Papers by Qin Chen
TeleMelody: Lyric-to-Melody Generation with a Template-Based Two-Stage Method (2022.emnlp-main)
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Zeqian Ju, Peiling Lu, Xu Tan, Rui Wang, Chen Zhang, Songruoyao Wu, Kejun Zhang, Xiang-Yang Li, Tao Qin, Tie-Yan Liu
| Challenge: | a new lyric-to-melody generation system bridges the gap between lyrics and melodies . previous generation systems lack paired data and lack of control on generated melodie. |
| Approach: | They develop a lyric-to-melody generation system with music template to bridge the gap between lyrics and melodies. |
| Outcome: | The proposed system bridges the gap between lyrics and melodies by using music template. |
Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning (2026.acl-long)
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| Challenge: | Recent reinforcement learning approaches have advanced radiology report generation (RRG) however, there are two limitations: report-level rewards offer limited evidence-grounded guidance for clinical faithfulness . |
| Approach: | They propose a method that uses group-wise evidence-aware alignment rewards and self-correcting preference learning to build a reliable, disease-agnostic preference dataset without human supervision. |
| Outcome: | ESC-RL promotes clinically faithful, disease-aligned reward and supports continual self-improvement during training. |
Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations (2024.findings-emnlp)
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| Challenge: | Existing CRSs can be highly persuasive, but they can be deceptive and can damage the long-term trust between users and the CRS. |
| Approach: | They propose a method to enhance the credibility of CRS’s explanations by using a set of credibility-aware persuasive strategies and a post-hoc self-reflection process. |
| Outcome: | The proposed method enhances the credibility of CRS’s explanations and refines them via post-hoc self-reflection. |
Lifelong Learning of Topics and Domain-Specific Word Embeddings (2021.findings-acl)
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| Challenge: | Existing lifelong topic models focus on indomain text streams in which each chunk only contains documents from a single domain. |
| Approach: | They develop a lifelong collaborative model that uses non-negative matrix factorization to learn topics and domain-specific word embeddings. |
| Outcome: | The proposed model can learn topics and domain-specific word embeddings from a lifelong collaborative model. |
Make Your Decision Convincing! A Unified Two-Stage Framework: Self-Attribution and Decision-Making (2023.findings-emnlp)
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| Challenge: | Existing frameworks for explaining black-box model behavior are unreliable . large-scale pre-trained models often rely on superficial clues for predictions . |
| Approach: | They propose a unified two-stage framework that uses subsequences from the input text as a rationale to generate model decision. |
| Outcome: | The proposed framework achieves competitive results on five reasoning datasets and in semi-supervised scenarios. |
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge (D19-1)
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| Challenge: | Recent neural models for data-to-text generation rely on parallel pairs of data and text to learn writing knowledge. |
| Approach: | They propose to enhance neural models with external knowledge to improve fidelity of generated text. |
| Outcome: | The proposed model improves on Wikipedia infobox-to-text datasets on 21 datasets. |
CogBERT: Cognition-Guided Pre-trained Language Models (2022.coling-1)
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| Challenge: | Existing methods fine-tune pre-trained models on cognitive data, ignoring the semantic gap between texts and cognitive signals. |
| Approach: | They propose a framework that can induce fine-grained cognitive features from cognitive data and incorporate them into pre-trained language models by adaptively adjusting the weight of cognitive features for different NLP tasks. |
| Outcome: | The proposed framework can induce fine-grained cognitive features from cognitive data and incorporate them into BERT by adaptively adjusting weight of cognitive features for different NLP tasks. |
A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder (2024.lrec-main)
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| Challenge: | Existing methods for information extraction (IE) focus on training task-specific models, while common knowledge among different IE tasks is not explicitly modeled. |
| Approach: | They propose a regularization-based transfer learning method for IE via an instructed graph decoder which decodes various complex structures into a graph uniformly based on corresponding instructions. |
| Outcome: | The proposed method can learn common knowledge from existing datasets and transfer it to a new dataset with new labels. |
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation (2025.findings-acl)
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| Challenge: | Experimental evaluations on NQ, TriviaQA, and HotpotQA datasets demonstrate that our approach achieves a 90% reduction in retrieval time compared to conventional methods while maintaining considerate recall performance. |
| Approach: | They propose a framework that integrates deep hashing techniques with systematic optimizations to address these limitations. |
| Outcome: | The proposed framework outperforms retrieval/non-retrieval baselines by 1.4-4.3% in EM scores on NQ, TriviaQA, and HotpotQA datasets. |
Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations (2025.findings-naacl)
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| Challenge: | Existing studies on backdoor defense have focused on training phase, overlooking critical aspect of testing time defense. |
| Approach: | They propose to use demonstrations as a defense mechanism against backdoor attacks in black-box LLMs. |
| Outcome: | The proposed method outperforms existing defense baselines across most evaluation scenarios. |
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. |
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation (2024.acl-long)
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Kun Zhu, Xiaocheng Feng, Xiyuan Du, Yuxuan Gu, Weijiang Yu, Haotian Wang, Qianglong Chen, Zheng Chu, Jingchang Chen, Bing Qin
| Challenge: | Experimental results show that retrieval-augmented generation improves accuracy and relevance of large language models. |
| Approach: | They propose to introduce the information bottleneck theory into retrieval-augmented generation by maximizing mutual information between compression and ground output while minimizing mutual information . |
| Outcome: | The proposed approach improves accuracy and correctness of answer generation and conciseness with 2.5% compression rate. |
Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing studies focus on improving the performance of domain-specific models based on the target dataset. |
| Approach: | They propose a Large Language Model-based Continual Learning (LLM-CL) model for ABSA that learns the target domain’s ability while maintaining the history domains’ abilities. |
| Outcome: | The proposed model obtains new state-of-the-art over 19 datasets. |
POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering (2026.acl-long)
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| Challenge: | Existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences. |
| Approach: | They propose a multilingual chart question answering benchmark that enables efficient multilingual generation via data translation and code reuse. |
| Outcome: | The proposed benchmark systematically evaluates multilingual chart understanding on state-of-the-art LVLMs and shows a significant performance gap between English and other languages. |
AS-ES Learning: Towards efficient CoT learning in small models (2024.findings-acl)
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| Challenge: | Existing methods to induce Chain-of-Thought (CoT) in LLMs are limited and do not consider the importance of efficiently utilizing existing CoT data. |
| Approach: | They propose a new training paradigm which exploits the inherent information in CoT for iterative generation. |
| Outcome: | The proposed training paradigm surpasses direct seq2seq training on CoT-extensive tasks without data augmentation or altering the model itself. |
ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration (2025.findings-acl)
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Xinyi Jiang, Tianyi Hu, Yuheng Qin, Guoming Wang, Zhou Huan, Kehan Chen, Gang Huang, Rongxing Lu, Siliang Tang
| Challenge: | Existing methods for enhancing dialogue performance rely on summarizing behavior . e-commerce chatbots need to align their dialogue strategies with human behavior to achieve coherent, human-like conversations with customers. |
| Approach: | They propose a method to extract core patterns from dialogue data and integrate them into models by mining service thought processes using a multi-agent aPproach. |
| Outcome: | The proposed method outperforms manual methods and outperfies baselines on Taobao in China. |
SimulSpeech: End-to-End Simultaneous Speech to Text Translation (2020.acl-main)
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| Challenge: | SimulSpeech is an end-to-end simultaneous speech to text translation system . conventional approaches to simultaneous speech translation divide the translation process into two stages . |
| Approach: | They develop an end-to-end simultaneous speech to text translation system which translates speech in source language to text in target language concurrently. |
| Outcome: | The proposed system achieves reasonable BLEU scores and lower delay compared to full-sentence translation model. |
Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering (2025.acl-long)
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| Challenge: | Existing retrieval-augmented generation methods are insufficient for multi-hop question answering . however, they tend to generate hallucinations due to semantic mismatching . |
| Approach: | They propose to optimize question semantic space for dynamic retrieval-augmented multi-hop question answering by optimizing the semantic embeddings. |
| Outcome: | The proposed method outperforms existing RAG methods in both in- and out-of-domain settings. |
Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Retrieval-augmented generation (RAG) improves large language models by incorporating non-parametric knowledge through evidence retrieved from external sources. |
| Approach: | They propose a training-free evidence compression technique that makes retrieved evidence more familiar to the target model while seamlessly integrating parametric knowledge from the model. |
| Outcome: | The proposed technique outperforms the most recent evidence compression baselines across open-domain QA datasets while achieving high compression rates. |
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. |
RedCoder: Automated Multi-Turn Red Teaming for Code LLMs (2026.acl-long)
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Wenjie Jacky Mo, Qin Liu, Xiaofei Wen, Dongwon Jung, Hadi Askari, Wenxuan Zhou, Zhe Zhao, Muhao Chen
| Challenge: | Existing red-teaming approaches for code generation rely on extensive human effort and are prone to generating malicious code under adversarial environments. |
| Approach: | They propose a red-teaming agent that engages victim models in multi-turn conversations to elicit vulnerable code. |
| Outcome: | Experiments show that RedCoder outperforms red-teaming methods in inducing vulnerabilities in code generation. |
InfoMetIC: An Informative Metric for Reference-free Image Caption Evaluation (2023.acl-long)
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| Challenge: | Existing image captioning metrics provide a single score to measure caption qualities, which are less explainable and informative. |
| Approach: | They propose an Informative Metric for Reference-free Image Caption evaluation to support this feedback . they propose to provide a text precision score, a vision recall score and an overall quality score . |
| Outcome: | The proposed method improves on existing metrics on multiple benchmarks and compares coarse-grained scores with human judgements. |
E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models (2022.coling-1)
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Ling Ge, ChunMing Hu, Guanghui Ma, Junshuang Wu, Junfan Chen, JiHong Liu, Hong Zhang, Wenyi Qin, Richong Zhang
| Challenge: | Empirical studies show that our approach outperforms the SOTA methods in improving the interpretability of text classification models. |
| Approach: | They propose an enhanced variational word masks approach that exploits the Variational Information Bottleneck to obtain task-specific words. |
| Outcome: | Empirical results show that the proposed method outperforms the SOTA methods in improving the interpretability of the model. |
Retrieving Multimodal Information for Augmented Generation: A Survey (2023.findings-emnlp)
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Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, Shafiq Joty
| Challenge: | Large Language Models (LLMs) are increasingly using multimodality to augment their generation ability, but there is no unified perception of at which stage and how to incorporate different modalities. |
| Approach: | They propose to use multimodality to augment Large Language Models (LLMs) this will provide scholars with a deeper understanding of the methods' applications and encourage them to adapt existing techniques to the fast-growing field of LLMs. |
| Outcome: | The proposed methods improve factuality, reasoning, interpretability, and robustness of the generated content. |
An Empirical Study of Sentiment-Enhanced Pre-Training for Aspect-Based Sentiment Analysis (2023.findings-acl)
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| Challenge: | Existing approaches to Aspect-Based Sentiment Analysis (ABSA) are lacking in a comprehensive evaluation and fair comparison. |
| Approach: | They propose to use a knowledge-mining method to build a large-scale knowledge-annotated SPT corpus and integrate sentiment knowledge into pre-training. |
| Outcome: | The proposed method is able to build a large-scale knowledge-annotated SPT corpus and compares with other methods. |
Unbiased Math Word Problems Benchmark for Mitigating Solving Bias (2022.findings-naacl)
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| Challenge: | Existing solvers with data bias and learning bias only learn shallow heuristics rather than deep semantics for understanding problems. |
| Approach: | They propose a MWP dataset named UnbiasedMWP which is constructed by varying the grounded expressions in collected data and annotating them manually. |
| Outcome: | The proposed dataset has significantly fewer biases than its original data and other datasets, posing a promising benchmark for fairly evaluating the solvers’ reasoning skills rather than matching nearest neighbors. |
DB-LLM: Accurate Dual-Binarization for Efficient LLMs (2024.findings-acl)
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Hong Chen, Chengtao Lv, Liang Ding, Haotong Qin, Xiabin Zhou, Yifu Ding, Xuebo Liu, Min Zhang, Jinyang Guo, Xianglong Liu, Dacheng Tao
| Challenge: | Existing methods for ultra-low bit quantization cause severe accuracy drops . a novel Dual-Binarization method is proposed for efficient Large Language Models . |
| Approach: | They propose a Dual-Binarization method that takes 2-bit-width and binarization into account . they propose DB-LLM, which uses a 2-bit binarized weighted model to represent weights efficiently . |
| Outcome: | The proposed method surpasses the current State-of-the-Art in ultra-low bit quantization and achieves 20% reduction in computational consumption compared to the SOTA method under the same bit-width. |
TLSA: LLM-Guided Text-Label Space Alignment with Contrastive Learning for Generalized Category Discovery (2026.acl-long)
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| Challenge: | Existing methods for generalized category discovery suffer from weak text–label alignment, inconsistent objectives across known and novel categories, and poor discrimination of semantically similar clusters. |
| Approach: | They propose a unified framework that enforces contrastive alignment between text and label representations within a shared semantic space. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on four benchmark datasets. |
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning (2021.findings-acl)
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| Challenge: | Existing methods to solve geometric problems are dependent on handcraft rules and limited on small-scale datasets. |
| Approach: | They propose a Geometric Question Answering dataset with 5,010 geometric problems with corresponding annotated programs to illustrate the solving process. |
| Outcome: | The proposed method is significantly lower than human performance on the proposed dataset than on a publicly available dataset. |
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck (2022.coling-1)
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| Challenge: | Event argument extraction (EAE) aims to extract arguments with given roles from texts. |
| Approach: | They propose a multi-format transfer learning model with variational information bottleneck to learn from existing datasets. |
| Outcome: | The proposed model improves on three benchmark datasets and obtains state-of-the-art performance on EAE. |
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. |
ExploraCoder: Advancing Code Generation for Multiple Unseen APIs via Planning and Chained Exploration (2025.acl-long)
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| Challenge: | Large language models face intrinsic limitations in coding with unseen APIs in training corpora. |
| Approach: | They propose a training-free framework that empowers LLMs to invoke multiple unseen APIs in code solution by planning a complex problem into several API invocation subtasks and experimenting with correct API usage at intermediate steps. |
| Outcome: | The proposed framework significantly improves performance for models lacking prior API knowledge, achieving 11.99% over retrieval-based approaches and 17.28% over pretraining-based methods in pass@10. |
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations (2020.coling-main)
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| Challenge: | Emotion recognition in conversations (ERC) is a task that aims to recognize the emotion of each utterance in conversations. |
| Approach: | They propose an iterative emotion interaction network which uses iterativly predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction. |
| Outcome: | The proposed method retains state-of-the-art performance on two datasets and achieves high accuracy. |
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. |
Is ChatGPT a General-Purpose Natural Language Processing Task Solver? (2023.emnlp-main)
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| Challenge: | Recent advances in scale have enabled large language models to perform NLP tasks zero-shot . however, it is not known whether ChatGPT can serve as a generalist model that can perform many NLP jobs zero- shot. |
| Approach: | They empirically evaluate ChatGPT's zero-shot learning ability on 20 popular NLP datasets . they find it performs well on many tasks favoring reasoning abilities . |
| Outcome: | The proposed model can perform many NLP tasks zero-shot without adaptation on downstream data. |
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing approaches to domain adaptation fail to generalize well on unknown test data. |
| Approach: | They propose a backdoor adjustment-based causal model to disentangle domain-specific and domain-invariant representations that play essential roles in tackling domain shift. |
| Outcome: | The proposed model disentangles domain-specific and domain-invariant representations that play essential roles in tackling domain shift. |
Reduce Human Labor On Evaluating Conversational Information Retrieval System: A Human-Machine Collaboration Approach (2023.emnlp-main)
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| Challenge: | Evaluating conversational information retrieval systems requires a significant amount of human labor for annotation. |
| Approach: | They propose to use human annotation to calibrate evaluation results to eliminate evaluation biases. |
| Outcome: | The proposed method consumes less than 1% of human labor and achieves a consistency rate of 95%-99% with human evaluation results. |
SheetDesigner: MLLM-Powered Spreadsheet Layout Generation with Rule-Based and Vision-Based Reflection (2025.emnlp-main)
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| Challenge: | Existing automated layout models are ill-suited for spreadsheets, authors say . existing layout models treat components as rectangles with continuous coordinates . authors: spreadsheets are powerful tools for organizing and analyzing data . |
| Approach: | They formalize a spreadsheet layout generation task and introduce a framework for spreadsheet layouts . they use multimodal large language models to combine rule and vision reflection . |
| Outcome: | The proposed framework outperforms baselines in a spreadsheet layout generation task by 22.6%. |
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing benchmarks for hallucination detection are intentionally generated by large language models (LLMs) however, many focus on factuality while ignoring faithfulness. |
| Approach: | They propose a dialogue-level hallucination evaluation benchmark for large language models . they integrate the topic into prompts and facilitate a dialog between two LLMs . |
| Outcome: | The proposed benchmark covers four common multi-turn dialogue domains and five hallucination subtypes, extended from factuality and faithfulness hallucines. |
Sparse Black-Box Multimodal Attack for Vision-Language Adversary Generation (2023.findings-emnlp)
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| Challenge: | Existing adversarial attacks using imperceptible perturbations are challenging to simulate . e-commerce product restrictions and hate speech monitoring are examples of such attacks . |
| Approach: | They propose a black-box adversarial attack that leverages sparse perturbations to simulate adversarials exhibited by illegal merchants in the black- box scenario. |
| Outcome: | The proposed method outperforms existing attacks and unimodal attacks by treating images and text in discrete space and outperforming existing models. |
Let’s Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models (2024.lrec-main)
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| Challenge: | Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
| Approach: | They propose a diffusion model which extracts aspects step by step and learns a denoising process that progressively restores them in a reverse manner. |
| Outcome: | Empirical evaluations on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
Controllable Contrastive Generation for Multilingual Biomedical Entity Linking (2023.emnlp-main)
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| Challenge: | Multilingual biomedical entity linking (MBEL) aims to map language-specific mentions in biomedically text to standardized concepts in a multilingual knowledge base (KB). |
| Approach: | They propose a prompt-based controllable contrastive generation framework for MBEL which summarizes multidimensional information of the UMLS concept mentioned in biomedical text into a natural sentence following a predefined template. |
| Outcome: | The proposed framework matches against UMLS concepts in as many languages and types as possible, thus facilitating cross-information disambiguation. |
Improving Arabic Diacritization with Regularized Decoding and Adversarial Training (2021.acl-short)
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| Challenge: | Existing studies regard auto-generated knowledge instances as gold references, which limits their effectiveness since they are not always accurate and inferior instances can lead to incorrect predictions. |
| Approach: | They propose to use regularized decoding and adversarial training to appropriately learn from noisy knowledge instances for Arabic diacritization. |
| Outcome: | The proposed model outperforms existing models on two benchmark datasets even with flawed auto-generated knowledge. |
M3CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought (2024.acl-long)
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| Challenge: | MCoT requires models to leverage knowledge from both textual and visual modalities for step-by-step reasoning. |
| Approach: | They propose a benchmark to address the challenges of MCoT, and evaluate it using vision large language models. |
| Outcome: | The proposed benchmark addresses the above challenges and shows that current models still struggle to reason in M3CoT. |
Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph (D19-1)
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| Challenge: | Existing studies on symptom diagnosis based on EHRs focus on the standard electronic medical records, but the dialogues between doctors and patients that contain more rich information are not well studied. |
| Approach: | They propose to build a global attention mechanism to capture more symptom related information and build symptom graphs to model the associations between symptoms rather than treating each symptom independently. |
| Outcome: | The proposed model achieves the state-of-the-art on the constructed dataset. |
Large Language Model-based Human-Agent Collaboration for Complex Task Solving (2024.findings-emnlp)
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| Challenge: | Recent advances in large language models have led to the development of LLM-based autonomous agents. |
| Approach: | They propose a Reinforcement Learning-based Human-Agent Collaboration method which trains a policy model to determine the most opportune stages for human intervention within the task-solving process. |
| Outcome: | The proposed method improves human-agent collaboration significantly through well-planned, limited human intervention. |
Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering (2026.acl-long)
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Qiming Li, Xiaocheng Feng, Yixuan Ma, Ruihan Chen, Zihe Tong, Zekai Ye, Xiachong Feng, Libo Qin, Haoyu Ren, Kun Chen, Yunfei Lu, Dandan Tu, Bing Qin
| Challenge: | Existing approaches to enhance multilingual reasoning capabilities rely on costly multilingual training or employ prompting with external translation tools. |
| Approach: | They propose a training-free inference-time method to enhance multilingual reasoning capabilities via Representation Engineering without additional training data or tools. |
| Outcome: | The proposed method outperforms existing methods on four reasoning benchmarks in English and Thai and Swahili. |
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)
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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. |
| Approach: | They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. |
| Outcome: | The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication. |
MetaScale: Test-Time Scaling with Evolving Meta-Thoughts (2026.findings-acl)
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| Challenge: | Existing approaches impose fixed cognitive structures that enhance performance in specific tasks but lack adaptability across diverse scenarios. |
| Approach: | They propose a test-time scaling framework based on meta-thoughts to improve performance . meta-thinkts are adaptive thinking strategies tailored to a given task . |
| Outcome: | Experimental results show that MetaScale outperforms standard inference approaches . it can scale more effectively with increasing sampling budgets and produces more structured responses . |
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention (P19-1)
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| Challenge: | Existing models for limited-domain RNNs are difficult to scale due to the complexity of the inputs. |
| Approach: | They propose to use dialog acts to build a multi-layer hierarchical graph with a disentangled self-attention network. |
| Outcome: | The proposed model improves on the baselines on automatic and human evaluation metrics. |
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. |
RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios (2026.findings-acl)
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Yibo Zhang, Kaiwen Luo, Liang Lin, Shilinlu Yan, Jin Wang, Yaoqi Guo, Yitian Chen, Yalan Qin, Zhenhong Zhou, Kun Wang, Li Sun
| Challenge: | Existing evaluations rely on synthetic Gaussian noise or simplistic single-source interference, failing to capture the intricate, multi-layered acoustic dynamics that characterize authentic physical environments. |
| Approach: | They propose a robustness benchmark to stress-test Audio Large Models (ALLMs) using high-fidelity auditory scene simulations. |
| Outcome: | The proposed model performs well on a wide range of tasks, including automatic speech recognition, speech translation, and audio-based reasoning. |
Editing the Moving World: Model Editing for Video LLMs (2026.acl-long)
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Qian Zhang, Xinye Li, Xiaokai Wu, Junhao Xu, Zhanyue Qin, Qingbin Liu, Junxian Cai, Xi Chen, Bolin Zhang, Zhiying Tu, Dianhui Chu, Xiaoyan Yu, Dianbo Sui
| Challenge: | Existing models for knowledge editing focus on knowledge-level or static visual domains, overlooking dynamic semantics. |
| Approach: | They propose a benchmark for modeling large language models using six representative models . they analyze the strengths and limitations of existing models and identify new directions . |
| Outcome: | The proposed benchmark extends existing models from static modalities to dynamic video scenarios. |
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition (2020.emnlp-main)
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| Challenge: | Existing methods to augment pre-trained language models with disease knowledge are lacking. |
| Approach: | They propose a method to augment BERT-like pre-trained language models with disease knowledge. |
| Outcome: | The proposed method improves on a suite of BERT models over three tasks. |
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. |
LongLeader: A Comprehensive Leaderboard for Large Language Models in Long-context Scenarios (2025.naacl-long)
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Pei Chen, Hongye Jin, Cheng-Che Lee, Rulin Shao, Jingfeng Yang, Mingyu Zhao, Zhaoyu Zhang, Qin Lu, Kaiwen Men, Ning Xie, Huasheng Li, Bing Yin, Han Li, Lingyun Wang
| Challenge: | LongLeader aims to assess different LLMs' long-context comprehension abilities . long-constext comprehension is a key bottleneck for many use cases . |
| Approach: | They propose a leaderboard to assess different LLMs' long-context comprehension abilities . they offer open-source access to the benchmarks and maintain a dedicated website . |
| Outcome: | The proposed model assesses different LLMs on selected benchmarks and provides open-source access to the benchmarks. |
Two Heads are Better than One: Nested PoE for Robust Defense Against Multi-Backdoors (2024.naacl-long)
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| Challenge: | Existing defense mechanisms assume that only one type of trigger is adopted by the attacker, while defending against multiple simultaneous and independent trigger types necessitates general defense frameworks. |
| Approach: | They propose a framework that uses a mixture of experts as a trigger-only ensemble to defend against multiple trigger types. |
| Outcome: | The proposed framework defends against multiple trigger types in a single ensemble and in combination of models. |
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. |
OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding (2023.acl-demo)
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| Challenge: | Spoken Language Understanding (SLU) is a task-oriented dialogue system . open-source toolkit provides a unified, modularized, and extensible toolkit for SLU . |
| Approach: | They introduce an open-source toolkit to provide a unified toolkit for spoken language understanding. |
| Outcome: | The proposed toolkit unifies 10 models for both single-intent and multi-intention scenarios. |
From Shortcuts to Triggers: Backdoor Defense with Denoised PoE (2024.naacl-long)
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| Challenge: | Existing backdoor defense methods focus on specific triggers, leaving a universal defense unexplored. |
| Approach: | They propose an ensemble-based backdoor defense framework that denies backdoor attacks by capturing backdoor shortcuts and preventing learning them. |
| Outcome: | The proposed framework significantly improves defense performance against backdoor attacks . it is also effective under a more challenging but practical setting . |
Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators (2023.emnlp-main)
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| Challenge: | Large language models outperform information retrieval techniques for downstream knowledge-intensive tasks when being prompted to generate world knowledge. |
| Approach: | They propose a COmpreheNsive kNowledge Evaluation framework to evaluate generated knowledge from six important perspectives . they conduct extensive empirical analysis of generated knowledge on two widely studied knowledge-intensive tasks . |
| Outcome: | The proposed framework evaluates generated knowledge from six important perspectives on two knowledge-intensive tasks. |
Learning to Describe for Predicting Zero-shot Drug-Drug Interactions (2023.emnlp-main)
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| Challenge: | Existing computational methods for DDI prediction fail to capture interactions for new drugs due to the lack of knowledge. |
| Approach: | They propose a problem setup as zero-shot DDI prediction that deals with the case of new drugs by using textual information from online databases. |
| Outcome: | The proposed method improves on several settings including zero-shot and few-shot DDI prediction and the selected texts are semantically relevant. |
Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence (2024.findings-acl)
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| Challenge: | Emotional Intelligence (EI) is a key concept in the field of human intelligence. |
| Approach: | They propose a method to enhance EI of large language models by naive fine-tuning on EI-related tasks. |
| Outcome: | The proposed method improves EI of two LLM-based assistants without compromising GI. |
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors (2022.findings-acl)
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| Challenge: | Existing models for multimodal sentiment analysis are limited in their capacity to be deployed in the real world. |
| Approach: | They propose a model that can dynamically refine erroneous sentiment words by leveraging multimodal sentiment clues. |
| Outcome: | The proposed model surpasses the state-of-the-art models on three datasets. |
Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)
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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang
| Challenge: | Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. |
| Approach: | They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice. |
| Outcome: | The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models . |
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. |
PsychEval: A Multi-Session and Multi-Therapy Benchmark for High-Realism AI Psychological Counselor (2026.findings-acl)
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Qianjun Pan, Junyi Wang, Jie Zhou, Yutao Yang, Junsong Li, Kaiyin Xu, Yougen Zhou, Yihan Li, JingYuan Zhao, Qin Chen, Ningning Zhou, Kai Chen, Liang He
| Challenge: | Existing models focus on a single therapy, but complex cases require flexible strategies among various therapies. |
| Approach: | They propose a multi-session, multi-therapy, and highly realistic benchmark . it is designed to address three key challenges: 1) can we train a highly realistic AI counselor? 2) How to systematically evaluate an AI counselor?" |
| Outcome: | The proposed benchmark is annotated with extensive professional skills and includes over 677 meta-skills and 4577 atomic skills. |
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. |
Language Resource Efficient Learning for Captioning (2021.findings-emnlp)
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| Challenge: | XE loss and SC loss are both considered to be performance degradations for captioning tasks. |
| Approach: | They propose to generalize the single pairwise comparison in SC loss and use multiple generalized pairwise compares to reduce noise in baseline. |
| Outcome: | The proposed method outperforms state-of-the-art models on a video caption dataset using only half of the language resources. |
In-Context Learning with Iterative Demonstration Selection (2024.findings-emnlp)
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| Challenge: | Existing literature has highlighted the importance of selecting examples that are diverse or semantically similar to the test sample . Existing studies have shown that the optimal selection dimension, i.e., diversity or similarity, is task-specific. |
| Approach: | They propose to use zero-shot chain-of-thought reasoning to iteratively select examples that are diverse but still strongly correlated with the test sample as ICL demonstrations. |
| Outcome: | The proposed method outperforms existing demonstration selection methods on reasoning, question answering, and topic classification tasks. |
OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models (2026.acl-long)
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Qiguang Chen, Chengyu Luan, Jiajun Wu, Qiming Yu, Yi Yang, Yizhuo Li, Jingqi Tong, Xiachong Feng, Libo Qin, Wanxiang Che
| Challenge: | Existing multimodal reasoning benchmarks for large vision-language models emphasize single-image analysis and fail to exploit contextual information across multiple images. |
| Approach: | They propose a benchmark to evaluate Olympiad-level reasoning when evidence is distributed over multiple images. |
| Outcome: | The proposed model outperforms existing models on bi-image Olympiads and Gemini-3-Pro on multimodal Olympiad-level reasoning tasks. |
CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models (2024.acl-long)
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Tong Zhang, Peixin Qin, Yang Deng, Chen Huang, Wenqiang Lei, Junhong Liu, Dingnan Jin, Hongru Liang, Tat-Seng Chua
| Challenge: | Large language models are used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity remains unknown. |
| Approach: | They propose a benchmark for evaluating large language models using a well-organized taxonomy. |
| Outcome: | The proposed model is based on a well-organized taxonomy and compares it with other models. |
P-React: Synthesizing Topic-Adaptive Reactions of Personality Traits via Mixture of Specialized LoRA Experts (2025.findings-acl)
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| Challenge: | Existing studies on personalized large language models focus on modeling explicit character profiles, while ignoring the underlying personality traits that truly shape behaviors and decision-making. |
| Approach: | They propose a personalized large language model (LLM) that captures implicit Big Five personality traits and integrates a Personality Specialization Loss to capture individual trait expressions. |
| Outcome: | The proposed model improves on Big Five personality traits and integrates a Personality Specialization Loss (PSL) to capture individual trait expressions. |
Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging (2024.emnlp-main)
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Deyuan Liu, Zhanyue Qin, Hairu Wang, Zhao Yang, Zecheng Wang, Fangying Rong, Qingbin Liu, Yanchao Hao, Bo Li, Xi Chen, Cunhang Fan, Zhao Lv, Dianhui Chu, Zhiying Tu, Dianbo Sui
| Challenge: | Existing methods for parameter pruning fail to utilize the knowledge from pruned parameters. |
| Approach: | They propose a method that uses manifold learning and the Information Bottleneck measure to merge similar layers to preserve model performance. |
| Outcome: | The proposed method outperforms pruning methods on multiple datasets and LLMs with quantization and achieves substantial compression ratios. |
CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue (2022.coling-1)
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| Challenge: | Consistency identification in task-oriented dialog usually consists of three subtasks . a proposed model for consistency identification in dialog is based on an explicit interaction paradigm . |
| Approach: | They propose a cycle guided interactive learning model that makes information exchange explicit from all the three tasks. |
| Outcome: | The proposed model achieves state-of-the-art performance pushing the overall score to 56.3% (5.0% point absolute improvement) |
BOLT: Benchmarking Open-World Learning for Text Classification (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on out-of-distribution (OOD) detection while overlooking broader challenges such as the discovery of novel categories. |
| Approach: | They propose a unified Benchmark and evaluation toolkit supporting Open-world learning for text classification. |
| Outcome: | The proposed methods overfit training distributions and struggle to generalize to unseen classes. |
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation (2024.acl-long)
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| Challenge: | Existing methods to study the Matthew effect in Recommender Systems (RSs) however, it is amplified when the user interacts with the system over time. |
| Approach: | They propose a paradigm to alleviate the Matthew effect in conversational recommendation by learning multi-aspect preferences. |
| Outcome: | The proposed paradigm achieves state-of-the-art performance and superior of alleviating Matthew effect in conversational recommendation tasks. |
UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression (2022.emnlp-main)
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| Challenge: | Existing work on geometry problem solving treats calculation and proving as two specific tasks hindering a deep model to unify reasoning ability on multiple math tasks. |
| Approach: | They propose a large-scale Unified Geometry problem benchmark to unify geometry on multiple math tasks. |
| Outcome: | The proposed framework outperforms the existing model with 5.6% and 3.2% accuracies on calculation and proving problems. |
Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges (2024.findings-acl)
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Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu, Shafiq Joty
| Challenge: | Data augmentation (DA) is a key technique for enhancing model performance by diversifying training examples without the need for additional data collection. |
| Approach: | They examine various strategies that utilize LLMs for data augmentation, including a novel exploration of learning paradigms where LLM-generated data is used for diverse forms of further training. |
| Outcome: | The proposed approach addresses the primary open challenges faced by LLMs in the field of large language models and aims to serve as a comprehensive guide for researchers and practitioners. |
Real-time Ad Retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising (2025.emnlp-main)
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| Challenge: | Existing methods for retrieving documents and ads use one-to-few mappings and time-consuming content extraction. |
| Approach: | They propose a framework that leverages LLM-generated commercial intents as an intermediate semantic representation to directly retrieve ads for queries in real-time. |
| Outcome: | The proposed framework has been implemented in a real-world online system, handling daily search volumes in billions. |
Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information (2024.findings-emnlp)
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| Challenge: | Chain-of-Thought (CoT) is a key technique for enhancing the performance of Large Language Models. |
| Approach: | They propose a framework that optimizes outputs by utilizing wrong information and multi-perspective verification. |
| Outcome: | The proposed framework surpasses all baselines on 8 datasets and 5 LLMs. |
Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models (2024.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR). |
| Approach: | They propose a multimodal LLM to receive source speech as extra input and reformat it as a cloze test with logits calibration to remove input information redundancy and simplify GER with clear instructions. |
| Outcome: | The proposed model improves on 9 popular ASR datasets and is faster than vanilla GER. |
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. |
Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits (2025.coling-main)
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Bohan Li, Jiannan Guan, Longxu Dou, Yunlong Feng, Dingzirui Wang, Yang Xu, Enbo Wang, Qiguang Chen, Bichen Wang, Xiao Xu, Yimeng Zhang, Libo Qin, Yanyan Zhao, Qingfu Zhu, Wanxiang Che
| Challenge: | Existing data on MBTI personality detection are based on self-reported labels and fail to capture the full range of population personality traits. |
| Approach: | They construct a manually annotated MBTI personality detection dataset with soft labels under the guidance of psychologists and use them to identify the task. |
| Outcome: | The MBTIBench is the first manually annotated MBti personality detection dataset with soft labels under the guidance of psychologists. |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation (2020.emnlp-main)
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| Challenge: | Existing adversarial text generation approaches can lead to generation lacking diversity or fluency, whereas perturbing in the intermediate representation space can lead a model to generate generations that are not related to the input. |
| Approach: | They propose to generate adversarial texts through controllable attributes that are known to be invariant to task labels. |
| Outcome: | The proposed model generates more diverse and fluent adversarial examples, compared to existing approaches, and is more robust against model re-training and different model architectures. |
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation (2024.findings-emnlp)
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Jia Fu, Xiaoting Qin, Fangkai Yang, Lu Wang, Jue Zhang, Qingwei Lin, Yubo Chen, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
| Challenge: | Recent advances in Large Language Models have transformed ML/AI development . a reevaluation of AutoML principles for Retrieval-Augmented Generation (RAG) systems is needed. |
| Approach: | They propose a framework for hyper-parameter tuning and a hierarchical MAB method for efficient exploration of large search spaces. |
| Outcome: | The proposed framework outperforms baseline methods in more challenging optimization scenarios. |
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song
| Challenge: | Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage. |
| Approach: | They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality. |
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. |
Monotonic Paraphrasing Improves Generalization of Language Model Prompting (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable proficiency in zero-shot decision making and instruction following. |
| Approach: | They propose an end-to-end decoding strategy that paraphrases given prompts or instructions into their lower perplexity counterparts based on an ensemble of a paraphrase LM for prompt rewriting, and a target LM that constrains the generation for lower perxity. |
| Outcome: | The proposed method can efficiently paraphrase the original prompt without altering its semantic meaning while decreasing the perplexity of each generation as calculated by the target LM. |
What are the Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets? Insights and Best Practices (2025.acl-long)
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| Challenge: | Existing methods to generate long-context instruction-tuning data are limited by poor quality and fewer than 35% of samples are multi-hop . |
| Approach: | They propose a framework that integrates a quality verification agent, a single-hop question generation agent, and a multi-hop questions merger agent to enhance model performance. |
| Outcome: | The proposed framework significantly improves data quality with high-quality, multi-hop, and diverse data. |
YouMakeup: A Large-Scale Domain-Specific Multimodal Dataset for Fine-Grained Semantic Comprehension (D19-1)
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| Challenge: | Multimodal semantic comprehension has attracted increasing research interest recently such as visual question answering and caption generation. |
| Approach: | They propose to use a large-scale multimodal instructional video dataset to support fine-grained comprehension research in specific domain. |
| Outcome: | The proposed dataset contains 2,800 videos from YouTube, spanning more than 420 hours in total. |
Beyond Output Matching: Bidirectional Alignment for Enhanced In-Context Learning (2025.acl-long)
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Chengwei Qin, Wenhan Xia, Fangkai Jiao, Chen Chen, Yuchen Hu, Bosheng Ding, Ruirui Chen, Shafiq Joty
| Challenge: | Existing methods to train student models on the generated outputs of teacher models are not efficient for ICL. |
| Approach: | They propose to align the output of smaller (student) models with that of larger (teacher) models by incorporating a ranking loss and aligning the token-level output distribution. |
| Outcome: | The proposed model outperforms baseline models on a variety of tasks involving language understanding, reasoning, and coding. |
DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective (2025.findings-emnlp)
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| Challenge: | Existing methods for prompt optimization still face challenges in robustness, efficiency, and generalization. |
| Approach: | They propose 7 new approaches inspired by traditional deep learning paradigms for prompt optimization that integrate text-based gradient optimization. |
| Outcome: | The proposed methods integrate deep learning paradigms into text-based gradient optimization. |
UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models (2024.findings-emnlp)
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| Challenge: | UrbanLLM is a fine-tuned large language model designed to tackle diverse urban problems. |
| Approach: | They propose a fine-tuned large language model to tackle diverse urban problems . UrbanLLM decomposes urban-related queries into manageable sub-tasks . |
| Outcome: | The proposed model outperforms existing models in urban planning and management tasks. |
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition (2022.acl-long)
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| Challenge: | Existing models for named entity recognition only consider the potential transferability between two identical tasks across both domains. |
| Approach: | They propose to use a similarity metric model to improve cross-lingual named entity recognition task on target domain. |
| Outcome: | Empirical studies on 7 different languages confirm the effectiveness of the proposed model. |
Large Language Models for Predictive Analysis: How Far Are They? (2025.findings-acl)
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| Challenge: | Existing studies on LLMs do not evaluate their capability in predictive analysis. |
| Approach: | They propose a benchmark to evaluate Large Language Models (LLMs) they integrate 1130 queries from 44 real-world datasets of 8 different fields to evaluate their capability . |
| Outcome: | The proposed benchmark evaluates 12 renowned LLMs from 44 real-world datasets . results offer insights into their practical use in predictive analysis . |
KG-RAG: Enhancing GUI Agent Decision-Making via Knowledge Graph-Driven Retrieval-Augmented Generation (2025.emnlp-main)
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Ziyi Guan, Jason Chun Lok Li, Zhijian Hou, Pingping Zhang, Donglai Xu, Yuzhi Zhao, Mengyang Wu, Jinpeng Chen, Thanh-Toan Nguyen, Pengfei Xian, Wenao Ma, Shengchao Qin, Graziano Chesi, Ngai Wong
| Challenge: | Recent advances in GUI agents have limited app-specific knowledge of complex mobile tasks. |
| Approach: | They propose a Knowledge Graph-driven Retrieval-Augmented Generation framework that transforms fragmented UTGs into structured vector databases for efficient real-time retrieval. |
| Outcome: | The proposed framework outperforms existing methods in a 75.8% success rate and 84.6% decision accuracy test across mobile apps. |
PromptRank: Unsupervised Keyphrase Extraction Using Prompt (2023.acl-long)
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| Challenge: | Existing keyphrase extraction methods struggle with document and candidate length discrepancies or fail to fully utilize the pre-trained language model without further fine-tuning. |
| Approach: | They propose an unsupervised keyphrase extraction approach that uses a pre-trained language model to rank candidates based on document embeddings. |
| Outcome: | The proposed approach outperforms the existing keyphrase extraction approach on six benchmarks. |
Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers (2022.findings-emnlp)
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| Challenge: | Existing methods to reduce model's reliance on bias features ignore the learnability of these features. |
| Approach: | They propose to reduce models' reliance on bias features by first training models with fixed low-capacity models which ignore the learnability of the bias features. |
| Outcome: | The proposed models can perform better on out-of-distribution datasets than baseline models with a more sophisticated model design. |
Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages (2023.emnlp-main)
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| Challenge: | Existing methods for zero-shot CoT are limited to a single language, making it difficult to generalize to other languages and hindering global development. |
| Approach: | They introduce cross-lingual prompting (CLP) to improve zero-shot CoT reasoning across languages. |
| Outcome: | The proposed method outperforms existing prompting methods on several benchmarks. |
Improving Copy-oriented Text Generation via EDU Copy Mechanism (2024.lrec-main)
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| Challenge: | Existing extractive models generate texts through word-by-word decoding, causing factual inconsistencies and slow inference. |
| Approach: | They propose a framework that integrates the behavior of copying EDUs into generative models. |
| Outcome: | The proposed framework reduces the number of generated tokens significantly. |
GenDis: Generative-Discriminative Dual-View Co-Training for Generalized Category Discovery (2026.acl-long)
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| Challenge: | Existing methods rely on one-hot discriminative supervision, leading to overfitting on seen classes and poor generalization to unseen ones. |
| Approach: | They propose a Generative–Discriminative Dual-View Co-Training framework that unifies discriminative classification and semantic label generation within an LLM. |
| Outcome: | The proposed framework outperforms existing methods on five benchmarks on the generalized category discovery (GCD) task. |
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). |
End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)
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| Challenge: | End-to-end task-oriented dialogue (EToD) can generate responses in an end-to end fashion without modular training, which attracts escalating popularity. |
| Approach: | They present a systematic review of EToD and propose a unified perspective to summarize existing approaches and recent trends. |
| Outcome: | The proposed approaches can generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. |
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System (2023.findings-acl)
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| Challenge: | Existing benchmarks for multi-modal sarcasm detection have some shortcomings . a new framework can leverage multi-grained cues from multiple perspectives for multimodal detection . |
| Approach: | They propose a correction dataset that removes spurious cues and re-annotates the unreasonable samples. |
| Outcome: | The proposed framework outperforms the existing benchmarks in multi-modal sarcasm detection. |
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. |
LLM-Based Multi-Hop Question Answering with Knowledge Graph Integration in Evolving Environments (2024.findings-emnlp)
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| Challenge: | Existing methods for knowledge editing in Large Language Models face difficulties with multi-hop questions that require accurate fact identification and sequential logical reasoning. |
| Approach: | They propose a method that merges explicit knowledge representations of Knowledge Graphs with the linguistic flexibility of Large Language Models to convert free-form language into structured queries and fact triples. |
| Outcome: | The proposed method significantly surpasses state-of-the-art knowledge editing methods in the multi-hop question answering benchmark, MQuAKE. |
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. |
GUICourse: From General Vision Language Model to Versatile GUI Agent (2025.acl-long)
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Wentong Chen, Junbo Cui, Jinyi Hu, Yujia Qin, Junjie Fang, Yue Zhao, Chongyi Wang, Jun Liu, Guirong Chen, Yupeng Huo, Yuan Yao, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Graphical User Interfaces (GUIs) are a pivotal medium for human-computer interaction. |
| Approach: | They propose a series of datasets for training visual-based GUI agents using general VLMs. |
| Outcome: | The proposed GUICourse datasets show that even a small-sized GUI agent performs better on GUI tasks. |
LOME: Large Ontology Multilingual Extraction (2021.eacl-demos)
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Patrick Xia, Guanghui Qin, Siddharth Vashishtha, Yunmo Chen, Tongfei Chen, Chandler May, Craig Harman, Kyle Rawlins, Aaron Steven White, Benjamin Van Durme
| Challenge: | LOME is a system for performing multilingual information extraction with large ontologies. |
| Approach: | They propose a system for multilingual information extraction with a framenet parser . LOME is available as a Docker container on Docker Hub and a lightweight version is available on the web . |
| Outcome: | The proposed system outperforms or is competitive with the (monolingual) state-of-the-art . it can be used to build knowledge graphs with large ontologies and across multiple languages . |
Theory of Mind in Large Language Models: Assessment and Enhancement (2025.acl-long)
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| Challenge: | Theory of Mind (ToM) is a cornerstone of human social intelligence . Large Language Models (LLMs) are increasingly integrated into daily life . |
| Approach: | They analyze evaluation benchmarks and enhancement strategies to evaluate LLMs' ToM capabilities. |
| Outcome: | The proposed and widely used story-based benchmarks and enhancement strategies are used to evaluate LLMs' ToM capabilities. |
Exclusion of Thought: Mitigating Cognitive Load in Large Language Models for Enhanced Reasoning in Multiple-Choice Tasks (2025.acl-long)
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| Challenge: | Multiple-choice questions (MCQs) are widely used and vital assessment format for evaluating large language models (LLMs). |
| Approach: | They propose a reasoning prompt strategy that redirects the model's attention away from erroneous options and eliminates incorrect options. |
| Outcome: | The proposed reasoning prompt reduces cognitive load by steering the model’s attention away from erroneous options, enabling the model to focus more effectively on reasonable answers. |
Uncertainty-Aware Contrastive Sentence Embedding With Local Context Representation for Text Classification (2026.findings-acl)
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| Challenge: | Existing models for text classification are based on encoder-only transformers and generative pre-trained transformers. |
| Approach: | They propose an uncertainty-aware contrastive sentence embedding approach that addresses language ambiguity and inter-class separability for a text classification task. |
| Outcome: | The proposed approach improves classification accuracy on public datasets compared with state-of-the-art methods. |
Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints (2022.naacl-main)
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| Challenge: | Existing approaches to lexically constrained neural machine translation suffer from high latency. |
| Approach: | They propose a plug-in algorithm for non-autoregressive translation for this problem . they propose ACT to familiarize the model with the source-side context of constraints . |
| Outcome: | The proposed model improves over the backbone constrained NAT model in constraint preservation and translation quality, especially for rare constraints. |
CLIPText: A New Paradigm for Zero-shot Text Classification (2023.findings-acl)
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| Challenge: | Experimental results show that CLIP can be applied to zero-shot text classification tasks. |
| Approach: | They propose a CLIP model for zero-shot text classification that integrates prompt into CLIPText to better derive knowledge from CLIP. |
| Outcome: | The proposed model can be applied to a text-image matching problem and show that it can be used for language tasks. |
Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement (2026.acl-long)
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| Challenge: | Existing methods to learn internal world models rely on one-step supervision . however, standard MTP suffers from structural hallucinations . |
| Approach: | They propose a method which anchors predictions to ground-truth hidden state trajectories. |
| Outcome: | The proposed method bridges the gap between discrete tokens and continuous state representations, reducing structural hallucinations, and improving robustness to perturbations. |
A Survey for Efficient Open Domain Question Answering (2023.acl-long)
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| Challenge: | Open domain question answering (ODQA) is a longstanding task that can answer factoid questions without explicit evidence in natural language processing (NLP). |
| Approach: | They propose to use open domain question answering to answer factual questions from a large knowledge corpus without explicit evidence. |
| Outcome: | The proposed models can answer factoid questions from a large knowledge corpus without explicit evidence. |
Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition (2023.acl-long)
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| Challenge: | Existing efforts to improve robustness of audio-visual speech recognition with visual information focus on audio modality . current approaches introduce noise adaptation techniques to improve reliability of AVSR task . |
| Approach: | They propose a visual-invariant modality to strengthen robustness of audio-visual speech recognition (AVSR) it can adapt to any testing noises without dependence on noisy training data, a.k.a., unsupervised noise adaptation. |
| Outcome: | The proposed method outperforms existing state-of-the-arts on visual speech recognition task under various noisy and clean conditions. |
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. |
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories (2021.emnlp-main)
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| Challenge: | Aspect-based sentiment analysis (ABSA) predicts sentiment polarity for aspect term in sentences . labeled data stored at different locations and inaccessible due to privacy or legal concerns . |
| Approach: | They propose a model with federated learning to combine labeled data across different domains . they incorporate topic memory to take data from diverse domains into consideration . |
| Outcome: | The proposed model outperforms baselines on a simulated environment with three nodes. |
CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models (2025.acl-long)
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| Challenge: | Existing studies on hallucinations in large language models are limited to a single scenario, either cross-lingual or cross-modal. |
| Approach: | They propose a joint Cross-lingual and Cross-modal hallucinations benchmark to fill this gap . they incorporate cross-lingual, cross-modal scenarios to assess hallucinic capabilities . |
| Outcome: | The proposed benchmark incorporates both cross-lingual and cross-modal hallucination scenarios to assess the cross-linguistic and crossmodal capabilities of LLMs. |
FlashMem: Distilling Intrinsic Latent Memory via Computation Reuse (2026.findings-acl)
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| Challenge: | Large Language Models lack the mechanism to preserve dynamic context, forcing agents to redundantly reprocess history to maintain long-horizon autonomy. |
| Approach: | They propose a framework that distills intrinsic memory directly from transient reasoning states via computation reuse. |
| Outcome: | Experiments show that FlashMem matches heavy baselines while reducing inference latency by 5 times, effectively bridging the gap between efficiency and persistent cognition. |
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. |
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)
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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu
| Challenge: | Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent. |
| Approach: | They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs. |
| Outcome: | The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models. |
Better Zero-Shot Reasoning with Role-Play Prompting (2024.naacl-long)
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Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li, Yong Qin, Ruiqi Sun, Xin Zhou, Enzhi Wang, Xiaohang Dong
| Challenge: | Recent years have witnessed a paradigm shift in natural language processing, driven by large language models such as GPT-3, PaLM, and Llama. |
| Approach: | They propose a strategy for role-play prompting and assess its performance under the zero-shot setting. |
| Outcome: | The proposed method outperforms the standard zero-shot prompting approach across 12 reasoning benchmarks. |
Xinference: Making Large Model Serving Easy (2024.emnlp-demo)
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| Challenge: | Open-source large models are rapidly catching up with the closed-source models . however, many current inference tools are not as simple and convenient to use. |
| Approach: | They develop an open-source library to simplify the deployment and management of large models. |
| Outcome: | The proposed library outperforms open-source models and offers high throughput and low latency. |
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach (2024.emnlp-main)
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| Challenge: | Existing studies on susceptibility to misinformation rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications. |
| Approach: | They propose a computational approach to efficiently model users’ latent susceptibility levels by using demographic factors and political ideology as inputs. |
| Outcome: | The proposed model shows that political leanings and other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation. |
Attending via both Fine-tuning and Compressing (2021.findings-acl)
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| Challenge: | Existing studies show that attention mechanisms can improve models' interpretation, but they are not explicable. |
| Approach: | They propose a framework consisting of a learner and a compressor to purify attention scores . they propose to fine-tune and compress the attention mechanism to obtain a more faithful explanation . |
| Outcome: | The proposed framework improves performance and interpretability on eight benchmark datasets. |
Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing (2024.emnlp-main)
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| Challenge: | Recent studies have raised concerns regarding the hallucination and flaws in their reasoning process. |
| Approach: | They propose a framework to learn planning-based reasoning through Direct Preference Optimization on collected trajectories, which are ranked according to synthesized process rewards. |
| Outcome: | The proposed model surpasses GPT-3.5-Turbo on logical reasoning benchmarks on a set of logically-based reasoning tasks. |
X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic System (2025.findings-acl)
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| Challenge: | X-WebAgentBench evaluates the planning and interaction performance of language agents across multiple languages. |
| Approach: | They propose a multilingual agent benchmark that evaluates the interaction performance of language agents across multiple languages. |
| Outcome: | The proposed benchmark evaluates the planning and interaction performance of language agents across multiple languages. |
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. |
From Short Video to Clickable Search: RLVR-Enabled Listwise Query Suggestion with Retrieval-Augmented Context (2026.acl-industry)
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| Challenge: | Existing methods to generate short-video bottom-bar queries are largely retrieval-based. |
| Approach: | They propose to reformulate the task as one-shot list generation, producing multiple queries per video . they also build multi-query ground truth from exposure and CTR logs, and redesign offline evaluation . |
| Outcome: | The proposed system yields strong offline and online improvements . it is deployed on Kuaishou to serve hundreds of millions of users daily . |
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 . |
Relevant or Random: Can LLMs Truly Perform Analogical Reasoning? (2025.findings-acl)
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| Challenge: | Analogical reasoning is a unique ability of humans to address unfamiliar challenges by transferring strategies from relevant past experiences. |
| Approach: | They propose to use self-generated random examples to improve performance on a variety of reasoning tasks by incorporating relevant examples from relevant past experiences. |
| Outcome: | The proposed methods achieve comparable or even better performance on GSM8K with random biological examples. |
A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)
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Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang
| Challenge: | Inductive reasoning is an important task for large language models (LLMs). |
| Approach: | They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation. |
| Outcome: | The proposed method improves inductive reasoning in large language models. |
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. |
Lifelong Event Detection with Embedding Space Separation and Compaction (2024.naacl-short)
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| Challenge: | Existing methods for event detection are prone to forgetting due to overlap between memory data and the previously learned embedding space. |
| Approach: | They propose a method that embeds feature distributions away from the previous embedding space and mitigates overfitting by a memory calibration mechanism. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods with extensive experiments. |
AutoCAP: Towards Automatic Cross-lingual Alignment Planning for Zero-shot Chain-of-Thought (2024.findings-acl)
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| Challenge: | Existing approaches to cross-lingual chain-of-thought integrate reasoning knowledge from different languages, but they still rely on manual language specification and weight allocation. |
| Approach: | They propose an automatic cross-lingual alignment planning framework that integrates reasoning knowledge from different languages. |
| Outcome: | The proposed framework surpasses existing methods that require manual effort to integrate languages. |
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations (2023.emnlp-main)
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| Challenge: | a recent study validates the effectiveness of chat language models by fine-tuning instruction data. |
| Approach: | They propose to use a large-scale dataset of instructional conversations to fine-tune a conversational model on instruction data. |
| Outcome: | The proposed model outperforms open-source models in key metrics including scale, average length, diversity, coherence, etc. |
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. |
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. |
LESA: Learnable LLM Layer Scaling-Up (2025.acl-long)
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| Challenge: | Existing methods for depth scaling-up rely on empirical heuristic rules for layer duplication, resulting in poor initialization and slower convergence during continual pre-training. |
| Approach: | They propose a method for learning latent parameters between layers by concatenating parameters from each layer and applying Singular Value Decomposition. |
| Outcome: | Experiments show that LESA outperforms baseline models with less than half the cost of existing methods. |
Multilingual Neural Machine Translation with Language Clustering (D19-1)
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| Challenge: | Existing work on multilingual neural machine translation has been neglected due to its burdensome training process. |
| Approach: | They develop a framework that clusters languages into different groups and trains one multilingual model for each cluster. |
| Outcome: | The proposed model reduces the cost of training and improves translation accuracy. |
Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System (2021.emnlp-main)
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| Challenge: | Consistency Identification has been used for preventing inconsistent response generation, but few efforts have been made to task-oriented dialogue. |
| Approach: | They propose a dataset for Consistency Identification in task-oriented dialog system. |
| Outcome: | The proposed dataset is based on a single label and provides fine-grained labels to encourage model to know what inconsistent sources lead to it. |
Federated Chinese Word Segmentation with Global Character Associations (2021.findings-acl)
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| Challenge: | Chinese word segmentation (CWS) is a fundamental task for natural language processing. |
| Approach: | They propose a neural model for Chinese word segmentation with federated learning to help CWS deal with data isolation. |
| Outcome: | The proposed model outperforms baselines on a simulated environment with five nodes. |
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 . |
LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning (2022.findings-emnlp)
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| Challenge: | Recent advances in MWP solving are uninterpretable due to shallow heuristics . a new approach to solve automatic word problem solvers requires a solver to predict expression tree and corresponding linguistic logic formulas simultaneously. |
| Approach: | They propose to annotate interpretable logical formulas based on algebraic knowledge as the grounded linguistic logic of each solution equation. |
| Outcome: | The proposed approach improves interpretability of a MWP solver by using logical prompts and interpretation generation. |
Overcoming Catastrophic Forgetting by Exemplar Selection in Task-oriented Dialogue System (2024.findings-acl)
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| Challenge: | Experimental results show that HESIT effectively alleviates catastrophic forgetting by exemplar selection, and achieves state-of-the-art performance on the largest CL benchmark of ToDs in terms of all metrics. |
| Approach: | They propose a method to overcome catastrophic forgetting in task-oriented dialogue systems by tracing their hyper-gradients and a retraining strategy that uses influential exemplars for periodic retrains. |
| Outcome: | The proposed method achieves state-of-the-art on the largest CL benchmark of ToDs in terms of all metrics. |
Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation (2023.findings-emnlp)
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| Challenge: | Existing methods for finding meaningful counterfactuals rely on human annotation or implicit label invariance . a small amount of human-annotated counterf actual data can generate a robust dataset with learned labels. |
| Approach: | They propose a framework that generates counterfactuals by actively sampling from regions of uncertainty and automatically labeling them with a learned auxiliary classifier. |
| Outcome: | The proposed framework generates a large number of diverse counterfactuals and labels them with a learned classifier. |
Lifelong Sequence Generation with Dynamic Module Expansion and Adaptation (2023.emnlp-main)
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| Challenge: | Existing methods focus on maintaining old knowledge while paying little attention to knowledge transfer across tasks. |
| Approach: | They propose to train a model on a sequence of generation tasks to learn new generation patterns while avoiding the forgetting of previous knowledge. |
| Outcome: | The proposed model outperforms existing methods in different settings. |
GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding (2022.acl-long)
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| Challenge: | Existing approaches to zero-shot cross-lingual spoken language understanding rely on shared parameters, which can only perform implicit alignment across languages. |
| Approach: | They propose a global-local contrastive learning framework to achieve a fine-grained cross-lingual transfer . they employ bilingual dictionaries to construct multilingual views of the same utterance . |
| Outcome: | Experiments on MultiATIS++ show that GL-CLeF achieves the best performance . GL is based on dictionaries and encourages representations to be more similar than negative example pairs . |
Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Parameter-Efficient Tuning (2022.findings-emnlp)
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| Challenge: | Existing delta tuning algorithms freeze most of the parameters and only optimize minimal adaptive parameters. |
| Approach: | They propose to decompose DETs into a unified optimization subspace and conduct optimization within the subspace. |
| Outcome: | The proposed DETs achieve comparable performance to the original DET and can be transferred to another DET with non-trivial performance. |
False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize (2026.findings-acl)
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| Challenge: | Recent work has leveraged probing-based approaches to study the separability of malicious and benign inputs in Large Language Models’ internal representations. |
| Approach: | They propose to use probing-based methods to study separability of malicious and benign inputs in LLMs' internal representations to detect harmful and benign content. |
| Outcome: | The proposed methods show that they learn superficial patterns rather than semantic harmfulness. |
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%. |
A Survey of Large Models in Sports (2026.findings-acl)
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| Challenge: | Increasing interest in sports has led to the rapid advancement of large models, particularly multimodal large language models (MLLMs) . linguistic intelligence is a key component of large-model-driven sports intelligence . |
| Approach: | They propose to establish a foundation for advancing research and practical development of large-model-driven sports intelligence. |
| Outcome: | The proposed model-driven sports intelligence will be able to process and generate sports-related language effectively and process multiple data modalities. |
LLM-KT: Enhancing Large Language Models with Knowledge Tracing via Multi-Level Plug-and-Play Alignment (2026.findings-acl)
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| Challenge: | Existing methods to learn behavioral sequences fail to capture complex behavioral patterns due to a lack of deep reasoning capabilities and world knowledge. |
| Approach: | They propose a framework that integrates the reasoning power of Large Language Models with the sequential modeling strengths of traditional KT methods via multi-level plug-and-play alignment. |
| Outcome: | Extensive experiments on four standard datasets show that the proposed framework outperforms existing methods on state-of-the-art questions. |
Taming Extreme Tokens: Covariance-Aware GRPO with Gaussian-Kernel Advantage Reweighting (2026.acl-short)
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| Challenge: | Excessive exploitation can cause the model to become overconfident in its suboptimal solutions, thereby limiting its capabilities to explore novel reasoning strategies. |
| Approach: | They propose a method that dynamically down-weights extreme token-level updates via a Gaussian kernel and reduces the instability caused by the trade-off. |
| Outcome: | The proposed method improves downstream performance across reasoning benchmarks and stabilizes entropy as training progresses. |
Automatic Term Name Generation for Gene Ontology: Task and Dataset (2020.findings-emnlp)
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Yanjian Zhang, Qin Chen, Yiteng Zhang, Zhongyu Wei, Yixu Gao, Jiajie Peng, Zengfeng Huang, Weijian Sun, Xuanjing Huang
| Challenge: | Gene Ontology (GO) terms are used to describe gene function in biology and bio-medicine. |
| Approach: | They propose a task to generate term names for GO and build a large-scale benchmark dataset. |
| Outcome: | The proposed model outperforms baselines by incorporating the relations between genes, words and terms for term name generation. |
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction (2021.emnlp-main)
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Mahsa Yarmohammadi, Shijie Wu, Marc Marone, Haoran Xu, Seth Ebner, Guanghui Qin, Yunmo Chen, Jialiang Guo, Craig Harman, Kenton Murray, Aaron Steven White, Mark Dredze, Benjamin Van Durme
| Challenge: | Zero-shot cross-lingual information extraction (IE) is a technique for training data in a source language but not in . |
| Approach: | They explore techniques including data projection and self-training to improve zero-shot cross-lingual information extraction (IE) IE is a construction of an IE model for some target language given existing annotations exclusively in English. |
| Outcome: | The proposed techniques show that they perform better than any single strategy. |
LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models (2025.findings-naacl)
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Zhenyue Qin, Yu Yin, Dylan Campbell, Xuansheng Wu, Ke Zou, Ninghao Liu, Yih Chung Tham, Xiuzhen Zhang, Qingyu Chen
| Challenge: | Existing benchmarks for large vision-language models (LVLMs) are limited to ophthalmology-specific applications. |
| Approach: | They introduce a large-scale multimodal ophthalmology benchmark consisting of 21,993 instances across five ocular imaging modalities and 13 state-of-the-art LVLM representatives from closed-source, open-source and medical domains. |
| Outcome: | The proposed model shows significant performance drop in ophthalmology compared to other domains. |
Ranking-Based Autoencoder for Extreme Multi-label Classification (N19-1)
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| Challenge: | Existing methods to solve label dependency and noisy labeling problems are limited . experimental results show the proposed method is competitive to state-of-the-art methods . |
| Approach: | They propose a deep learning XML method with word-vector-based self-attention followed by ranking-based AutoEncoder architecture to solve these problems. |
| Outcome: | The proposed method is competitive to state-of-the-art methods on benchmark datasets. |
SudoLM: Learning Access Control of Parametric Knowledge with Authorization Alignment (2025.acl-long)
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| Challenge: | Existing preference alignment is a one-size-fits-all alignment mechanism, where the part of the large language model parametric knowledge with non-preferred features is uniformly blocked to all the users. |
| Approach: | They propose a framework that lets LLMs learn access control over parametric knowledge for users with different credentials via authorization alignment. |
| Outcome: | Experiments on two application scenarios show that the proposed framework effectively controls the user’s access to parametric knowledge and maintains its general utility. |
Securing Multi-turn Conversational Language Models From Distributed Backdoor Attacks (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have acquired the ability to handle longer context lengths and understand nuances in text, expanding their dialogue capabilities beyond a single utterance. |
| Approach: | They propose a decoding time defense that scales linearly with the input sequence length and reduces the backdoor to as low as 0.35%. |
| Outcome: | The proposed framework is generalizable, compatible with any trigger in an adversary’s toolbox in a plug-and-play manner. |
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference (2022.findings-acl)
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Kai Hui, Honglei Zhuang, Tao Chen, Zhen Qin, Jing Lu, Dara Bahri, Ji Ma, Jai Gupta, Cicero Nogueira dos Santos, Yi Tay, Donald Metzler
| Challenge: | State-of-the-art neural models typically encode document-query pairs using cross-attention for re-ranking. |
| Approach: | They propose to fine tune a pretrained encoder-decoder model using document to query generation. |
| Outcome: | The proposed model achieves comparable results to more expensive approaches while being 6.8X faster. |
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. |
Less Languages, Less Tokens: An Efficient Unified Logic Cross-lingual Chain-of-Thought Reasoning Framework (2026.acl-long)
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Chenyuan Zhang, Qiguang Chen, Xie Chen, Zhuotao Tian, Bowen Xing, Meishan Zhang, Libo Qin, Baotian Hu, Min Zhang
| Challenge: | Existing methods for cross-lingual chain-of-thought (XCoT) with self-consistency are costly due to extensive sampling of full trajectories across languages. |
| Approach: | They propose a cross-lingual chain-of-thought framework that minimizes redundancy in token usage and latency. |
| Outcome: | Experiments on polymath show that UL-XCoT reduces decoding token costs and latency by 50% . UL XCot also aggregates remaining high-quality reasoning paths via voting . |
Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)
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Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing approaches lack flexibility to address diverse and ever-evolving user queries in open domains. |
| Approach: | They propose to evaluate LLMs on open-domain knowledge that requires tools to solve diverse and ever-evolving user queries. |
| Outcome: | The proposed system outperforms baselines in the open domain task-solving benchmark. |
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. |
SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation (2026.acl-long)
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Hui Wang, Jinghua Zhao, Yifan Yang, Shujie Liu, Junyang Chen, Yanzhe Zhang, Shiwan Zhao, Jinyu Li, Jiaming Zhou, Haoqin Sun, Yan Lu, Yong Qin
| Challenge: | Existing methods for evaluating the perceptual quality of synthetic speech are limited due to the complexity of perceptual quality factors and the diversity of speech generation tasks. |
| Approach: | They propose a new paradigm for enabling large language models to conduct structured speech quality evaluation using a large-scale dataset. |
| Outcome: | The proposed model performs well across tasks and languages. |
MPR-GUI: Benchmarking and Enhancing Multilingual Perception and Reasoning in GUI Agents (2026.acl-long)
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Ruihan Chen, Qiming Li, Xiaocheng Feng, Weihong Zhong, Xiaoliang Yang, Yuxuan Gu, Zekun Zhou, Yunfei Lu, Haoyu Ren, Kun Chen, Dandan Tu, Bing Qin
| Challenge: | Existing GUI benchmarks lack fine-grained diagnostics to identify which capabilities lead to task failures. |
| Approach: | They propose a multilingual P R GUI Benchmark to assess LVLMs' language capabilities . they propose XLI to align non-English hidden states with English ones during inference . |
| Outcome: | The proposed benchmark reveals consistent gaps between English and non-English settings . it reduces the cross-lingual gaps with an average gain of 6.5% in non- English settings compared to static benchmarks . |