed framework transforms user-generated content into user queries and generates responses from the policy model. |
Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards (2026.acl-long)
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Ming Li, Pei Chen, Zhenhao Zhang, Tao Yang, Xinyang Zhang, Han Li, Tianyu Cao, Ming Zeng, Zhuofeng Wu, Meng Jiang, Huasheng Li, Lihong Li, Bing Yin
| Challenge: | Large Language Models exhibit strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC) when instructions are revealed progressively in multi-turn settings, models get "Lost in Conversation" |
| Approach: | They propose a framework that encourages models to generate correct answers and judge solvability in multi-turn conversations. |
| Outcome: | The proposed framework improves models' ability to balance problem-solving with abstention . it reduces premature answering behaviors that cause lost-in-conversation (LiC) |
Question Generation from SQL Queries Improves Neural Semantic Parsing (D18-1)
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| Challenge: | Using question generation, we learn a semantic parser with 30% of the supervised training data. |
| Approach: | They propose to use question generation to learn a semantic parser with less supervised training data. |
| Outcome: | The proposed method improves the state-of-the-art model with less training data. |
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling (2022.naacl-main)
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| Challenge: | Existing methods to train cross-lingual pre-trained language models have shown great success in cross-linguistic sequence labeling tasks. |
| Approach: | They propose a cross-lingual language informative span masking task to eliminate the objective gap between pre-training and fine-tuning stages. |
| Outcome: | The proposed method surpasses the state-of-the-art methods on multiple benchmarks even with limited pre-training data. |
Learning to Ask Conversational Questions by Optimizing Levenshtein Distance (2021.acl-long)
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| Challenge: | Existing methods for estimating maximum likelihood are limited by easily learned tokens . Existing systems that generate questions based on dialogue context are limited in their ability to learn tokens. |
| Approach: | They propose a framework that optimizes the minimum Levenshtein distance through explicit editing actions. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two benchmark datasets and generalizes well on unseen data. |
Understanding and Improving the Robustness of Terminology Constraints in Neural Machine Translation (2023.acl-long)
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| Challenge: | Existing terminology constraint test sets are blind to this issue due to oversimplified settings . PH methods retain high constraint accuracy but lower translation quality . |
| Approach: | They propose a method that replaces terminology terms with ordered labels . placeholder methods are better at retaining high constraint accuracy but lower translation quality . |
| Outcome: | The proposed method achieves high accuracy and translation quality regardless of the number or length of constraints. |
AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation (2025.findings-acl)
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Ming Wang, Peidong Wang, Lin Wu, Xiaocui Yang, Daling Wang, Shi Feng, Yuxin Chen, Bixuan Wang, Yifei Zhang
| Challenge: | Existing models of seeker simulations are limited by the cost and ethical concerns of involving real seekers in mental health research. |
| Approach: | They propose an emotional and cognitive dynamic agent system equipped with tertiary memory to enable dynamic control of the simulator's configurations. |
| Outcome: | The proposed system achieves more realistic seeker simulation compared to baselines. |
From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction (2024.lrec-main)
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| Challenge: | Existing charge prediction methods have shown impressive performance, but they face significant challenges when dealing with confusing charges, such as Snatch and Robbery. |
| Approach: | They propose a novel approach which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge’s reasoning process. |
| Outcome: | The proposed approach maintains exceptional performance in imbalanced label distributions. |
Browse and Concentrate: Comprehending Multimodal Content via Prior-LLM Context Fusion (2024.acl-long)
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Ziyue Wang, Chi Chen, Yiqi Zhu, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) lack understanding of multi-image and interleaved inputs due to the visual features encoded by frozen encoders before being fed into the LLM backbone. |
| Approach: | They propose a two phase paradigm to enable in-depth multimodal context fusion prior to feeding the features into LLMs. |
| Outcome: | The proposed paradigm boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively. |
TheoremQA: A Theorem-driven Question Answering Dataset (2023.emnlp-main)
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| Challenge: | Recent LLMs like GPT-4 and PaLM-2 have made tremendous progress in solving fundamental math problems like GSM8K by achieving over 90% accuracy. |
| Approach: | They propose to use theorem-driven question-answering dataset to evaluate AI models' ability to apply theoretic concepts to solving challenging science problems. |
| Outcome: | TheoremQA is curated by domain experts and contains 800 high-quality questions covering 350 theoremics from Math, Physics, EE&CS, and Finance. |
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)
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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks. |
| Approach: | They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes. |
| Outcome: | The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset. |
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation (2020.emnlp-main)
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| Challenge: | Existing techniques for natural language understanding and generation use autoencoding and/or autoregressive objectives to train models. |
| Approach: | They propose a self-supervised pre-training scheme that pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus for generating new text conditioned on context. |
| Outcome: | The proposed scheme achieves state-of-the-art results on a variety of language generation benchmarks covering generative question answering, abstractive summarization and conversational response generation. |
Incorporating External Knowledge into Machine Reading for Generative Question Answering (D19-1)
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| Challenge: | Existing knowledge-aware QA models do not have commonsense and background knowledge to answer nontrivial questions. |
| Approach: | They propose a new neural model which exploits external knowledge to generate answers in natural language for a given question with context. |
| Outcome: | The proposed model improves answer quality over existing models without knowledge and knowledge-aware models, a study shows . state officials in Hawaii confirmed that president Barack Obama was born in the U.S. |
Generative Bridging Network for Neural Sequence Prediction (N18-1)
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| Challenge: | Existing approaches to improve the likelihood of sequence prediction models are based on MLE and teacher forcing. |
| Approach: | They propose a Generative Bridging Network (GBN) that extends the point-wise ground truth to a bridge distribution conditioned on it and optimizes their KL-divergence. |
| Outcome: | The proposed bridge module can improve on two recognized sequence prediction tasks and minimize learning burden. |
A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)
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Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu
| Challenge: | a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences. |
| Approach: | They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference . |
| Outcome: | The proposed frameworks achieve state-of-the-art across knowledge-intensive benchmarks. |
ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training (2020.findings-emnlp)
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| Challenge: | Existing sequence-to-sequence models are optimized for future n-gram prediction and n stream self-attention mechanism. |
| Approach: | They propose a self-supervised objective called future n-gram prediction and the proposed n stream self-attention mechanism to optimize the model for sequence-to-sequence learning. |
| Outcome: | The proposed model achieves state-of-the-art on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks compared to the models using the same scale pre-training corpus. |
Revisiting the Self-Consistency Challenges in Multi-Choice Question Formats for Large Language Model Evaluation (2024.lrec-main)
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| Challenge: | Multi-choice questions (MCQs) are a common method for assessing the world knowledge of large language models. |
| Approach: | They propose three knowledge-equivalent question variants to assess LLMs' world knowledge . they propose option position shuffle, option label replacement, and conversion to a True/False format . |
| Outcome: | The proposed questions are shuffle, label replacement, and True/False format. |
On the Human-level Performance of Visual Question Answering (2025.coling-main)
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| Challenge: | Visual7W has been widely used in assessing multiple-choice visual question-answering systems. |
| Approach: | They replicated a human experiment on Visual7W to examine the human-level performance of VQA. |
| Outcome: | The results show that the better a model performs on Visual7W, the better it aligns with human-level intelligence. |
EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering (2026.acl-long)
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Chenhui Mao, Yuanting Lei, Zhixiang Wei, Ming Liang, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li
| Challenge: | Entropy-Guided Stepwise Scaling (EGSS) is a novel TTS framework for software engineering tasks. |
| Approach: | They propose an entropy-guided stepwise scaling framework that balances efficiency and effectiveness through entropic-guide encoding and robust test-suite augmentation. |
| Outcome: | EGSS boosts performance by 5–10% across all evaluated models, and reduces inference-time token usage by over 28% . compared to existing methods, EGS reduces token usage and reduce inference time by over 20% . |
GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing (2026.acl-long)
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Ming Wang, Shuang Wu, Bixuan Wang, Lu Lin, Yuxin Chen, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang, Yufan Sun
| Challenge: | Large language models (LLMs) inherit contamination from training corpora, directional bias under social-desirability framing, and limited responsiveness to context beyond the item text. |
| Approach: | They propose a paradigm that reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline. |
| Outcome: | The proposed paradigm reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline. |
Small LLMs Are Weak Tool Learners: A Multi-LLM Agent (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have revolutionized natural language processing with impressive capabilities, but they lack domain specificity, real-time information and face challenges in solving specialized problems. |
| Approach: | They propose a multi-LLM approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer. |
| Outcome: | The proposed model outperforms existing models by demonstrating its effectiveness and advantages in tool learning. |
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction (2021.acl-long)
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Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Xu Ming, Yefeng Zheng
| Challenge: | Recent methods for extracting entities and relations from unstructured texts suffer from limitations, such as redundancy of relation prediction and inefficiency. |
| Approach: | They propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC) they propose overlapping triples for relation prediction and relation-relational alignment . |
| Outcome: | The proposed framework achieves state-of-the-art performance on public benchmarks with higher efficiency and consistent performance gain on complex scenarios of overlapping triples. |
Making LLMs Better Many-to-Many Speech-to-Text Translators with Curriculum Learning (2025.acl-long)
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Yexing Du, Youcheng Pan, Ziyang Ma, Bo Yang, Yifan Yang, Keqi Deng, Xie Chen, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Existing studies on English-centric translation tasks have focused on multimodal large language models, but the exploration of many-to-many translation is limited by the scarcity of parallel data. |
| Approach: | They propose a three-stage curriculum learning strategy that leverages the machine translation capabilities of large language models and adapts them to S2TT tasks. |
| Outcome: | The proposed strategy achieves state-of-the-art average performance in 1514 language pairs, requiring fewer than 10 hours of speech data per language to achieve competitive results. |
Planning Like Human: A Dual-process Framework for Dialogue Planning (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) operate in a reactive mode, often resulting in efficiency issues or suboptimal performance. |
| Approach: | They propose a dual-process dialogue planning framework that leverages the dual-process theory of human cognition and a deliberative Monte Carlo Tree Search mechanism to emulate human-like conversational dynamics. |
| Outcome: | The proposed framework outperforms existing methods in achieving high-quality dialogues and operational efficiency. |
I²B-LPO: Latent Policy Optimization via Iterative Information Bottleneck (2026.acl-long)
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Huilin Deng, Hongchen Luo, Yue Zhu, Long Li, Zhuoyue Chen, Xinghao Zhao, Ming LI, Chuyang Zhao, Jihai Zhang, MengChang Wang, Yang Cao, Yu Kang
| Challenge: | Existing methods for large language model reasoning suffer from exploration collapse due to the semantic homogeneity of random rollouts. |
| Approach: | They propose to use latent policy optimization via iterative information bottleneck to optimize reasoning trajectories by diversifying reasoning . |
| Outcome: | Empirical results show that the proposed method achieves state-of-the-art performance with margins of up to 5.3% in accuracy and 7.4% in diversity metrics. |
VQAGuider: Guiding Multimodal Large Language Models to Answer Complex Video Questions (2025.acl-long)
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| Challenge: | Multimodal large language models (MLLMs) can grasp the intention of a question and decomposing it to a series of visual recognition sub-tasks to find out the answer with the help of an agent. |
| Approach: | They propose a framework for multimodal large language models to grasp the intention of a question and decompose it into a series of visual recognition sub-tasks to find out the answer. |
| Outcome: | The proposed framework improves the accuracy of complex video-related questions by 29.6% and 17.2% on CVQA and the existing VQA datasets. |
GAP: a Global Adaptive Pruning Method for Large Language Models (2025.emnlp-main)
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| Challenge: | Existing structured pruning methods employ uniform compression rates across network layers, neglecting the varying importance of different network depths. |
| Approach: | They propose a pruning framework that minimizes global capability loss by layer-adaptive pruning rates. |
| Outcome: | The proposed approach achieves comparable performance with state-of-the-art methods at high pruning rates and shows significant advantages at low pruning rates. |
RuleR: Improving LLM Controllability by Rule-based Data Recycling (2025.naacl-short)
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| Challenge: | Existing supervised fine-tuning datasets are composed of general instructions without userspecified constraints. |
| Approach: | They propose a data augmentation method incorporating multiple constraints into the original data samples according to predefined rules to create new training tasks. |
| Outcome: | The proposed method improves LLM controllability while maintaining general instruction-following capabilities. |
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems (2020.findings-emnlp)
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| Challenge: | Existing evaluation methods for text summarization systems are limited to in-domain setting, where supervised pre-trained models are evaluated on the same dataset. |
| Approach: | They propose to use a cross-dataset evaluation approach to evaluate different summarization systems in a multi-domain setting. |
| Outcome: | The proposed model can be used to evaluate text summarization systems on different datasets. |
Analyzing the Intensity of Complaints on Social Media (2022.findings-naacl)
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| Challenge: | Prior studies on identifying the existence or the type of complaints focus on building automatic classification models for identifying complaints. |
| Approach: | They propose to measure the intensity of complaints from text using Best-Worst Scaling method to estimate the popularity of posts on social media. |
| Outcome: | The proposed model can estimate the popularity of complaints on social media with best-worst scaling (BWS) method. |
Structure-Aware Zero-Shot Relational Learning for Knowledge Graphs without External Knowledge (2026.findings-acl)
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| Challenge: | Existing methods for Zero-shot Relational Learning depend on external knowledge, resulting in increased annotation costs and limited practical applicability. |
| Approach: | They propose a structure-aware paradigm that performs ZRL without external knowledge . it leverages intrinsic structural patterns in KGs to bridge semantic correlations for new relations with existing ones. |
| Outcome: | The proposed paradigm achieves 10.66% improvement in MRR while reducing annotation costs and enhancing practical applicability on three real-world benchmarks. |
Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction (2020.emnlp-main)
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| Challenge: | Existing methods to improve the efficiency of GEC are not efficient enough for GEC. |
| Approach: | They propose a language-independent approach to improve the efficiency of GEC by dividing the task into two subtasks: ESD and ESC. |
| Outcome: | The proposed approach performs comparably to conventional seq2seq approaches in English and Chinese GEC benchmarks with less than 50% time cost for inference. |
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. |
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning (2024.findings-acl)
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| Challenge: | Instruction tuning is critical to large language models but its success heavily relies on the training data quality. |
| Approach: | They propose a paradigm that synergizes a teacher LLM’s reflection and introspection with the data selection capability of the student LLM to automatically refine existing instruction-tuning data. |
| Outcome: | The proposed method achieves much stronger and top-tier 7B and 13B LLMs without collecting brand-new data. |
Unveiling Inherent Visual Grounding in Multimodal LLMs for Text-Rich Images (2026.findings-acl)
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Shijie Zhou, Jihyung Kil, Ming Li, Jiuxiang Gu, Curtis Wigington, Rajiv Jain, Changyou Chen, Ruiyi Zhang
| Challenge: | Existing multimodal large language model (MLLM) approaches struggle to align query tokens with visual–text patches, heavily relying on lengthy OCR inputs. |
| Approach: | They propose an OCR-free approach that leverages the MLLM's inherent multi-head attention for multi-patch grounding. |
| Outcome: | Empirical results show that the proposed approach outperforms existing approaches on challenging document grounding benchmarks. |
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills (2024.lrec-main)
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| Challenge: | Existing methods to fine-tune code intelligence models to individual tasks are costly and require large data sets. |
| Approach: | They propose a Transferable fine-tuning strategy for Code representation learning that uses a tunable prefix encoder to capture cross-task and cross-language transferable knowledge and apply it to downstream adaptation. |
| Outcome: | The proposed method can lead to superior performance on code-related tasks and encourage mutual reinforcement. |
Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning (2025.findings-acl)
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| Challenge: | Current instruction tuning relies on teacher models or human intervention to generate and refine the instructions and responses for training, which are costly, non-sustainable, and may lack diversity. |
| Approach: | They propose a human/model-free compositional data synthesis method that can create rich and diverse augmentations from existing instruction tuning data to enhance large language models. |
| Outcome: | The proposed method improves performance over benchmarks and reduces training costs by 80% compared with original instruction tuning. |
Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering (2023.findings-acl)
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| Challenge: | Existing knowledge-enhanced methods have trouble obtaining knowledge from different knowledge bases . a concept-centric model can be used to generate a contrastive explanation for QA tasks . |
| Approach: | They propose a Concept-centric Prompt-bAsed Contrastive Explanation Generation model which converts obtained symbolic knowledge into the contrastive explanation for better distinguishing the differences among given candidates. |
| Outcome: | The proposed model achieves new SOTA on CSQA, QASC, and OBQA. |
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. |
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. |
Hybrid-Regressive Paradigm for Accurate and Speed-Robust Neural Machine Translation (2023.findings-acl)
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| Challenge: | Autoregressive translation (NAT) is less robust in decoding batch size and hardware settings than NAT. |
| Approach: | They propose a two-stage translation prototype that prompts a small number of AT predictions and fills in previously skipped tokens at once. |
| Outcome: | The proposed translation prototype achieves comparable translation quality with AT while having 1.5x faster inference speed regardless of batch size and device. |
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 . |
Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization (2025.acl-long)
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| Challenge: | Protein language models pose significant risks of generating harmful sequences, e.g., viral transmissibility, drug resistance, environmental imbalances, public health crises, etc. |
| Approach: | They propose a protein-based model that integrates prior knowledge via a Protein Safety Knowledge Graph to minimize the risk of generating harmful sequences. |
| Outcome: | The proposed framework reduces the likelihood of producing hazardous sequences while maintaining high functionality. |
Deep Attentive Sentence Ordering Network (D18-1)
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| Challenge: | Existing methods for sentence ordering tasks rely on linguistic knowledge and are domain specific. |
| Approach: | They propose a deep attentive sentence ordering network which integrates self-attention mechanism with LSTMs in the encoding of input sentences. |
| Outcome: | The proposed model outperforms the state-of-the-art models on Sentence Ordering and Order Discrimination tasks and is shown to be highly efficient. |
Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization? (2024.emnlp-main)
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| Challenge: | Text summarization is a key natural language generation task, but the high cost of inaccurate summaries raises concerns about the reliability of uncertainty estimation on text summarisation (UE-TS) evaluation methods. |
| Approach: | They propose a UE-TS benchmark that evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets. |
| Outcome: | The proposed benchmark evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets, with human-annotation analysis incorporated where applicable. |
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. |
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding (2023.findings-emnlp)
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| Challenge: | Existing methods for pre-trained language models rely on noisy data, which can be expensive if all parameters are updated. |
| Approach: | They propose a self-training framework that incorporates Monte Carlo dropouts into the model and judiciously selects reliable pseudo-labeled examples based on confidence and certainty. |
| Outcome: | The proposed framework improves performance and efficiency over multiple tasks over multiple datasets. |
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. |
Cognitive Graph for Multi-Hop Reading Comprehension at Scale (P19-1)
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| Challenge: | a new framework for multi-hop reading comprehension question answering is needed to cross the chasm of reading comprehension between machine and human. |
| Approach: | They propose a CogQA framework for multi-hop reading comprehension question answering in web-scale documents that builds a cognitive graph in an iterative process by coordinating an implicit extraction module and an explicit reasoning module. |
| Outcome: | The proposed framework outperforms the best competitor in the hotpotQA dataset in F1 . it provides explainable reasoning paths and accurate answers, while giving accurate answers . |
Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering (P18-1)
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| Challenge: | Existing approaches to read comprehension style question answering are limited by the volume of annotated datasets. |
| Approach: | They propose a hierarchical attention network for reading comprehension style question answering . they first encode the question and paragraph with fine-grained language embeddings . then propose fusion approach to fuse information from both global and attended representations based on the hierarchic attention network . |
| Outcome: | The proposed method achieves state-of-the-art on the SQuAD and TriviaQA Wiki leaderboards and two adversarial SQu AD datasets. |
Breaking the Reasoning Barrier A Survey on LLM Complex Reasoning through the Lens of Self-Evolution (2025.findings-acl)
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Tao He, Hao Li, Jingchang Chen, Runxuan Liu, Yixin Cao, Lizi Liao, Zihao Zheng, Zheng Chu, Jiafeng Liang, Ming Liu, Bing Qin
| Challenge: | OpenAI's O1 and subsequent projects like DeepSeek R1 have significantly advanced research on complex reasoning in LLMs. |
| Approach: | They analyze existing reasoning studies from the perspective of self-evolution and summarize O1-like works from open-source projects like DeepSeek R1 and Kimi-k1.5. |
| Outcome: | The proposed models are based on open-source models and pioneer advanced methodologies like Scaling Reinforcement Learning (RL). |
GLGE: A New General Language Generation Evaluation Benchmark (2021.findings-acl)
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Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan
| Challenge: | Multi-task benchmarks focus on a range of Natural Language Understanding (NLU) tasks without considering the Natural Language Generation (NLG) models. |
| Approach: | They propose a multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks. |
| Outcome: | The proposed benchmarks are based on GLUE and Su-perGLUE for English and several other languages. |
ATLAS: Agent Tuning via Learning Critical Steps (2025.findings-acl)
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| Challenge: | Existing agent tuning approaches employ supervised finetuning on entire expert trajectories, but behavior-cloning of full traitories introduces expert bias and weakens generalization to states not covered by the expert data. |
| Approach: | They propose a method that finetunes LLMs on critical steps in expert trajectories and identifies and finetuns them on these steps with reduced costs. |
| Outcome: | The proposed method outperforms existing methods and open-source LLM agents on only 30% critical steps in extensive experiments. |
How do Language Models Reshape Entity Alignment? A Survey of LM-Driven EA Methods: Advances, Benchmarks, and Future (2025.emnlp-main)
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| Challenge: | Entity alignment (EA) is critical for knowledge graph (KG) integration. |
| Approach: | They propose a taxonomy that categorizes methods in three stages: data preparation, feature embedding, and alignment. |
| Outcome: | The proposed taxonomy categorizes methods in three key stages: data preparation, feature embedding, and alignment. |
SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)
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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin
| Challenge: | Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation. |
| Approach: | They propose an adaptive acceleration framework which prunes redundant token representations and attention heads within each layer of the original model. |
| Outcome: | The proposed framework accelerates the original model by 2-3 times with minimal performance degradation across vision-language tasks. |
Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)
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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton
| Challenge: | Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options. |
| Approach: | They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations . |
| Outcome: | The proposed model outperforms human experts in multiple medical tasks. |
Learning Decoupled Retrieval Representation for Nearest Neighbour Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing methods to integrate external corpus are sparse in practical applications, and noises in low similarity retrieval could lead to severe performance degradation. |
| Approach: | They propose a method to integrate external corpus into k-nearest neighbor machine translation (kNNMT) instead of storing discrete word sequence, kNN-MT uses a pre-trained NMT model to force decoding the external corpi. |
| Outcome: | The proposed approach improves retrieval accuracy and BLEU score on five domains compared to vanilla kNNMT. |
Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality (2026.findings-acl)
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Qipeng Xie, Zi Liang, Jiafei Wu, Yufei Chen, Weizheng Wang, Wenao Ma, Zhong Ming, Haiqin Yang, Kaishun Wu
| Challenge: | Existing studies on prompt engineering have focused on optimizing models for performance under stylistic perturbations. |
| Approach: | They conduct the first analysis of n-gram token-level mechanisms . they find that higher average performance is inherently associated with lower variance and greater stability. |
| Outcome: | The proposed model reduces the variance of the generated code by 40% . the proposed model is based on a large-scale dataset of 132,000 prompt variants . |
BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving (2025.acl-long)
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| Challenge: | Existing approaches to theorem proving in large language models rely on value functions and/or Monte Carlo Tree Search (MCTS), but the potential of simpler methods like Best-First Tree Search remains underexplored. |
| Approach: | They propose a scalable expert iteration framework that implements strategic data filtering at each expert iteration round, excluding problems solvable via beam search node expansion to focus on harder cases. |
| Outcome: | The proposed framework achieves a state-of-the-art score of 72.95 on the MiniF2F test set and challenges the perceived necessity of complex tree search methods. |
TS-CLIP: Time Series Understanding by CLIP (2025.emnlp-main)
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| Challenge: | Contrastive Language–Image Pre-training (CLIP) has demonstrated remarkable success in aligning vision and language. |
| Approach: | They propose a synonym bank mechanism that generates synonym embeddings as alignment targets. |
| Outcome: | The proposed approach achieves state-of-the-art (SOTA) performance on 51 datasets. |
MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding (2026.acl-long)
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Fuwen Luo, Shengfeng Lou, Chi Chen, Ziyue Wang, Chenliang Li, Weizhou Shen, Jiyue Guo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu
| Challenge: | Existing methods for MLLMs struggle with fine-grained temporal reasoning . despite advances in video understanding, current methods struggle with time-sensitive tasks . |
| Approach: | They propose a time-stamp-aware multi-segment grounding method that enhances temporal understanding by introducing timestamps. |
| Outcome: | The proposed method outperforms existing methods on time-sensitive tasks and generalizes well across diverse temporal understanding scenarios. |
How Do Large Language Models Perform in Dynamical System Modeling (2025.findings-naacl)
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| Challenge: | Recent data-driven methods often use graph neural networks (GNNs) to learn interactions between objects. |
| Approach: | They propose prompting techniques for dynamical system modeling and evaluate their performance . they find that large language models demonstrate competitive performance without training . |
| Outcome: | The proposed methods show competitive performance without training compared to state-of-the-art methods in dynamical system modeling. |
Alleviating Over-smoothing for Unsupervised Sentence Representation (2023.acl-long)
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| Challenge: | Existing approaches to learn better unsupervised sentence representations have been successful . over-smoothing problem in unsupervised sentences reduces the capacity of powerful PLMs . |
| Approach: | They propose a method to solve the over-smoothing problem in unsupervised sentence representations by combining negatives from PLMs intermediate layers. |
| Outcome: | The proposed method improves on different strong baselines on Semantic Textual Similarity and Transfer datasets. |
A Reinforcement Learning Approach to Improve Low-Resource Machine Translation Leveraging Domain Monolingual Data (2024.lrec-main)
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| Challenge: | Existing methods for fine-tuning domain adaptation have overfitting problem in low-resource domains . lack of parallel data makes it difficult for model to learn domain-specific knowledge . |
| Approach: | They propose a Reinforcement Learning Domain Adaptation method for Neural Machine Translation that uses in-domain source monolingual data to make up for the lack of parallel data. |
| Outcome: | The proposed method can alleviate overfitting and reinforce the model to learn domain-specific knowledge. |
Text2World: Benchmarking Large Language Models for Symbolic World Model Generation (2025.findings-acl)
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Mengkang Hu, Tianxing Chen, Yude Zou, Yuheng Lei, Qiguang Chen, Ming Li, Yao Mu, Hongyuan Zhang, Wenqi Shao, Ping Luo
| Challenge: | Recent studies have encountered limitations in leveraging large language models to generate symbolic world models. |
| Approach: | They propose a benchmarking framework based on planning domain definition language (PDDL) that employs multi-criteria, execution-based metrics for a more robust evaluation. |
| Outcome: | The proposed model outperforms models trained with large-scale reinforcement learning, but lacks the robustness needed to perform in world modeling. |
Enhancing Multilingual Reasoning via Steerable Model Merging (2026.findings-acl)
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Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu, Zhijun Chen, Qianren Mao, Hongcheng Guo, Jiaheng Liu, Likang Xiao, Ming LI, Xiaojie Wang
| Challenge: | Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. |
| Approach: | They propose a model merging framework that modulates the contribution of each source model. |
| Outcome: | Experiments show that the proposed model merging framework outperforms strong baselines on multilingual reasoning benchmarks across 21 different languages. |
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)
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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou
| Challenge: | XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios . |
| Approach: | They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora. |
| Outcome: | The proposed dataset is labeled in English and includes only natural language understanding tasks. |
Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection via Querying ChatGPT (2023.emnlp-main)
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Biru Zhu, Lifan Yuan, Ganqu Cui, Yangyi Chen, Chong Fu, Bingxiang He, Yangdong Deng, Zhiyuan Liu, Maosong Sun, Ming Gu
| Challenge: | Large language models (LLMs) are capable of performing tasks but are likely to be misused. |
| Approach: | They propose a zero-shot black-box method to detect LLM-generated texts . they revise the text to be detected using the ChatGPT model . |
| Outcome: | The proposed method can detect LLM-generated texts with a zero-shot black-box model . it is based on intuition that the model will make fewer revisions to LLMs than to human-written texts . |
Self-Critique Guided Iterative Reasoning for Multi-hop Question Answering (2025.findings-acl)
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Zheng Chu, Huiming Fan, Jingchang Chen, Qianyu Wang, Mingda Yang, Jiafeng Liang, Zhongjie Wang, Hao Li, Guo Tang, Ming Liu, Bing Qin
| Challenge: | Large language models (LLMs) have demonstrated remarkable reasoning capabilities, but they still face challenges in knowledge-intensive multi-hop reasoning. |
| Approach: | They propose a method that uses self-critique feedback to guide iterative reasoning by enabling iteration and self-evaluation of its intermediate reasoning steps. |
| Outcome: | The proposed method surpasses the previous SOTA by 8.6% on three multi-hop reasoning datasets. |
Dense Procedure Captioning in Narrated Instructional Videos (P19-1)
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| Challenge: | Existing models for video dense captioning learn video segments and generate captions without considering transcripts. |
| Approach: | They propose a model to generate procedure captions from narrated instructional videos . they extract procedures by a cross-modality module and generate captions by encoding video frames and transcripts within each extracted procedure. |
| Outcome: | The proposed model can extract procedures from narrated instructional videos and generate procedure captions by encoding video frames and transcripts. |
Structural Contrastive Pretraining for Cross-Lingual Comprehension (2023.findings-acl)
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| Challenge: | Existing methods to train multilingual language models using pretraining tasks like mask language modeling have yielded promising results on a wide range of downstream tasks. |
| Approach: | They propose a new task to align the structural words in a parallel sentence, enhancing models’ ability to comprehend cross-lingual representations. |
| Outcome: | The proposed task improves model's ability to comprehend cross-lingual representations by increasing the frequency of negative pairings. |
Extractive Summarization as Text Matching (2020.acl-main)
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| Challenge: | Currently, most of the neural extractive summarization systems score and extract sentences individually and model the relationship between sentences. |
| Approach: | They propose to instantiate a neural extractive summarization task as a semantic text matching problem and use it to match a source document and candidate summaries in a semantic space. |
| Outcome: | The proposed framework is faster and more efficient than existing frameworks. |
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)
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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo
| Challenge: | Existing research on reinforcement learning for LLMs under data scarcity has not been unified. |
| Approach: | They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric. |
| Outcome: | The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area. |
Triangular Architecture for Rare Language Translation (P18-1)
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| Challenge: | Empirical results show that Neural Machine Translation (NMT) performs poor on low-resource pairs especially when Z is a rare language. |
| Approach: | They propose a triangular triangulation technique to leverage bilingual data to optimize the translation performance of low-resource pairs. |
| Outcome: | Empirical results show that the proposed architecture significantly improves translation quality of rare languages on MultiUN and IWSLT2012 datasets and even better when combining back-translation methods. |
Analytical Reasoning of Text (2022.findings-naacl)
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Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan
| Challenge: | Existing models with implicit reasoning ability struggle to solve analytical reasoning of text. |
| Approach: | They propose an approach to analyze text and use it to perform reasoning over it. |
| Outcome: | The proposed approach outperforms pre-trained models on an analysis of the Law School Admission Test dataset. |
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations (2024.findings-emnlp)
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| Challenge: | Existing research focuses on developing powerful large language models for mathematical reasoning within monolingual languages. |
| Approach: | They propose to use translation to build powerful multilingual math reasoning models . they propose different training strategies to build xMR LLMs that outperform open-source LLM . |
| Outcome: | The proposed model outperforms open-source LLMs and surpasses ChatGPT in few-shot scenarios. |
SPEAK: Spiking Neurons as an Entropy-Aware Tokenizer for Large Language Models (2026.acl-long)
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| Challenge: | Existing tokenizers fail to explicitly leverage historical tokenization results . large language models (LLMs) have demonstrated remarkable effectiveness across NLP tasks . |
| Approach: | They propose a tokenizer that integrates spiking neurons to explicitly leverage historical tokenization results. |
| Outcome: | The proposed tokenizer leverages historical tokenization results, but does not selectively leverage history based on contextual relevance. |
FashionKLIP: Enhancing E-Commerce Image-Text Retrieval with Fashion Multi-Modal Conceptual Knowledge Graph (2023.acl-industry)
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Xiaodan Wang, Chengyu Wang, Lei Li, Zhixu Li, Ben Chen, Linbo Jin, Jun Huang, Yanghua Xiao, Ming Gao
| Challenge: | Recent advances in visual-language pre-trained (VLP) models have greatly improved cross-modal retrieval performance . however, the fine-grained interactions between objects from different modalities are far from well-established . e-commerce domain lacks sufficient training data and fine-granular cross-modulal knowledge . |
| Approach: | They propose a visual-language pre-trained (VLP) image-text retrieval model that integrates cross-modal knowledge into the model to improve performance. |
| Outcome: | The proposed model improves performance on e-commerce image-text retrieval task by a large margin. |
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) are a promising new approach to understanding biological sequences such as proteins. |
| Approach: | They propose an LLM that can generate protein sequences in human and protein languages by pre-training an Lm on protein and natural language corpora and supervised instruction tuning to facilitate alignment. |
| Outcome: | The proposed model outperforms state-of-the-art LLMs on protein-text generation tasks by a large margin. |
MT2: Towards a Multi-Task Machine Translation Model with Translation-Specific In-Context Learning (2023.emnlp-main)
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| Challenge: | Sentence-level translation, document-level and terminology constrained translations are important in machine translation. |
| Approach: | They propose a multi-task machine translation model that integrates translation memory sentences . they propose 'in-context learning' paradigm that allows translation-specific context learning . |
| Outcome: | The proposed model improves translation memory, document-level translation, and document-constrained translation tasks. |
Structure-aware Fine-tuning for Code Pre-trained Models (2024.lrec-main)
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| Challenge: | Existing CodePTMs are mainly structure-free and structurebased, but how to fine-tune them remains a challenge. |
| Approach: | They propose a plug-and-play fine-tuning method that incorporates structural knowledge into pre-trained code models. |
| Outcome: | The proposed method can benefit CodePTMs more with limited training data. |
TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models (2024.acl-long)
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| Challenge: | Grasping the concept of time is a fundamental facet of human cognition. |
| Approach: | They propose a hierarchical temporal reasoning benchmark that covers a broad spectrum of temporal phenomena. |
| Outcome: | The proposed benchmark shows that state-of-the-art LLMs are still far behind humans in temporal reasoning . |
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)
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Yulong Chen, Huajian Zhang, Yijie Zhou, Xuefeng Bai, Yueguan Wang, Ming Zhong, Jianhao Yan, Yafu Li, Judy Li, Xianchao Zhu, Yue Zhang
| Challenge: | Existing work on cross-lingual summarization (CLS) does not consider crosslingual sources for summarizing. |
| Approach: | They propose a cross-lingual conversation summarization benchmark that explicitly considers source context. |
| Outcome: | The proposed method surpasses baselines on ConvSumX and 3 widely-used manual annotations. |
Towards Knowledge-Based Recommender Dialog System (D19-1)
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| Challenge: | Existing frameworks that only provide information about user preferences can be inaccurate in e-commerce recommender systems. |
| Approach: | They propose a framework which integrates the recommender system and dialog generation system by introducing information about users’ preferences. |
| Outcome: | The proposed framework can achieve better performance in both dialog generation and recommendation compared with baselines. |
Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (2024.acl-long)
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Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu
| Challenge: | Recent studies have revealed that chain-of-thought prompting significantly enhances LLM’s reasoning capabilities, which attracts widespread attention from both academics and industry. |
| Approach: | They propose to summarize advanced methods through a taxonomy that offers novel perspectives. |
| Outcome: | The proposed method delineates the challenges and future directions, thereby shedding light on future research. |
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)
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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si
| Challenge: | Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment. |
| Approach: | They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. |
| Outcome: | The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering. |
Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction (2026.findings-acl)
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| Challenge: | Accurate estimation of item (question or task) difficulty suffers from the cold start problem. |
| Approach: | They propose to use large-scale empirical analysis to examine human-AI Difficulty Alignment . they find that models struggle to simulate the capability limitations of students . |
| Outcome: | The proposed model size is not reliably helpful for human-AI alignment . high performance often impedes accurate difficulty estimation, the authors say . |
Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking (2024.findings-acl)
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| Challenge: | Chinese Spell Checking (CSC) is a widely used technology for speech to text and optical character recognition. |
| Approach: | They propose to use Chinese rich semantic information to introduce large language models as the foundation model. |
| Outcome: | The proposed framework performs better on few-shot CSC task than existing methods. |
Pass-Tuning: Towards Structure-Aware Parameter-Efficient Tuning for Code Representation Learning (2023.findings-emnlp)
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| Challenge: | Code pre-trained models have been proposed and widely applied in the domain of code intelligence. |
| Approach: | They propose a method that uses a plug-and-play graph neural network module as a tunable prefix to exploit structural information of source code. |
| Outcome: | The proposed method exploits structural information of source code and could replace full fine-tuning. |
Towards Faithful Multi-step Reasoning through Fine-Grained Causal-aware Attribution Reasoning Distillation (2025.coling-main)
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| Challenge: | Recent advances have witnessed large language models (LLMs) achieving significant milestones across various domains of natural language processing. |
| Approach: | They introduce fine-grained attribution reasoning distillation (FARD) which incorporates grounded citations to consolidate the relationships between reasoning steps. |
| Outcome: | The proposed method outperforms CoT distillation methods on mathematical and general reasoning benchmarks. |
MIND: A Large-scale Dataset for News Recommendation (2020.acl-main)
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Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou
| Challenge: | Personalized news recommendation is an important technique for personalized news service. |
| Approach: | They propose to build a large-scale news recommendation dataset from Microsoft News . they demonstrate that news recommendation relies on the quality of news content understanding . |
| Outcome: | The proposed dataset contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body. |
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure (2022.findings-emnlp)
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| Challenge: | Existing code pre-trained models fail to consider inherent characteristics of codes . Existing methods to interpret code pretrained model fail to take into account inherent characteristics . |
| Approach: | They propose a probing method to quantitatively interpret how CodePTMs attend code structure. |
| Outcome: | The proposed method denoises input code sequences and measures commonality between token-level attention scores and pair-wise distances between corresponding AST nodes. |
ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research (2025.acl-industry)
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| Challenge: | Large Language Models (LLMs) have shown promising results in various domains, but their practical application in industry-relevant operations research presents significant challenges and opportunities. |
| Approach: | They propose a cognitive-inspired framework that enhances optimization through counterfactual reasoning . they use a workflow that transforms requirements into mathematical models and executable solver code . |
| Outcome: | Experiments show that ORMind outperforms existing methods in the NL4Opt dataset and ComplexOR dataset. |
QA‐LIGN: Aligning LLMs through Constitutionally Decomposed QA (2025.findings-emnlp)
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Jacob Dineen, Aswin Rrv, Qin Liu, Zhikun Xu, Xiao Ye, Ming Shen, Zhaonan Li, Shijie Lu, Chitta Baral, Muhao Chen, Ben Zhou
| Challenge: | QA-LIGN decomposes monolithic rewards into interpretable principle-specific evaluations . scalar rewards obscure which objectives drive the training signal . |
| Approach: | a new method decomposes monolithic rewards into interpretable principle-specific evaluations . QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate . |
| Outcome: | QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate . the results outperform DPO and GRPO with state-of-the-art reward models given equivalent training . |
xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)
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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Autonomous agents powered by large language models (LLMs) have attracted significant research interest, but there are few standards for developing specialized models for agent tasks. |
| Approach: | They propose a series of large action models with dense and mixture-of-expert architectures that unifies, augments, and synthesizes diverse datasets to enhance agent generalizability and performance. |
| Outcome: | The proposed models outperform GPT-4, Claude-3, and many other models in terms of tool use and outperformed GPT-based models on multiple agent ability benchmarks. |
Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features (N19-2)
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Wei Yang, Luchen Tan, Chunwei Lu, Anqi Cui, Han Li, Xi Chen, Kun Xiong, Muzi Wang, Ming Li, Jian Pei, Jimmy Lin
| Challenge: | e-commerce companies often have the option of escalating complaints by filing grievances with a government authority . this is detrimental to an ecommerce company, but this problem is challenging to solve by integrating recurrent neural networks with manually-engineered features. |
| Approach: | They propose a model that integrates recurrent neural networks with manually-engineered features to identify cases where the customer expresses such an intent. |
| Outcome: | The proposed model outperforms baseline models and provides better recall and triage for specialized agents. |
How Does the Disclosure of AI Assistance Affect the Perceptions of Writing? (2024.emnlp-main)
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| Challenge: | Recent advances in generative AI technologies like large language models have boosted the incorporation of AI assistance in writing workflows. |
| Approach: | They conduct an experimental study to determine whether disclosure of AI assistance in the writing process would affect people's evaluation on the quality of the writing and ranking of different writings. |
| Outcome: | The disclosure of AI assistance decreases the average quality ratings for argumentative essays and creative stories, and increases the quality of the writings. |
Conjoin after Decompose: Improving Few-Shot Performance of Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing prompt-based NER models fail to detect entity boundaries, causing performance degradation. |
| Approach: | They propose a model which consists of a BART encoder and a parabiotic decoder and propose ' boundary expansion strategy' to enhance the model's capability in entity type classification. |
| Outcome: | The proposed model can achieve significant performance gains over state-of-the-art models. |
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering (2024.acl-long)
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Zheng Chu, Jingchang Chen, Qianglong Chen, Haotian Wang, Kun Zhu, Xiyuan Du, Weijiang Yu, Ming Liu, Bing Qin
| Challenge: | Large language models (LLMs) have demonstrated strong reasoning capabilities, but they still suffer from factual errors when tackling knowledge-intensive tasks. |
| Approach: | They propose a reasoning framework for knowledge-intensive multi-hop QA that prioritizes promising answers at each hop of question. |
| Outcome: | The proposed framework outperforms SOTA methods on four open-domain multi-hop reasoning datasets by 8.5%. |
Semi-supervised Fine-tuning for Large Language Models (2025.findings-naacl)
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| Challenge: | Existing LLMs require labeled data, which can be costly in real-world applications. |
| Approach: | They propose a framework that can fully exploit labeled and unlabeled data for LLM fine-tuning . they conducted experiments using GPT-4o-mini and Llama-3.1 on seven general or domain-specific datasets . |
| Outcome: | The proposed framework can fully exploit labeled and unlabeled data for LLM alignment from a propagate-and-select manner. |
When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario (2023.findings-acl)
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| Challenge: | Large pre-trained language models (PLMs) are expensive and may not be open-sourced due to commercial considerations and potential risks of misuse. |
| Approach: | They propose to introduce gradient descent into black-box tuning scenario . they propose a method which integrates gradient descent and derivative-free optimization . |
| Outcome: | The proposed method achieves significant performance gains over previous state-of-the-art methods. |
Fine-tune BERT with Sparse Self-Attention Mechanism (D19-1)
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| Challenge: | Existing sparse self-attention fine-tuning models have been used to improve sentiment analysis, question answering, and natural language inference tasks. |
| Approach: | They propose a Sparse Self-Attention Fine-tuning model which integrates sparsity into self-attention mechanism to enhance the fine-tune performance of BERT. |
| Outcome: | The proposed model outperforms the baseline models on sentiment analysis, question answering, and natural language inference tasks and is able to interpret the input better. |
MCC-KD: Multi-CoT Consistent Knowledge Distillation (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable abilities in complex reasoning through chain of thought (CoT) prompting. |
| Approach: | They propose to generate multiple rationales for each question and enforce consistency among their predictions by minimizing the bidirectional KL-divergence between the answer distributions. |
| Outcome: | The proposed model achieves superior performance on in-distribution and commonsense reasoning benchmarks. |
Profiling LLM’s Copyright Infringement Risks under Adversarial Persuasive Prompting (2025.findings-emnlp)
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| Challenge: | Large Language Models have demonstrated impressive capabilities in text generation but raise concerns regarding potential copyright infringement. |
| Approach: | They propose a structured persuasion workflow to analyze the influence of persuasive prompts on LLM outputs. |
| Outcome: | The proposed method analyzes the influence of persuasive prompts on LLM outputs. |
MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation (2025.naacl-long)
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Jinsheng Huang, Liang Chen, Taian Guo, Fu Zeng, Yusheng Zhao, Bohan Wu, Ye Yuan, Haozhe Zhao, Zhihui Guo, Yichi Zhang, Jingyang Yuan, Wei Ju, Luchen Liu, Tianyu Liu, Baobao Chang, Ming Zhang
| Challenge: | Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, but many benchmarks suffer from systematic biases. |
| Approach: | They propose a benchmark to avoid Type-I errors by creating one perception question and one knowledge anchor question through a meticulous annotation process. |
| Outcome: | The proposed benchmark avoids Type-I errors while maintaining reliability of MCQ evaluations. |
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information (2025.coling-main)
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Yuxin Wang, MingHua Ma, Zekun Wang, Jingchang Chen, Shan Liping, Qing Yang, Dongliang Xu, Ming Liu, Bing Qin
| Challenge: | Existing LLM pruning works focus on unstructured pruning, which typically requires special hardware support for a practical speed-up. |
| Approach: | They propose a network pruning framework that leverages both coarse and fine-grained activation information as an importance criterion to guide pruning. |
| Outcome: | The proposed framework outperforms existing pruning methods on diverse models across sparsity budgets. |
Graph Neural News Recommendation with Unsupervised Preference Disentanglement (2020.acl-main)
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| Challenge: | Existing methods to learn informative user and news representations fail to consider high-order connectivity underlying the user-news interactions. |
| Approach: | They propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement which can encode high-order relationships into user and news representations by information propagation along the graph. |
| Outcome: | The proposed model can encode high-order relationships into user and news representations by information propagation along the graph and disentangle latent preference factors by a neighborhood routing algorithm. |
Infrared-LLaVA: Enhancing Understanding of Infrared Images in Multi-Modal Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing methods for infrared modeling ignore supervisory signals of infra-modality-specific attributes, which may lead to biased understanding of in-frarea images. |
| Approach: | They propose a multi-agent generation system which transfers knowledge from visible images to generate infrared image-text pairs and infra-instructional data. |
| Outcome: | The proposed system generates infrared image-text pairs and infra-response data and is able to answer common infreas tasks with the proposed model. |
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space (2020.emnlp-main)
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| Challenge: | Existing data augmentation techniques for natural language processing tasks are difficult to design. |
| Approach: | They propose a controllable rewriting based question data augmentation method for machine reading comprehension, question generation and question-answering natural language inference tasks. |
| Outcome: | The proposed method generates high-quality, high-quality question data samples on machine reading comprehension, question generation, and question-answering natural language inference tasks. |
FuseSearch: Learning Adaptive Parallel Execution for Efficient Code Localization (2026.findings-acl)
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Ke Xu, Siyang Xiao, Ming Liang, Yichen Yu, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li
| Challenge: | Existing parallel code localization agents suffer from a 34.9% redundant tool invocation rate . specialized localization agent that operate as dedicated search components is needed to achieve high localization accuracy. |
| Approach: | They propose a parallel code localization system that reframes parallel code execution as a quality–efficiency co-optimization problem. |
| Outcome: | The proposed method matches SOTA performance while being 93.6% faster. |