Papers by Wei Hao

Snapshot-Guided Domain Adaptation for ELECTRA (2022.findings-emnlp)

Challenge:	Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions.
Approach:	They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach .
Outcome:	The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size.

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Daixuan Cheng, Shaohan Huang, Jianfeng Liu, Yuefeng Zhan, Hao Sun, Furu Wei, Denvy Deng, Qi Zhang

Language Models as Inductive Reasoners (2024.eacl-long)

Challenge:	Existing domain-specific knowledge of domain-related tasks is lacking in pre-trained language models.
Approach:	They propose a domain-adaptation method which can dynamically select domain-specific tokens and guide the discriminator to emphasize them, without introducing new training parameters.
Outcome:	The proposed method can capture domain-specific knowledge of domain-related tasks without introducing new training parameters.

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Zonglin Yang, Li Dong, Xinya Du, Hao Cheng, Erik Cambria, Xiaodong Liu, Jianfeng Gao, Furu Wei

Scaling Laws for Code: Every Programming Language Matters (2026.findings-acl)

Challenge:	Inductive reasoning is a core component of human intelligence.
Approach:	They propose a task to induce natural language rules from natural language facts using natural language as representation for knowledge instead of formal language.
Outcome:	The proposed task surpasses baselines in both automatic and human evaluations.

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Jian Yang, Shuyue Guo, Linzheng Chai, Wei Zhang, Aishan Liu, Chuan Hao, Zhoujun Li, Xin Zhao, Xianglong Liu, Weifeng Lv, Bryan Dai

Enhancing Self-Attention with Knowledge-Assisted Attention Maps (2022.naacl-main)

Challenge:	Existing studies focus on language-agnostic settings, neglecting the inherently multilingual nature of modern software development.
Approach:	They propose a proportion-dependent scaling law that prioritizes high-utility languages . they propose PLs to have varying effects during pre-training that affect model performance .
Outcome:	The proposed scaling law is based on 1000+ experiments across multiple languages and models.

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Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Defu Cao, Mingliang Zhang1, Yunhai Tong, Yaming Yang, Jing Bai, Ruofei Zhang, Hao Sun, Wei Shen

Guided Knowledge Generation with Language Models for Commonsense Reasoning (2024.findings-emnlp)

Challenge:	Existing works of knowledge infusion depend on multi-task learning frameworks, which are inefficient and require large-scale retraining when new knowledge is considered.
Approach:	They propose a method which integrates knowledge-generated attention maps into the self-attention mechanism and integrates it into the model.
Outcome:	The proposed model outperforms existing methods on academic datasets and industry-scale ad relevance applications.

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Xiao Wei, Haoran Chen, Hang Yu, Hao Fei, Qian Liu

Enhancing Retrieval-Augmented Generation via Evidence Tree Search (2025.acl-long)

Challenge:	Large Language Models (LLMs) have achieved notable success in commonsense reasoning tasks, benefiting from extensive world knowledge acquired through extensive pretraining.
Approach:	They propose a method to generate knowledge explanations and to automatically assign labels based on the probability of correct answers.
Outcome:	The proposed method outperforms baselines on four widely-used commonsense reasoning benchmarks and shows that it can generate high quality knowledge leading to correct answers.

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Hao Sun, Hengyi Cai, Yuchen Li, Xuanbo Fan, Xiaochi Wei, Shuaiqiang Wang, Yan Zhang, Dawei Yin

LoopCoder: Scaling Code Intelligence via Looped Language Models (2026.findings-acl)

Challenge:	Evidence retrieval is used to enhance Large Language Models (LLMs) but in real-world applications, it often returns lengthy documents with redundant or irrelevant content, confusing downstream readers.
Approach:	They propose a framework that reformulates evidence retrieval as a dynamic tree expansion process.
Outcome:	The proposed framework outperforms existing methods on five datasets.

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Jian Yang, Wei Zhang, Shuyue Guo, Yizhi LI, Linzheng Chai, Zhengmao Ye, Shukai Liu, Yuyang Song, Jiajun Wu, Che Liu, Tianyu Zheng, Siwei Wu, Leo L, Xudong Ma, Chuan Hao, Ran Tao, Yan Xing, Jianzhou Wang, Mingjie Tang, Aishan Liu, Zhoujun Li, Xianglong Liu, Weifeng Lv, Bryan Dai

Generative Annotation for ASR Named Entity Correction (2025.emnlp-main)

Challenge:	Large language models have mastered syntax-level code generation, but complex algorithmic reasoning remains a challenge.
Approach:	They propose a recurrent inductive bias that aligns with the recursive nature of programming logic.
Outcome:	The proposed model achieves comparable performance to standard dense models with more parameters.

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Yuanchang Luo, Daimeng Wei, Shaojun Li, Hengchao Shang, Jiaxin Guo, Zongyao Li, Zhanglin Wu, Xiaoyu Chen, Zhiqiang Rao, Jinlong Yang, Hao Yang

Rethinking Text-to-SQL: Dynamic Multi-turn SQL Interaction for Real-world Database Exploration (2026.findings-acl)

Challenge:	Existing named entity correction models fail to transcribe domain-speciffcnamed entities when theforms of the wrongly-transcribed words and the ground-truth entity are signiffcantly different.
Approach:	They propose a method that utilizes speech sound features to retrieve candidate entities . it uses speech sound feature to annotate entityerrors in ASR transcripts .
Outcome:	The proposed method can bring signiffcant improvement to entity accuracy.

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Linzhuang Sun, Tianyu Guo, Hao Liang, Ruitong Liu, Yuying Li, Qifeng Cai, Jingxuan Wei, Yuchen Wu, Bihui Yu, Xiangxiang Zhang, Wentao Zhang, Bin Cui

Programming Languages in NLP

Challenge:	Structured Query Language (SQL) is the cornerstone for data-driven decision-making.
Approach:	They propose a benchmark to rigorously evaluate Large Language Models within a dynamic interaction framework.
Outcome:	The proposed benchmark aims to rigorously evaluate LLMs within a dynamic interaction framework.

CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs (2025.findings-emnlp)

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Qi Xu, Qian Liu, Hao Fei, Hang Yu, Shuhao Guan, Xiao Wei

A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM𝛥 Integration into Upcycled MoE (2026.acl-long)

Challenge:	Existing work focuses on enabling LLMs to leverage legal rules to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules.
Approach:	They propose a legal paragraph prediction task that aims to predict the legal paragraph given criminal facts and a framework CLEAR to enhance their legal reasoning ability.
Outcome:	The proposed model improves the ability of LLMs to analyze legal cases with the guidance of legal rule insights.

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Hao Zhou, Tianhao Li, Zhijun Wang, Shuaijie She, Linjuan Wu, Hao-Ran Wei, Baosong Yang, Jiajun Chen, Shujian Huang

Green & Sustainable NLP

Challenge:	Large Language Models (LLMs) are expensive and require extensive Continued Pre-Training and data-intensive alignment to expand.
Approach:	They propose a method which upcycles a dense model into a Mixture-of-Experts architecture, allocating different experts to different languages.
Outcome:	Experiments show that the proposed model upcycles a dense model into a Mixture-of-Experts(MoE) architecture, allocating different experts to different languages.

Span-based Localizing Network for Natural Language Video Localization (2020.acl-main)

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Hao Zhang, Aixin Sun, Wei Jing, Joey Tianyi Zhou

Visual Data in NLP

Challenge:	Existing approaches to NLVL are either ranking tasks or regressing the target video span.
Approach:	They propose a video span localizing network to solve a natural language video localization task using a span-based QA approach.
Outcome:	The proposed network outperforms the state-of-the-art methods on three benchmark datasets.

MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering (2025.findings-acl)

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Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu, Shu Wei, An-Lan Wang, Chunhui Lin, Hao Feng, Zhen Zhao, Yanjie Wang, Yuliang Liu, Hao Liu, Xiang Bai, Can Huang

E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning (2022.findings-acl)

Challenge:	Text-Centric Visual Question Answering (TEC-VQA) is a text-centric visual task understanding tool.
Approach:	They introduce a benchmark that features human expert annotations across 9 languages . they prioritize the text in question-answer pairs while disregarding visual text in images .
Outcome:	The proposed benchmarks prioritize the text in question-answer pairs while disregarding visual text in images.

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Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, Hao Zhou

CR-LLM: A Dataset and Optimization for Concept Reasoning of Large Language Models (2024.findings-acl)

Challenge:	Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.
Approach:	They propose an explanation benchmark for analogical reasoning using a Civil Service exam . they use a free-text explanation scheme to explain whether an analogy should be drawn .
Outcome:	The proposed benchmark is very challenging for state-of-the-art models, it is found.

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Nianqi Li, Jingping Liu, Sihang Jiang, Haiyun Jiang, Yanghua Xiao, Jiaqing Liang, Zujie Liang, Feng Wei, Jinglei Chen, Zhenghong Hao, Bing Han

UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning (2021.acl-long)

Challenge:	Existing concept reasoning related datasets suffer from modeledge leakage and context leakage.
Approach:	They propose a concept reasoning for large language models with modeledge leakage prevention and context leakage preventive methods to improve the models' conceptual reasoning abilities.
Outcome:	The proposed method significantly improves the existing models and reasoning methods, achieving a 7% increase in accuracy compared to CoT and showing better granularity.

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Wei Li, Can Gao, Guocheng Niu, Xinyan Xiao, Hao Liu, Jiachen Liu, Hua Wu, Haifeng Wang

EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning (2021.emnlp-main)

Challenge:	Existing pre-training methods focus on single-modal tasks or multi-modal ones . large-scale pre- training has drawn much attention in both the community of Compute Vision (CV) and Natural Language Processing (NLP).
Approach:	They propose a UNIfied-MOdal pre-training architecture which can adapt to both single-modal and multi-modal understanding and generation tasks.
Outcome:	The proposed model can learn more generalizable representations with rich non-paired single-modal data.

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Hao Zhou, Minlie Huang, Yong Liu, Wei Chen, Xiaoyan Zhu

CLEAN–EVAL: Clean Evaluation on Contaminated Large Language Models (2024.findings-naacl)

Challenge:	Existing knowledge-grounded conversation models lack knowledge that occurs in training data, resulting in incomplete knowledge generation.
Approach:	They propose an Entity-Agnostic Representation Learning method to introduce knowledge graphs to informative conversation generation using context of conversations and relational structure of knowledge graph.
Outcome:	The proposed model generates more informative, coherent, and natural responses than baseline models.

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Wenhong Zhu, Hongkun Hao, Zhiwei He, Yun-Ze Song, Jiao Yueyang, Yumeng Zhang, Hanxu Hu, Yiran Wei, Rui Wang, Hongyuan Lu

Se2: Sequential Example Selection for In-Context Learning (2024.findings-acl)

Challenge:	Existing methods to evaluate large language models are prone to data contamination.
Approach:	They propose a method which parses contaminated data and back-translates it into a candidate set.
Outcome:	The proposed method reduces data contamination and evaluates the LLMs more cleanly.

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Haoyu Liu, Jianfeng Liu, Shaohan Huang, Yuefeng Zhan, Hao Sun, Weiwei Deng, Furu Wei, Qi Zhang

GeAR: Generation Augmented Retrieval (2025.findings-acl)

Challenge:	Prior work has explored the selection of examples for in-context learning, neglecting the internal relationships between examples and exist an inconsistency between training and inference.
Approach:	They propose a sequential-aware method that leverages the LLM’s feedback on varying context, aiding in capturing inter-relationships and sequential information among examples.
Outcome:	Experiments on 23 NLP tasks show that Se2 surpasses baselines and achieves 42% relative improvement over random selection.

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Haoyu Liu, Shaohan Huang, Jianfeng Liu, Yuefeng Zhan, Hao Sun, Weiwei Deng, Feng Sun, Furu Wei, Qi Zhang

Knowledge Neurons in Pretrained Transformers (2022.acl-long)

Challenge:	Document retrieval techniques are used to compute semantic similarity between a query and documents, but the scalar similarity fails to reflect enough information, hindering the interpretation of retrieval results.
Approach:	They propose a method which improves the global document-query similarity through contrastive learning and integrates well-designed fusion and decoding modules.
Outcome:	The proposed method improves the global document-query similarity through contrastive learning and integrates well-designed fusion and decoding modules.

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Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, Furu Wei

CoIR: A Comprehensive Benchmark for Code Information Retrieval Models (2025.acl-long)

Challenge:	Existing studies show that pretrained language models are good at recalling factual knowledge without fine-tuning.
Approach:	They propose a method to identify neurons that express factual knowledge in pretrained Transformers by filling-in-the-blank cloze queries.
Outcome:	The proposed method can be used to edit, erase, and update factual knowledge without fine-tuning.

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Xiangyang Li, Kuicai Dong, Yi Quan Lee, Wei Xia, Hao Zhang, Xinyi Dai, Yasheng Wang, Ruiming Tang

GAPO: Robust Advantage Estimation for Real-World Code LLMs (2026.findings-acl)

Challenge:	Existing methods and benchmarks for information retrieval are inadequately representing the diversity of code in various domains and tasks.
Approach:	They propose a benchmark specifically designed to assess code retrieval capabilities.
Outcome:	The proposed benchmark aims to invigorate research in the code retrieval domain . it shares the same data schema as other popular benchmarks like MTEB and BEIR .

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Jianqing Zhang, Zhezheng Hao, Wei Xia, Hande Dong, Hong Wang, Chenxing Wei, Yuyan Zhou, Yubin Qi, Qiang Lin, Jian Cao

Robustness in NLP

Challenge:	Reinforcement learning (RL) is widely used for post-training large language models (LLMs) in code editing, but in real-world code editing scenarios, reward distributions are often skewed with unpredictable noise, leading to distorted advantage computation and increased rollout outliers.
Approach:	They propose a group-relative method that finds an interval with the highest SNR and uses the median of that interval as an adaptive Q to replace the group mean in advantage calculation.
Outcome:	The proposed method improves on nine instruction-tuned LLMs while remaining plug-and-play and efficient.

AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning (2024.emnlp-main)

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Hao Sun, Jiayi Wu, Hengyi Cai, Xiaochi Wei, Yue Feng, Bo Wang, Shuaiqiang Wang, Yan Zhang, Dawei Yin

LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration (2024.emnlp-main)

Challenge:	Recent advances in large language models have been remarkable . users face a choice between using cloud-based LLMs for generation quality or local-based ones for lower computational cost .
Approach:	They propose a new LLM utilization paradigm that facilitates collaborative operation . they evaluate AdaSwitch across 7 benchmarks and compare it to other LLMs .
Outcome:	The proposed model improves performance of local and cloud agents across 7 benchmarks . it achieves competitive results compared to the cloud agent while utilizing less computational overhead.

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Jun Zhao, Can Zu, Xu Hao, Yi Lu, Wei He, Yiwen Ding, Tao Gui, Qi Zhang, Xuanjing Huang

Question Answering

Challenge:	Large language models (LLMs) have been successful in understanding language and processing text, but their cost prohibits their practical applications.
Approach:	They propose a multi-agent collaboration method that breaks down lengthy documents into smaller, more manageable chunks and organizes the member agents to read their assigned chunks.
Outcome:	The proposed method achieves 16.42% and 1.63% accuracy gains over existing models on single-hop and multi-hop QA settings.

MPL: Multiple Programming Languages with Large Language Models for Information Extraction (2025.findings-acl)

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Bo Li, Gexiang Fang, Wei Ye, Zhenghua Xu, Jinglei Zhang, Hao Cheng, Shikun Zhang

Cross-Domain Audio Deepfake Detection: Dataset and Analysis (2024.emnlp-main)

Challenge:	Existing research focuses on Python for code-style simulation, overlooking the potential of other widely-used PLs during the supervised fine-tuning phase.
Approach:	They propose a framework that incorporates programming languages into IE tasks . they introduce function-prompt with virtual running to simulate code-style inputs .
Outcome:	The proposed framework exploits the potential of different programming languages during the supervised fine-tuning phase.

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Yuang Li, Min Zhang, Mengxin Ren, Xiaosong Qiao, Miaomiao Ma, Daimeng Wei, Hao Yang

Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020.emnlp-main)

Challenge:	Existing audio deepfake detection datasets are outdated and lack generalization capabilities.
Approach:	They construct a new cross-domain audio deepfake detection dataset comprising over 300 hours of speech data that is generated by five advanced zero-shot TTS models.
Outcome:	The proposed models achieve 4.1% and 6.5% error rates in the cross-domain ADD dataset generated by five advanced zero-shot TTS models.

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Lu Xu, Hao Li, Wei Lu, Lidong Bing

Sentiment Analysis

Challenge:	Existing research efforts focus on extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment.
Approach:	They propose a position-aware tagging scheme that can extract triplets using a sequence tapping approach.
Outcome:	The proposed model improves performance on multiple datasets and compares with existing models.

TabPrompt: Graph-based Pre-training and Prompting for Few-shot Table Understanding (2023.findings-emnlp)

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Rihui Jin, Jianan Wang, Wei Tan, Yongrui Chen, Guilin Qi, Wang Hao

BizCompass: Benchmarking the Reasoning Capabilities of LLMs in Business Knowledge and Applications (2026.findings-acl)

Challenge:	Existing methods of Table Understanding (TU) focus on the textual content within the tabular data, disregarding the topological information of the table.
Approach:	They propose a framework that uses tabs to understand tabular data without ignoring the topological information of the table.
Outcome:	The proposed framework outperforms baselines in few-shot table understanding tasks.

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Jianing Hao, Yuhe Wu, Yuanjian Xu, Shichang Meng, Shuai Yuan, Wei Zeng, Zixuan Wang, Guang Zhang

Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search (2023.emnlp-main)

Challenge:	Existing benchmarks focus on narrow tasks and leave a fundamental question unanswered . Existing models only focus on specific tasks, requiring rigorous reasoning and knowledge .
Approach:	They propose a benchmark to connect theoretical foundations with practical business knowledge and applications.
Outcome:	The benchmark systematically evaluates both open-source and commercial LLMs . it reveals how theoretical knowledge translates into practical performance in business .

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Xiang Geng, Yu Zhang, Zhejian Lai, Shuaijie She, Wei Zou, Shimin Tao, Hao Yang, Jiajun Chen, Shujian Huang

Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations (2025.findings-naacl)

Challenge:	evaluating the quality of machine translation outputs becomes increasingly essential with the rapid development of machine language (MT).
Approach:	They propose to generate pseudo data using the MT model with constrained beam search (CBSQE) they propose to preserve the reference parts with high MT probabilities as correct translations .
Outcome:	The proposed model outperforms strong baselines in both supervised and unsupervised settings.

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Hao Yang, Hongyuan Lu, Xinhua Zeng, Yang Liu, Xiang Zhang, Haoran Yang, Yumeng Zhang, Shan Huang, Yiran Wei, Wai Lam

Dialogue Systems & Conversational Agents

Challenge:	a new paradigm for dialogue systems is being developed to mimic human interactions . the current single-step dialogue paradigm lacks the depth and fluidity of human interactions.
Approach:	They propose a step-by-step dialogue paradigm that mimics human interactions . they use a dataset to fine-tune existing language models .
Outcome:	The proposed system mimics the dynamic nature of human conversations . it is compared with existing paradigms and will be released later this year .

Learning to Rewrite: Generalized LLM-Generated Text Detection (2025.acl-long)

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Wei Hao, Ran Li, Weiliang Zhao, Junfeng Yang, Chengzhi Mao

INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion (2023.findings-emnlp)

Challenge:	Existing detectors for Large Language Models (LLMs) struggle to generalize in open-world settings.
Approach:	They propose a framework to detect LLM-generated text with exceptional generalization to unseen domains by reinforcing LLMs’ inherent rewriting tendencies.
Outcome:	The proposed framework outperforms state-of-the-art detection methods by 23.04% in AUROC, 35.10% for out-of distribution tests, and 48.66% under adversarial attacks.

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Hengchao Shang, Zongyao Li, Daimeng Wei, Jiaxin Guo, Minghan Wang, Xiaoyu Chen, Lizhi Lei, Hao Yang

MdEval: Massively Multilingual Code Debugging (2026.findings-acl)

Challenge:	Existing models for word-level autocompletion (WLAC) only use human typed sequences as prefixes in decoding module.
Approach:	They propose a novel iterative nonautoregressive instruct generation model for WLAC task . it uses human typed sequences and iterating decoding with subwords to fully utilize input information.
Outcome:	The proposed model is more competent in dealing with low-frequency words, and achieves state-of-the-art results on the WMT22 and benchmark datasets.

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Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li

Pretraining Context Compressor for Large Language Models with Embedding-Based Memory (2025.acl-long)

Challenge:	Existing benchmarks primarily focus on Python and are limited in terms of language diversity.
Approach:	They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions.
Outcome:	The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task.

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Yuhong Dai, Jianxun Lian, Yitian Huang, Wei Zhang, Mingyang Zhou, Mingqi Wu, Xing Xie, Hao Liao

Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph (2020.emnlp-main)

Challenge:	Efficient processing of long contexts in large language models is essential for real-world applications such as retrieval-augmented generation and in-context learning.
Approach:	They propose a decoupled compressor-LLM framework that preserves contextual information within condensed embedding representations.
Outcome:	The proposed framework outperforms baseline models in three domains and across eight datasets while adapting to different downstream LLMs.

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Xin Lv, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Wei Zhang, Yichi Zhang, Hao Kong, Suhui Wu

Structured Data in NLP

Challenge:	Existing reasoning methods for sparse KGs are incomplete and lack of evidential paths to target entities makes multi-hop reasoning difficult.
Approach:	They propose a multi-hop reasoning model over sparse KGs to solve this problem . they use latent prediction of embedding-based models to make the model perform more potential path search over sparses .
Outcome:	The proposed method outperforms state-of-the-art models on five datasets from Freebase, NELL and Wikidata.

Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction (2021.emnlp-main)

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Kailong Hao, Botao Yu, Wei Hu

Robustness in NLP

Challenge:	Existing methods for relation extraction ignore the incompleteness of existing knowledge bases . current methods are too weak and cause noises when training and testing are not based on training data.
Approach:	They propose a method to automatically align unstructured text with relation instances in a knowledge base . they use heuristics to leverage the memory mechanism of deep neural networks to find out possible FN samples .
Outcome:	Experiments on two wildly-used benchmark datasets show the effectiveness of the proposed method.

DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation (2025.findings-acl)

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Xinglin Lyu, Wei Tang, Yuang Li, Xiaofeng Zhao, Ming Zhu, Junhui Li, Yunfei Lu, Min Zhang, Daimeng Wei, Hao Yang, Min Zhang

Calibrating Inference Time Alignment with Sequence-level Risk Accumulation (2026.acl-long)

Challenge:	Document-level context is crucial for speech translation due to noise from ASR . incorporating document-level contextual information into ST remains a challenge .
Approach:	They develop an online framework that integrates document-level context into machine translation . they use document-based modules to integrate document- level context into ST .
Outcome:	The proposed framework outperforms baselines in sentence and discourse metrics . it can correct ASR transcription errors and improve translation performance .

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Shanwen Tan, Ziyang Dong, Wei Ju, Yiwei Fu, Hao Wu, Kun Wang, Yifan Wang, Ziyue Qiao

Entity-Aware Abstractive Multi-Document Summarization (2021.findings-acl)

Challenge:	Existing approaches to decode large language models (LLMs) often over-reject benign information, limiting their generalizability in real-world scenarios where harmful and benign information coexist.
Approach:	They propose a framework to regulate decoding alignments for Large Language Models (LLMs) they employ a reward-guided branch decoding paradigm to incorporate safety awareness during generation.
Outcome:	The proposed framework achieves superior performance on four attack benchmarks and two neutral datasets.

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Hao Zhou, Weidong Ren, Gongshen Liu, Bo Su, Wei Lu

Summarization

Challenge:	Existing models for multidocument summarization do not focus on explicitly modeling the underlying semantic information across documents.
Approach:	They propose an entityaware model for abstractive multi-document summarization that augments the classical Transformer-based encoder-decoder framework with a heterogeneous graph consisting of text units and entities as nodes.
Outcome:	The proposed model can deal with saliency and redundancy issues explicitly and can be used with pre-trained language models, arriving at improved performance.

Towards Verifiable Text Generation with Evolving Memory and Self-Reflection (2024.emnlp-main)

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Hao Sun, Hengyi Cai, Bo Wang, Yingyan Hou, Xiaochi Wei, Shuaiqiang Wang, Yan Zhang, Dawei Yin

M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models (2025.findings-emnlp)

Challenge:	Large language models (LLMs) often produce factually incorrect information, also known as hallucination.
Approach:	They propose a framework for verifiable text generation with evolving memory and self-reflection that incorporates long-term memory to retain documents and recent documents.
Outcome:	The proposed framework outperforms baselines on five datasets across three knowledge-intensive tasks.

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Jiaxin Guo, Daimeng Wei, Yuanchang Luo, Hengchao Shang, Zongyao Li, Jinlong Yang, Zhanglin Wu, Zhiqiang Rao, Shimin Tao, Hao Yang

Diversity Helps Jailbreak Large Language Models (2025.naacl-long)

Challenge:	a new ensemble decoding approach enhances the performance of Large Language Models.
Approach:	They propose a multi-prompt ensemble decoding approach to enhance LLM performance . they submit n variations of prompts with X to LLMs in batch mode to decode and derive probability distributions .
Outcome:	The proposed method improves pass@k rates, LENS metrics and BLEU scores on diverse NLP tasks.

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Weiliang Zhao, Daniel Ben-Levi, Wei Hao, Junfeng Yang, Chengzhi Mao

Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers (2023.findings-acl)

Challenge:	Existing methods for jailbreaking large language models rely on laborious human engineering and whitebox access to model internals.
Approach:	They propose a method that instructs large language models to deviate from prior context and generate harmful outputs by instructing them to deviat from previous attacks.
Outcome:	The proposed method achieves a 62.83% higher success rate in compromising ten leading chatbots, while using only 12.9% of the queries.

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Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Shuming Ma, Zhifang Sui, Furu Wei

From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models (2026.findings-acl)

Challenge:	Large pretrained language models have shown surprising in-context learning ability . despite the great success in performance, its working mechanism remains unclear .
Approach:	They explain language models as meta-optimizers and understand in-context learning as implicit finetuning . they find that Transformer attention has a dual form of gradient descent .
Outcome:	The proposed model can predict labels for unseen inputs without parameter updates . the proposed model outperforms smaller models with a single parameter update .

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Junlong Tong, Zilong Wang, YuJie Ren, Peiran Yin, Hao Wu, Wei Zhang, Xiaoyu Shen

GOME: Grounding-based Metaphor Binding With Conceptual Elaboration For Figurative Language Illustration (2024.emnlp-main)

Challenge:	Existing definitions of streaming LLMs are fragmented and lack a systematic taxonomy . large language models are pre-trained on static and full-context corpora .
Approach:	They propose a systematic taxonomy of current streaming Large Language Models and propose underlying methodologies for streaming LLMs.
Outcome:	The proposed model is based on data flow and dynamic interaction to clarify existing ambiguities.

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Linhao Zhang, Jintao Liu, Li Jin, Hao Wang, Kaiwen Wei, Guangluan Xu

Visual Data in NLP

Challenge:	Existing Large Language Models (LLMs) and multimodal models are unable to illustrate figurative language based on literal objects, ignoring the underlying groundings and associations across disparate metaphorical domains.
Approach:	They propose a grounding-based method for metaphor illustration that integrates metaphorical knowledge into systematic instructions for existing large language models.
Outcome:	The proposed method is superior to existing LLMs, diffusion models, or their direct collaboration.

Prompts Can Play Lottery Tickets Well: Achieving Lifelong Information Extraction via Lottery Prompt Tuning (2023.acl-long)

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Zujie Liang, Feng Wei, Yin Jie, Yuxi Qian, Zhenghong Hao, Bing Han

ClimAgent: LLM as Agents for Autonomous Open-ended Climate Science Analysis (2026.findings-acl)

Challenge:	Existing research on information extraction tasks focuses on one specific task, but in real-world scenarios, new data of different IE tasks and domains come in a stream over time.
Approach:	They propose a parameter- and deployment-efficient prompt tuning method to evaluate the UIE system under a “lifelong learning” setting.
Outcome:	The proposed method is able to learn new tasks without forgetting old ones and expand knowledge and functionalities without retraining the whole system.

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Hao Wang, Jindong Han, Wei Fan, Hao Liu

Challenge:	Existing approaches to climate research are limited to simple Q A tasks . a lack of data and computational expertise has created bottlenecks .
Approach:	They propose a general-purpose autonomous framework to perform end-to-end climate research tasks across diverse climate sub-fields.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks in rigorousness and practicality.

Parallel Attention Network with Sequence Matching for Video Grounding (2021.findings-acl)

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Hao Zhang, Aixin Sun, Wei Jing, Liangli Zhen, Joey Tianyi Zhou, Siow Mong Rick Goh

Investigating Learning Dynamics of BERT Fine-Tuning (2020.aacl-main)

Challenge:	Existing approaches to video grounding are sensitive to quality of proposals and inefficient because all proposal-query pairs are compared.
Approach:	They propose a Parallel Attention Network with Sequence matching to capture selfmodal contexts and cross-modal attentive information between video and text.
Outcome:	The proposed approach is superior to state-of-the-art methods on three datasets.

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Yaru Hao, Li Dong, Furu Wei, Ke Xu

Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment (2025.emnlp-main)

Challenge:	Recent studies have shown that the fine-tuning process improves performance on downstream tasks.
Approach:	They propose two new pre-training tasks to improve the model performance on downstream tasks.
Outcome:	The proposed model achieves state-of-the-art on a wide array of NLP tasks.

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Hao Li, Lijun Li, Zhenghao Lu, Xianyi Wei, Rui Li, Jing Shao, Lei Sha

DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware Translators (2024.emnlp-main)

Challenge:	Recent studies show that fine-tuning with benign data can compromise safety of aligned LLMs.
Approach:	They propose a Layer-Aware Representation Filtering method that detects safety-degrading layers within the LLM and leverages their representations to detect them.
Outcome:	The proposed method can detect safety-degrading features in benign data and remove them from the model.

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Xinglin Lyu, Junhui Li, Yanqing Zhao, Min Zhang, Daimeng Wei, Shimin Tao, Hao Yang, Min Zhang

Divide and Conquer: Legal Concept-guided Criminal Court View Generation (2024.findings-emnlp)

Challenge:	Concatenating large language models are adapted to context-aware neural machine translation in a concatenated way . a recent paradigm shift has been witnessed in discourse-related challenges such as zero pronoun translation .
Approach:	They propose an alternative adaptation approach to make large language models discriminately model and utilize inter- and intra-sentence contexts.
Outcome:	The proposed approach outperforms concatenation mode and improves performance in discourse modeling.

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Qi Xu, Xiao Wei, Hang Yu, Qian Liu, Hao Fei

Neural Chinese Address Parsing (N19-1)

Challenge:	Existing methods for creating rationales for criminal cases do not pay enough attention to the important legal concepts.
Approach:	They propose a legal concept-guided court view generation framework that generates rationales based on predicted legal concepts . they first divide the court view into sub-views, then employ a solver and verifier to generate and select rationale.
Outcome:	The proposed model generates coherent and coherent court views on a real-world criminal case dataset.

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Hao Li, Wei Lu, Pengjun Xie, Linlin Li

Challenge:	Recent research shows that systems that perform address parsing can be useful for building e-commerce or product recommendation systems.
Approach:	They propose a task of parsing Chinese addresses into semantically meaningful chunks using a linear-chain structure.
Outcome:	The proposed model is able to capture complex dependencies between labels that cannot be readily captured by a simple linear-chain structure.

Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation (2021.findings-emnlp)

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Kaiyu Huang, Hao Yu, Junpeng Liu, Wei Liu, Jingxiang Cao, Degen Huang

Syntactic Text Processing

Challenge:	Existing methods for Chinese word segmentation have high performance on benchmarks but are limited by the small-scale annotated corpus.
Approach:	They propose a framework that incorporates a lexicon-based graph convolutional network into the Transformer encoder to improve Chinese word segmentation (CWS) Chinese word is an essential and pre-processing step for many downstream NLP tasks.
Outcome:	The proposed framework captures the information of candidate words and improves performance on benchmarks and datasets.

DEIE: Benchmarking Document-level Event Information Extraction with a Large-scale Chinese News Dataset (2024.lrec-main)

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Yubing Ren, Yanan Cao, Hao Li, Yingjie Li, Zixuan ZM Ma, Fang Fang, Ping Guo, Wei Ma

Information Extraction & Text Mining

Challenge:	Existing event-based datasets mainly target sentence-level tasks . current models struggle with "document" annotation, a key feature of the current model .
Approach:	They propose a large-scale document-level event information extraction dataset with over 56,000+ events and 242,000+ arguments.
Outcome:	The proposed dataset has over 56,000+ events and 242,000+ arguments.

Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026.findings-acl)

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Linzhuang Sun, Mingyang Chen, Hao Liang, Tianpeng Li, Zhou Yijie, Chenzheng Zhu, Tianyu Guo, Huanyao Zhang, Jingxuan Wei, Bihui Yu, Fan Yang, Wentao Zhang

Challenge:	Existing methods for training effective AI agents often resort to synthetic data generation.
Approach:	They propose a plug-and-play framework for data quality control in tool-use scenarios . they construct a tool-verify dataset and release a benchmark to assess its performance .
Outcome:	The proposed framework surpasses Qwen2.5-72B-Instruct on Tool-V-Bench and the previous APIGen-MT dataset.

METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models (2026.acl-long)

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Pengfeng Li, Chen Huang, Chaoqun Hao, Hongyao Chen, Xiao-Yong Wei, Wenqiang Lei, See-Kiong Ng

Text Style Transfer Back-Translation (2023.acl-long)

Challenge:	Existing benchmarks evaluate contextual causal reasoning in fragmented settings, failing to ensure context consistency or cover the full causal hierarchy.
Approach:	They use a unified context to benchmark large language models' contextual causal reasoning skills.
Outcome:	The proposed benchmarks show that LLMs are susceptible to distraction by irrelevant but factually correct information at lower level of causality.

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Daimeng Wei, Zhanglin Wu, Hengchao Shang, Zongyao Li, Minghan Wang, Jiaxin Guo, Xiaoyu Chen, Zhengzhe Yu, Hao Yang

Learning Explicit and Implicit Structures for Targeted Sentiment Analysis (D19-1)

Challenge:	Current methods require large amount of bilingual training data, which is challenging and sometimes impossible task.
Approach:	They propose a method to modify the style of inputs by modifying the source side of BT data.
Outcome:	The proposed method significantly improves translation quality against popular BT benchmarks on high-resource and low-resourced language pairs.

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Hao Li, Wei Lu

Sentiment Analysis

Challenge:	Existing research efforts focus on targeting sentiment analysis as a sequence labeling problem, building models that can capture explicit structures in the output space.
Approach:	They argue that both implicit and explicit structural information are crucial for building a successful targeted sentiment analysis model.
Outcome:	The proposed model outperforms existing models by capturing implicit and explicit structural information.

MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions (2023.acl-long)

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Hao Sun, Zhexin Zhang, Fei Mi, Yasheng Wang, Wei Liu, Jianwei Cui, Bin Wang, Qun Liu, Minlie Huang

Natural Language Interfaces

Challenge:	A moral dialogue system aligned with users’ values could enhance conversation engagement and user connections.
Approach:	They propose a framework to train and evaluate moral dialogue systems based on communication mechanisms of morality and a method to construct moral discussions between simulated users and the dialogue system.
Outcome:	The proposed framework can train and evaluate moral dialogue systems based on simulated users and their values .

A Novel Paradigm Boosting Translation Capabilities of Large Language Models (2024.findings-naacl)

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Jiaxin Guo, Hao Yang, Zongyao Li, Daimeng Wei, Hengchao Shang, Xiaoyu Chen

Learning with Structured Representations for Negation Scope Extraction (P18-2)

Challenge:	Existing studies on LLMs focused on supervised fine-tuning but their effectiveness has been limited.
Approach:	They propose a paradigm consisting of three stages: Secondary Pre-training using extensive monolingual data, Continual Pre- training with interlinear text format documents, and Leveraging source-language consistent instruction for supervised fine-tuning.
Outcome:	The proposed approach surpasses previous work and achieves superior performance compared to models such as NLLB-54B(CITATION) and GPT3.5-text-davinci-003.

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Hao Li, Wei Lu

Representation Learning

Challenge:	Existing approaches to negation scope detection have been criticized for capturing information related to negations, long-distance dependencies and structural information.
Approach:	They propose to use conditional random fields, semi-Markov CRF and latent-variable CRF models to capture useful information such as long-distance dependencies and some latent structural information.
Outcome:	The proposed approaches can capture useful information such as features related to negation cue, long-distance dependencies and some latent structural information.

Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement (2026.acl-long)

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Qimin Zhong, Hao Liao, Haiming Qin, Mingyang Zhou, Rui Mao, Wei Chen, Naipeng Chao

Plug-and-Play Data Module for Code RL: Adaptive Ambiguity Replay (2026.findings-acl)

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.

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Jianqing Zhang, Wei Xia, Zhezheng Hao, Hong Wang, Hande Dong, Qiang Lin, Yang Liu, Jian Cao, Qiang Yang

Towards Stable and Effective Reinforcement Learning for Mixture-of-Experts (2026.acl-long)

Challenge:	Existing approaches to reinforcement learning (RL) rely on static, in-epoch metrics that overlook training dynamics, often introducing low-utility or outdated data.
Approach:	They propose a plug-and-play module that prioritizes cross-epoch ambiguous samples to neutralize the noise from stale experiences.
Outcome:	Extensive experiments on nine LLMs show that Adaptive Ambiguity Replay outperforms state-of-the-art baselines on real-world code editing tasks.

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Di Zhang, Xun Wu, Shaohan Huang, Lingjie Jiang, Yaru Hao, Li Dong, Zewen Chi, Zhifang Sui, Furu Wei

MemTR: Enhancing Tool-Calling Reliability via Uncertainty-Triggered FFN-Space Retracing (2026.findings-acl)

Challenge:	Reinforcement learning with verifiable rewards (RLVR) training with Mixture-of-Experts policies remains fragile and prone to reward collapse.
Approach:	They propose a router shift-based policy optimization method that computes a per-token router-shift ratio conditioned on the previously activated experts and applies stop-gradient and a lower-bound floor.
Outcome:	The proposed method achieves better performance and greater stability than previous methods.

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Hongtao Duan, Lu Jiang, Minying Zhang, Xiaobing Zhu, Tianpeng Bu, Hao Jiang, Xinyu Wei, Lulu hu

Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement (2025.acl-long)

Challenge:	Existing tool-calling methods rely on costly tool-use training data or only constrain syntax, leaving tool selection and argument value errors largely unsolved.
Approach:	They propose a method that decodes tool evidence from the tool library and mixes it into the output at the uncertain layer.
Outcome:	The proposed method reduces tool calling failures by 2%–9% with only 1%–2% runtime overhead.

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Yichen Dong, Xinglin Lyu, Junhui Li, Daimeng Wei, Min Zhang, Shimin Tao, Hao Yang

Machine Translation

Challenge:	Recent research has shown that large language models (LLMs) can enhance translation quality through self-refinement.
Approach:	They propose to extend translation refinement from sentence-level to document-level by using document-to-document (Doc2Doc) translations.
Outcome:	The proposed method improves translation quality across ten translation tasks with LLaMA-3-8B-Instruct and Mistral-Nemo-Instru.

UNIMO-2: End-to-End Unified Vision-Language Grounded Learning (2022.findings-acl)

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Wei Li, Can Gao, Guocheng Niu, Xinyan Xiao, Hao Liu, Jiachen Liu, Hua Wu, Haifeng Wang

LCMA-SRT: Language-Conditional Mixture-of-Experts Adapters for Joint Multilingual Speech Recognition and Translation (2026.acl-long)

Challenge:	Existing methods for vision-language pre-training can only learn from aligned image-caption data and rely heavily on expensive regional features.
Approach:	They propose an end-to-end unified-modal pre-training framework for joint learning . they propose to conduct grounded learning on both images and texts via a sharing grounded space .
Outcome:	The proposed model improves visual and visual semantic alignment on images and texts.

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Nanjie Li, Xiaoyong Guo, Hao Huang, Xu Haihua, Wei Shi

Enhancing Large Language Models for Document-Level Translation Post-Editing Using Monolingual Data (2025.coling-main)

Challenge:	Existing hierarchical transducers suffer from negative transfer and unstable target-language generation, while training separate models for each direction is computationally prohibitive.
Approach:	They propose a hierarchical transducer with language-conditional Mixture-of-Experts adapters to improve multilingual joint automatic speech recognition and speech translation.
Outcome:	Experiments on Europarl-ST (9 languages, 72 directions) show that LCMA-SRT improves both ASR and ST within a single joint model, reducing average WER and improving BLEU and COMET over strong hierarchical transducer baselines.

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Zongyao Li, Zhiqiang Rao, Hengchao Shang, Jiaxin Guo, Shaojun Li, Daimeng Wei, Hao Yang

OFFSIDE: Benchmarking Unlearning Misinformation in Multimodal Large Language Models (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have excellent performance in many tasks, but they still face challenges in document translation.
Approach:	They propose a method that leverages the capabilities of Large Language Models to optimize document translation using only monolingual data.
Outcome:	The proposed method improves translation quality and improves contextual consistency in document translation using only monolingual data.

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Hao Zheng, Zirui Pang, Ling Li, Zhijie Deng, Yuhan Pu, Zhaowei Zhu, Xiaobo Xia, Jiaheng Wei

OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)

Challenge:	Existing benchmarks for MU are limited by a lack of image diversity and coarse-grained unlearning targets.
Approach:	They propose a benchmark to evaluate misinformation unlearning in MLLMs . OFFSIDE supports advanced unlearning targets such as fine-grained unlearning and visual rumor removal.
Outcome:	OFFSIDE supports advanced unlearning targets, such as fine-grained unlearning and visual rumor removal.

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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang

QAEncoder: Towards Aligned Representation Learning in Question Answering Systems (2025.acl-long)

Challenge:	Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence.
Approach:	They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included .
Outcome:	The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model.

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Zhengren Wang, Qinhan Yu, Shida Wei, Zhiyu Li, Feiyu Xiong, Xiaoxing Wang, Simin Niu, Hao Liang, Wentao Zhang

Explainable Recommendation with Personalized Review Retrieval and Aspect Learning (2023.acl-long)

Challenge:	Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses, but the inherent gap between user queries and relevant documents hinders precise matching.
Approach:	They propose a retrieval-augmented generation (RAG)-based approach to bridge this gap by attaching document fingerprints to the embedding to estimate the expectation of potential queries.
Outcome:	Experiments across diverse datasets, languages, and embedding models confirm the proposed solution is simple-yet-effective with zero additional index storage, retrieval latency, training costs, or catastrophic forgetting and hallucination issues.

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Hao Cheng, Shuo Wang, Wensheng Lu, Wei Zhang, Mingyang Zhou, Kezhong Lu, Hao Liao

Explainability & Interpretability in NLP

Challenge:	Recent years have witnessed a growing interest in the development of explainable recommendation models.
Approach:	They propose a model that combines prediction and generation tasks to produce more persuasive explanations by obtaining additional information from the training sets.
Outcome:	The proposed model outperforms state-of-the-art models on three datasets and shows that it is more persuasive than previous models.

Advancing Sequential Numerical Prediction in Autoregressive Models (2025.acl-short)

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Xiang Fei, Jinghui Lu, Qi Sun, Hao Feng, Yanjie Wang, Wei Shi, An-Lan Wang, Jingqun Tang, Can Huang

Challenge:	Autoregressive models are the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlooking the coherent structure of numerical sequences.
Approach:	They propose a novel approach to entropy loss by extending the Earth Mover’s Distance to preserve ordinal relationships between numerical values and sequence-level to penalize the overall discrepancy between predicted and actual sequences.
Outcome:	Extensive experiments show that NTIL improves numerical prediction and integrates effectively with LLMs/MLLMs.

An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor Isomorphism (2022.acl-long)

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Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, ZongYu Wang, Rui Xie, Wei Wu, Man Lan

Structured Data in NLP

Challenge:	Existing methods focus on graph representation learning, but decoding is a key part of the process.
Approach:	They propose an EA Decoding Algorithm via Third-order Tensor Isomorphism (DATTI) they combine two sets of isomorphic equations to enhance the decoding process .
Outcome:	The proposed algorithm can deliver significant performance improvements even on the most advanced methods while the extra required time is less than 3 seconds.

Learning to Sample Replacements for ELECTRA Pre-Training (2021.findings-acl)

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Yaru Hao, Li Dong, Hangbo Bao, Ke Xu, Furu Wei

Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation (2025.findings-acl)

Challenge:	Experimental results show that ELECTRA pretrains a discriminator to detect replaced tokens . despite compelling performance, there is no direct feedback loop from discriminator and generator to generator, making replacements biased to correct tokens.
Approach:	They propose to augment sampling with a hardness prediction mechanism to encourage the discriminator to learn what it has not acquired.
Outcome:	The proposed method improves ELECTRA pre-training on various downstream tasks.

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Zhanglin Wu, Daimeng Wei, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Zongyao Li, Yuanchang Luo, Jinlong Yang, Zhiqiang Rao, Hao Yang

Aligning Large Language Models for Controllable Recommendations (2024.acl-long)

Challenge:	Large language models have advantages over neural machine translation systems, but they suffer from high computational costs and significant latency.
Approach:	They propose a scheduling policy that optimizes translation result while ensuring fast speed and as little LLM usage as possible.
Outcome:	The proposed model achieves optimal translation performance with less LLM usage on multilingual test sets.

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Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li, Mingyang Zhou, Hao Liao, Xing Xie

PRISM-MCTS: Learning from Reasoning Trajectories with Metacognitive Reflection (2026.findings-acl)

Challenge:	Existing literature focuses on integrating domain-specific knowledge into LLMs to enhance accuracy using a fixed task template.
Approach:	They propose a collection of supervised learning tasks augmented with labels derived from a conventional recommender model to improve LLMs’ proficiency in adhering to recommendation-specific instructions.
Outcome:	The proposed approach significantly improves the capability of LLMs to respond to instructions within recommender systems, reducing formatting errors while maintaining a high level of accuracy.

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Siyuan Cheng, Bozhong Tian, Yanchao Hao, Zheng Wei

Visualizing and Understanding the Effectiveness of BERT (D19-1)

Challenge:	Existing reasoning models are limited by inefficiency and computational redundancy . PRISM-MCTS integrates a process reward model with a dynamic shared memory .
Approach:	They propose a reasoning framework that integrates a process reward model with a dynamic shared memory.
Outcome:	PRISM-MCTS integrates a process reward model with a dynamic shared memory . it halves trajectory requirements on GPQA while surpassing MCTS-RAG and Search-o1 .

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Yaru Hao, Li Dong, Furu Wei, Ke Xu

MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification (2025.acl-long)

Challenge:	Language model pre-training, such as BERT, has achieved strong performance in many NLP tasks.
Approach:	They propose to visualize loss landscapes and optimization trajectories of fine-tuning BERT on specific datasets.
Outcome:	The proposed model improves performance and generalization capability across tasks.

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Linzhuang Sun, Hao Liang, Jingxuan Wei, Bihui Yu, Tianpeng Li, Fan Yang, Zenan Zhou, Wentao Zhang

R-CHAR: A Metacognition-Driven Framework for Role-Playing in Large Language Models (2025.emnlp-main)

Challenge:	MM-Verifier and MM Reasoner are a powerful multimodal reasoning model . large language models (LLMs) have demonstrated exceptional performance across tasks spanning myriad domains.
Approach:	They propose a method which combines tree search and verification to generate high-quality chain-of-thought data.
Outcome:	The proposed method outperforms all larger models on the MathCheck, MathVista, and MathVerse benchmarks.

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Haiming Qin, Jiwei Zhang, Wei Zhang, KeZhong Lu, Mingyang Zhou, Hao Liao, Rui Mao

UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation (2023.emnlp-main)

Challenge:	Existing role-playing structures lack cognitive consistency in complex scenarios . Existing models excel in math and coding tasks but lack coherent reasoning .
Approach:	They propose a metacognition-driven framework that enhances role-playing performance . experimental results show performance improvements across varying scenario complexities .
Outcome:	The proposed framework outperforms existing models in social intelligence tasks and shows strength in long-context comprehension and group-level social interactions.

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Daixuan Cheng, Shaohan Huang, Junyu Bi, Yuefeng Zhan, Jianfeng Liu, Yujing Wang, Hao Sun, Furu Wei, Weiwei Deng, Qi Zhang

A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension (D18-1)

Challenge:	Large Language Models (LLMs) have impressive capabilities but need for task-specific prompt engineering can hinder their generalization.
Approach:	They propose a lightweight and versatile retriever that automatically retrieves prompts for a given zero-shot task input.
Outcome:	The proposed model is universally applicable across tasks and models . it mitigates hallucination problem in chatGPT, and it improves even the strongest LLMs.

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Jiahua Liu, Wan Wei, Maosong Sun, Hao Chen, Yantao Du, Dekang Lin

IoTMigrator: LLM-driven Embedded IoT Code Migration across Different OSes for Cloud-device Integration (2025.findings-emnlp)

Challenge:	Existing models of machine reading comprehension (MRC) are based on cloze style questions or crowdworkers given a short passage from well-edited sources.
Approach:	They propose a multi-answer multi-task framework that uses multiple reference answers for multiple questions.
Outcome:	The proposed model increases the ROUGE-L score on the DuReader dataset from 44.18, the previous state-of-the-art, to 51.09 .

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null Yq, Kaijie Gong, Yi Gao, Hao Wang, Wei Dong

Challenge:	Neither outline-based code generation nor common code translation techniques can adequately address this challenge, despite their prevalence in existing systems.
Approach:	They have developed an algorithm that employs a multi-agent pipeline to handle embedded code migration under the TSL paradigm.
Outcome:	The proposed algorithm outperforms the baseline by 50.5% for pass rate and 13.0% for completeness across all tasks in RIOT and Zephyr.

Improving Contextual Query Rewrite for Conversational AI Agents through User-preference Feedback Learning (2023.emnlp-industry)

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Zhongkai Sun, Yingxue Zhou, Jie Hao, Xing Fan, Yanbin Lu, Chengyuan Ma, Wei Shen, Chenlei Guo

Natural Language Interfaces

Challenge:	Contextual query rewriting (CQR) is a crucial component in Conversational AI agents, leveraging contextual information from previous user-agent conversations to improve comprehension of current user intent.
Approach:	They propose a framework to enhance the CQR model's capability in generating user preference-aligned rewrites.
Outcome:	The proposed framework improves the CQR model's ability to generate user preference-aligned rewrites.

Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent (2024.emnlp-main)

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Xiaoyan Yu, Tongxu Luo, Yifan Wei, Fangyu Lei, Yiming Huang, Hao Peng, Liehuang Zhu

Green & Sustainable NLP

Challenge:	Large Language Models (LLMs) have revolutionized open-domain dialogue agents but face challenges in multi-character role-playing (MCRP) scenarios.
Approach:	They propose a framework for efficient multi-character role-playing that employs a dynamic low-rank adapter strategy and distinct LoRA blocks for each character.
Outcome:	Neeko employs a dynamic low-rank adapter (LoRA) strategy, enabling it to adapt seamlessly to diverse characters.

Beyond Surface Features: Advancing Medical Vision-Language Alignment via Dynamic Evidence-Guided Preference Optimization (2026.acl-long)

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Zixuan Huang, Zhihong Zhu, Xiaolong Liu, Yanchao Hao, Manman Zhang, Zheng Wei, Bowen Xing, Xian Wu, Ye Li, Fen Miao, Yefeng Zheng

CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models (2025.naacl-long)

Challenge:	Existing preference-based methods for medical large vision-Language Models face limitations in medical settings . existing methods are limited by overfitting to superficial cues and pseudo convergence of the preference signal.
Approach:	They propose a framework that enables evidence-aware and adaptive preference learning for Med-LVLMs.
Outcome:	The proposed framework improves evidence-aware and adaptive preference learning for Med-LVLMs.

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Ying Nie, Binwei Yan, Tianyu Guo, Hao Liu, Haoyu Wang, Wei He, Binfan Zheng, Weihao Wang, Qiang Li, Weijian Sun, Yunhe Wang, Dacheng Tao

Cross2StrA: Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignment (2023.acl-long)

Challenge:	Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging task like finance, has not been fully explored.
Approach:	They propose a benchmark to assess the financial knowledge of large language models (LLMs) in China.
Outcome:	The proposed benchmark is the most comprehensive evaluation benchmark to date for LLMs in finance.

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Shengqiong Wu, Hao Fei, Wei Ji, Tat-Seng Chua

Generating Visual Spatial Description via Holistic 3D Scene Understanding (2023.acl-long)

Challenge:	Current captioning models are limited to the English language due to the largescale paired image-caption datasets.
Approach:	They propose to integrate the scene graph (SG) structures and the syntactic constituency trees into a captioner to improve captioning relevancy and fluency.
Outcome:	The proposed model improves captioning relevancy and fluency on English-Chinese transfers.

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Yu Zhao, Hao Fei, Wei Ji, Jianguo Wei, Meishan Zhang, Min Zhang, Tat-Seng Chua

OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework (2025.emnlp-demos)

Challenge:	Existing VSD work focuses on skewed spatial understanding of target objects . Existing work merely models the 2D geometrical vision features .
Approach:	They propose to incorporate 3D scene features into visual spatial description tasks by sampling topologically-diverse subgraphs from Go3D-S2G.
Outcome:	The proposed framework outperforms baselines on two VSD datasets and produces more spatially-diversified generation.

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Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, null Xianyu, Yu Cao, Haotian Xu, Yiming Liu

LONG2RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall (2024.findings-emnlp)

Challenge:	Existing RLHF frameworks face inference bottlenecks and complexity barriers restricting their accessibility for newcomers.
Approach:	They propose an open-source RLHF framework that can be used to train large language models.
Outcome:	The proposed framework achieves superior training efficiency with speedups ranging from 1.22 to 1.68 across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation.

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Zehan Qi, Rongwu Xu, Zhijiang Guo, Cunxiang Wang, Hao Zhang, Wei Xu

Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting (2025.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) is a promising approach to address limitations of fixed knowledge in large language models.
Approach:	They propose a benchmark and a metric to assess LLMs' ability to generate long-form responses that exploit retrieved information.
Outcome:	The proposed benchmarks lack a comprehensive evaluation method to assess LLMs' ability to generate long-form responses that effectively exploits retrieved information.

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Hao Feng, Shu Wei, Xiang Fei, Wei Shi, Yingdong Han, Lei Liao, Jinghui Lu, Binghong Wu, Qi Liu, Chunhui Lin, Jingqun Tang, Hao Liu, Can Huang

LAiW: A Chinese Legal Large Language Models Benchmark (2025.coling-main)

Challenge:	Current document image parsing solutions rely on specialized models or generate content autoregressively.
Approach:	They propose a multimodal document image parsing model that integrates specialized models with autogeneous content generation.
Outcome:	The proposed model achieves state-of-the-art performance across diverse page-level and element-level settings while ensuring superior efficiency.

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Yongfu Dai, Duanyu Feng, Jimin Huang, Haochen Jia, Qianqian Xie, Yifang Zhang, Weiguang Han, Wei Tian, Hao Wang

Transferable Direct Prompt Injection via Activation-Guided MCMC Sampling (2025.emnlp-main)

Challenge:	Xie et al., 2023) show that large language models (LLMs) can generate legal text, but lack the legal syllogism . legal experts are cautious about their practical application due to the opaque nature of the LLMs.
Approach:	They propose a Chinese legal LLM benchmark structured around the legal syllogism . they evaluate LLMs across three levels of capability, each reflecting a more complex stage of legal .
Outcome:	The proposed benchmark identifies that LLMs lack the legal syllogism, which hinders trust and understanding from legal experts.

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Minghui Li, Hao Zhang, Yechao Zhang, Wei Wan, Shengshan Hu, Pei Xiaobing, Jing Wang