Papers by Tao Wu

From Text Segmentation to Enhanced Representation Learning: A Novel Approach to Multi-Label Classification for Long Texts (2024.findings-emnlp)

Challenge:	Syntactic trees are widely used in relation extraction (RE) but they are not stable on different text domains and a pre-defined grammar may not fit the target relation schema.
Approach:	They propose to use unsupervised structures to extract relation extraction models . they also conduct detailed analyses on their abilities of adapting new RE domains .
Outcome:	The proposed models obtain competitive (even the best) performance scores on benchmark RE datasets.

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Wang Zhang, Xin Wang, Qian Wang, Tao Deng, Xiaoru Wu

Updating Large Language Models’ Memories with Time Constraints (2024.findings-emnlp)

Challenge:	Existing models rely on pre-trained language models, which have a maximum input sequence length of 512 tokens, and therefore have 'input length limitation'.
Approach:	They propose a text segmentation algorithm which guarantees to produce the optimal segmentation to address the issue of input length limitation caused by PLMs.
Outcome:	The proposed method improves both text and label representations on MLTC datasets, unraveling the intricate correlations between texts and labels.

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Xin Wu, Yuqi Bu, Yi Cai, Tao Wang

Crossing Variational Autoencoders for Answer Retrieval (2020.acl-main)

Challenge:	Large language models (LLMs) can modify their internal memory by incorporating the latest external knowledge, but in practical applications, outdated information may be inputted into LLMs.
Approach:	They propose a two-stage decoupling framework that separates the identification and computation of time constraints into a symbolic system and propose 'selective update' of internal memory based on time constraints.
Outcome:	The proposed framework improves ChatGPT performance by 60% and improves state-of-the-art LLM GPT-4.

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Wenhao Yu, Lingfei Wu, Qingkai Zeng, Shu Tao, Yu Deng, Meng Jiang

Reason-KE++: Aligning the Process, Not Just the Outcome, for Faithful LLM Knowledge Editing (2026.findings-acl)

Challenge:	Existing methods learned semantic representations with dual encoders or dual variational auto-encoders failed to capture the aligned semantics between question and answer.
Approach:	They propose to use two variational auto-encoders to generate questions with aligned answers and generating answers with align questions.
Outcome:	The proposed method outperforms the state-of-the-art answer retrieval method on SQuAD.

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Yuchen Wu, Liang Ding, Li Shen, Dacheng Tao

Privacy-Preserving News Recommendation Model Learning (2020.findings-emnlp)

Challenge:	Current methods for modifying parameters to integrate new knowledge are not accurate enough.
Approach:	They propose an SFT+RL framework that instills process-level faithfulness by a stage-aware Reward mechanism and a Stage-assisted Reward Mechanism.
Outcome:	The proposed framework instills process-level faithfulness while boosting final accuracy.

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Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie

Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection (2024.findings-emnlp)

Challenge:	Existing news recommendation methods rely on centralized storage of user behavior data for model training, which may lead to privacy concerns and risks due to the privacy-sensitive nature of user behaviors.
Approach:	They propose a privacy-preserving method where user behavior data is locally stored on user devices to train accurate news recommendation models.
Outcome:	The proposed method can train accurate news recommendation models without centralized storage of user behavior data.

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Tianxiang Chen, Zhentao Tan, Tao Gong, Yue Wu, Qi Chu, Bin Liu, Jieping Ye, Nenghai Yu

Challenge:	Existing methods to augment pre-trained large language models require extensive computational efforts and massive data volumes, challenging the widespread accessibility of LLM research.
Approach:	They propose a post-pretraining strategy of selectively enhancing shallow layers while pruning less effective deep ones to augment pretrained large language models.
Outcome:	The proposed approach improves performance on the corpus of code & math and a legal corpus and is widely applicable.

From Charts to Code: A Hierarchical Benchmark for Multimodal Models (2026.acl-long)

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Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li, Alex Jinpeng Wang

Improving Neural Machine Translation by Bidirectional Training (2021.emnlp-main)

Challenge:	Chart2Code is a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models.
Approach:	They introduce Chart2Code, a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models.
Outcome:	The proposed benchmark is the first to scale task complexity while capturing diverse scenarios.

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Liang Ding, Di Wu, Dacheng Tao

Task-oriented Domain-specific Meta-Embedding for Text Classification (2020.emnlp-main)

Challenge:	Experimental results show that bidirectional training pushes the SOTA neural machine translation performance significantly higher.
Approach:	They propose a bidirectional training strategy that updates model parameters at the early stage and tunes it normally.
Outcome:	The proposed approach pushes the SOTA neural machine translation performance significantly higher on 15 translation tasks on 8 language pairs.

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Xin Wu, Yi Cai, Yang Kai, Tao Wang, Qing Li

Length Generalization of Causal Transformers without Position Encoding (2024.findings-acl)

Challenge:	Existing methods neglect domain-specific knowledge and use the same word embedding for each word in all domain-specified datasets.
Approach:	They propose a method to incorporate domain-specific and task-oriented information into meta-embeddings by combining pre-trained word embeddings.
Outcome:	The proposed method performs well on four text classification datasets and shows that it is compatible with existing methods.

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Jie Wang, Tao Ji, Yuanbin Wu, Hang Yan, Tao Gui, Qi Zhang, Xuanjing Huang, Xiaoling Wang

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

Challenge:	Besides Transformers without position encodings, the success of NoPE provides a new way to overcome the challenge of generalizing to longer sentences.
Approach:	They propose a parameter-efficient tuning for searching attention heads’ best temperature hyper-parameters, which substantially expands NoPE’s context size.
Outcome:	The proposed tuning significantly expands NoPE's context size, allowing it to generalize to longer sentences with state-of-the-art generalization algorithms.

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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

Rehearsal-free Continual Language Learning via Efficient Parameter Isolation (2023.acl-long)

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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Zhicheng Wang, Yufang Liu, Tao Ji, Xiaoling Wang, Yuanbin Wu, Congcong Jiang, Ye Chao, Zhencong Han, Ling Wang, Xu Shao, Wenqiu Zeng

Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards (2026.acl-long)

Challenge:	Existing methods for learning continual tasks do not cache history data, which makes the problem more challenging.
Approach:	They propose a method that allocates a small portion of private parameters and learns them with a shared pre-trained model.
Outcome:	The proposed method is comparable to existing methods and comparable to those using historical data.

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Ming Li, Pei Chen, Zhenhao Zhang, Tao Yang, Xinyang Zhang, Han Li, Tianyu Cao, Ming Zeng, Zhuofeng Wu, Meng Jiang, Huasheng Li, Lihong Li, Bing Yin

Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)

Challenge:	Large Language Models exhibit strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC) when instructions are revealed progressively in multi-turn settings, models get "Lost in Conversation"
Approach:	They propose a framework that encourages models to generate correct answers and judge solvability in multi-turn conversations.
Outcome:	The proposed framework improves models' ability to balance problem-solving with abstention . it reduces premature answering behaviors that cause lost-in-conversation (LiC)

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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu

CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction (2021.acl-long)

Challenge:	Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature.
Approach:	They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management.
Outcome:	The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench.

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Tao Chen, Haizhou Shi, Siliang Tang, Zhigang Chen, Fei Wu, Yueting Zhuang

Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving (2021.findings-emnlp)

Challenge:	Existing methods to reduce noise from DS generated training data are not effective for distantly supervised relation extraction (DSRE)
Approach:	They propose a multi-instance learning framework to reduce DS noise by dividing training instances into several bags and using them as new data units.
Outcome:	The proposed framework improves on NYT10, GDS and KBP with significant improvements over existing methods.

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Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie

UniLG: A Unified Structure-aware Framework for Lyrics Generation (2023.acl-long)

Challenge:	Existing news recommendation methods rely on user behavior data to model user interests and user interests.
Approach:	They propose a unified news recommendation framework that uses user data locally stored in user clients to train models and serve users in a privacy-preserving way.
Outcome:	The proposed framework outperforms baseline methods and effectively protects user privacy.

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Tao Qian, Fan Lou, Jiatong Shi, Yuning Wu, Shuai Guo, Xiang Yin, Qin Jin

One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues (P19-1)

Challenge:	Existing works ignore musical attributes hidden behind lyrics and structure of lyrics . existing works ignore structure of generated lyrics and do not consider structure of songs .
Approach:	They propose a framework for conditional lyrics generation that considers structure and relationship between lyrics and music.
Outcome:	The proposed framework improves the structure modeling and unifies different conditions for different types of lyrics generation.

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Chongyang Tao, Wei Wu, Can Xu, Wenpeng Hu, Dongyan Zhao, Rui Yan

Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts (2025.findings-emnlp)

Challenge:	Currently, retrieval-based dialogues are performed in shallow ways . a recent study investigated the problem of context-response matching in open-domain .
Approach:	They propose a model that lets utterance-response interaction go deep by stacking interaction blocks.
Outcome:	The proposed model outperforms state-of-the-art methods on three benchmark data sets.

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Xuyang Wu, Yuan Wang, Hsin-Tai Wu, Zhiqiang Tao, Yi Fang

Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction (2026.acl-long)

Challenge:	Large vision-language models have demonstrated strong capabilities in open-world visual understanding, but it is not clear how they address demographic biases in real life.
Approach:	They propose a method to assess visual fairness in LVLMs by question-answering/classification tasks.
Outcome:	The proposed approach improves transparency and offers a scalable solution for fairness mitigation.

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Tao Wu, Jingyuan Chen, Wang Lin, Jian Zhan, Mengze Li, Fangzhou Jin, Min Zhang, Kun Kuang, Fei Wu

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	Current distractor generation methods produce shared distractors for all students, ignoring individual variations in reasoning, which limits their diagnostic effectiveness.
Approach:	They propose a method which tailors distractors to each student’s specific cognitive flaws, inferred from their past question-answering (QA) history.
Outcome:	The proposed framework outperforms existing methods in generating plausible distractors and adapts to group-level settings.

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Two-Stage Regularization-Based Structured Pruning for LLMs (2026.acl-long)

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

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Mingkuan Feng, Jinyang Wu, Siyuan Liu, Shuai Zhang, Hongjian Fang, Ruihan Jin, Feihu Che, Pengpeng Shao, Zhengqi Wen, Jianhua Tao

Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network (D19-1)

Challenge:	Structural pruning is a promising solution for large language models . prior structured pruning methods remove unimportant parameters based on certain metrics .
Approach:	They propose a structural pruning method that iteratively learns the weights of transformer layers by adding their l1-norm to the loss function.
Outcome:	The proposed pruning method outperforms strong layer-wise pruning methods without requiring retraining.

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Chuhan Wu, Fangzhao Wu, Tao Qi, Suyu Ge, Yongfeng Huang, Xing Xie

TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities (2024.emnlp-main)

Challenge:	Existing methods to learn user and item representations from review texts do not take into account the user-user and item-item relatedness of the user.
Approach:	They propose to use review content and user-item graphs to integrate them as different views.
Outcome:	The proposed approach can learn user and item representations from review content and user-item graphs.

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Ming Zhang, Caishuang Huang, Yilong Wu, Shichun Liu, Huiyuan Zheng, Yurui Dong, Yujiong Shen, Shihan Dou, Jun Zhao, Junjie Ye, Qi Zhang, Tao Gui, Xuanjing Huang

Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains (2025.findings-emnlp)

Challenge:	Current datasets cater to user-led systems and are limited to predefined specific scenarios and slots.
Approach:	They propose to use a Chinese dialogue dataset to train a model that authentically simulates human-computer dialogues in 30 popular life service scenarios.
Outcome:	The proposed model achieves a joint accuracy of 75.09% in out-of-domain evaluations . it also achieves notable abilities in slot filling and questioning .

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Yang Wu, Raha Moraffah, Rujing Yao, Jinhong Yu, Zhimin Tao, Xiaozhong Liu

A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition (2023.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated an impressive level of general knowledge, but often struggle in highly specialized domains due to the lack of expert knowledge.
Approach:	They propose a framework to actively engage domain experts within a fixed budget to enhance domain-specific LLMs.
Outcome:	The proposed framework improves LLMs in highly specialized domains while adhering to budget constraints.

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Limao Xiong, Jie Zhou, Qunxi Zhu, Xiao Wang, Yuanbin Wu, Qi Zhang, Tao Gui, Xuanjing Huang, Jin Ma, Ying Shan

SINCon: Mitigate LLM-Generated Malicious Message Injection Attack for Rumor Detection (2025.acl-long)

Challenge:	Existing models for named entity recognition (NER) are based on large-scale labeled datasets, which always obtain using crowdsourcing.
Approach:	They propose a CONfidence-based partial Label Learning method to integrate prior and posterior confidences for crowd-annotated named entity recognition models.
Outcome:	The proposed model improves on real-world and synthetic datasets compared with baselines.

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Mingqing Zhang, Qiang Liu, Xiang Tao, Shu Wu, Liang Wang

Towards General Agentic Intelligence via Environment Scaling (2026.findings-acl)

Challenge:	Existing methods define important nodes as important and target them for attacks if the model treats nodes’ predictive influence more uniformly . Existing approaches target high predictive influence nodes but are vulnerable to malicious message injection attacks.
Approach:	They propose a defense mechanism that encourages the model to learn graph representations where nodes with varying importance have a more uniform influence on predictions.
Outcome:	Extensive experiments on the Twitter and Weibo datasets show that similarizing the predictive Influence of nodes with Contrastive Learning significantly enhances resistance against LLM-driven message injection attacks.

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Runnan Fang, Shihao Cai, Baixuan Li, Jialong Wu, Guangyu Li, Wenbiao Yin, Xinyu Wang, Xiaobin Wang, Liangcai Su, Zhen Zhang, Shibin Wu, Zhengwei Tao, Yong Jiang, Pengjun Xie, Ningyu Zhang, Fei Huang, Wentao Zhang, Jingren Zhou

BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving (2025.acl-long)

Challenge:	Diverse real-world APIs require precise, robust function-calling intelligence, which needs agents to develop these capabilities through interaction in varied environments.
Approach:	They propose a framework that scales up environments to enable agentic intelligence . they use a two-phase agent fine-tuning strategy to first endow agents with basic agentic capabilities, then specializing them for domain-specific contexts.
Outcome:	Experiments on -bench, -Bench, and ACEBench show that the model significantly enhances the models’ function-calling capability.

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Teng Wang, Wing Yin Yu, Zhenqi He, Zehua Liu, HaileiGong HaileiGong, Han Wu, Xiongwei Han, Wei Shi, Ruifeng She, Fangzhou Zhu, Tao Zhong

DeepGuard: Secure Code Generation via Multi-Layer Semantic Aggregation (2026.acl-long)

Challenge:	Existing datasets in operations research domain lack detailed annotations of the modeling process, focusing only on objective values.
Approach:	They propose an annotation-based tree-of-thought tree-based reasoning algorithm that integrates reinforcement learning into a tree- of-though.
Outcome:	The proposed algorithm outperforms state-of-the-art methods on StructuredOR, NL4OPT, and MAMO-ComplexLP datasets.

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Li Huang, Zhongxin Liu, Yifan Wu, Tao Yin, Dong li, Jichao Bi, Nankun Mu, Hongyu Zhang, Meng Yan

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) can replicate insecure patterns from training data.
Approach:	They propose a framework that leverages distributed security-relevant cues by aggregating representations from multiple upper layers via an attention-based module.
Outcome:	Experiments show that the framework improves the secure-and-correct generation rate by 11.9% over baselines.

Efficient Sequence Learning with Group Recurrent Networks (N18-1)

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Fei Gao, Lijun Wu, Li Zhao, Tao Qin, Xueqi Cheng, Tie-Yan Liu

Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models (2026.acl-long)

Challenge:	Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation and speech recognition.
Approach:	They propose an efficient architecture to improve the efficiency of such RNN model training by adopting the group strategy for recurrent layers while exploiting the representation rearrangement strategy between layers as well as time steps.
Outcome:	The proposed architecture achieves comparable or better accuracy compared with baselines, with a much smaller number of parameters and at a lower computational cost.

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Runxuan Liu, Xianhao Ou, Xinyan Ma, Jiyuan Wang, Jiafeng Liang, Jiaqi Li, Tao He, Zheng Chu, Rongchuan Mu, Zekun Wang, Baoxin Wang, Dayong Wu, Ming Liu, Shijin Wang, Guoping Hu, Bing Qin

PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)

Challenge:	Existing methods for long chain-of-thought (LCoT) are coarse-grained, reward hacking, and poor generalization.
Approach:	They propose a Long Chain-of-Thought (LCoT) model that integrates reinforcement learning with verifiable rewards with a process-aware verification approach.
Outcome:	The proposed model improves reasoning and code generation tasks while reducing the cost of training and performance bottlenecks.

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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang

Does Reasoning Introduce Bias? A Study of Social Bias Evaluation and Mitigation in LLM Reasoning (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks.
Approach:	They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes.
Outcome:	The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset.

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Xuyang Wu, Jinming Nian, Ting-Ruen Wei, Zhiqiang Tao, Hsin-Tai Wu, Yi Fang

Word Reordering for Zero-shot Cross-lingual Structured Prediction (2021.emnlp-main)

Challenge:	Recent advances in large language models have enabled automatic generation of chain-of-thought reasoning . however, when reasoning steps reflect social stereotypes, they can reinforce harmful associations and lead to misleading conclusions.
Approach:	They propose a method that detects how model predictions change across incremental reasoning steps.
Outcome:	The proposed method outperforms a stereotype-free baseline and improves accuracy.

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Tao Ji, Yong Jiang, Tao Wang, Zhongqiang Huang, Fei Huang, Yuanbin Wu, Xiaoling Wang

Low-Resource NLP

Challenge:	Current sentence encoders are word order sensitive, resulting in poor performance . Adapting word order from one language to another is key in cross-lingual structured prediction.
Approach:	They propose a new module to organize words following the source language order . they build structured prediction models with bag-of-words inputs and introduce a module to do this .
Outcome:	The proposed model significantly improves target language performance for languages that are distant from the source language.

Neural News Recommendation with Heterogeneous User Behavior (D19-1)

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Chuhan Wu, Fangzhao Wu, Mingxiao An, Tao Qi, Jianqiang Huang, Yongfeng Huang, Xing Xie

DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization (2022.acl-long)

Challenge:	Existing news recommendation methods rely on news click history to model user interest, but data sparsity is a problem . other kinds of user behaviors such as webpage browsing and search queries can provide useful clues of users’ news reading interest.
Approach:	They propose to exploit heterogeneous user behaviors to learn news representations from their titles via CNN networks and apply attention networks to select important words.
Outcome:	The proposed approach exploits heterogeneous user behaviors on a real-world dataset.

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Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed Awadallah, Dragomir Radev

Depth Growing for Neural Machine Translation (P19-1)

Challenge:	Existing models struggle with summarizing long text due to high memory complexity of the full self-attention.
Approach:	They propose a dynamic latent extraction approach for abstractive long-input summarization that treats extracted text snippets as latent variables and allows dynamic attention weights during decoding.
Outcome:	The proposed method outperforms existing methods on GovReport, QMSum, and arXiv while yielding strong results on arX.

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Lijun Wu, Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, Jianhuang Lai, Tie-Yan Liu

Machine Translation

Challenge:	Neural machine translation models with tens and even more than a hundred blocks have shown effectiveness in image recognition.
Approach:	They propose a two-stage approach with three specially designed components to construct deeper NMT models.
Outcome:	The proposed approach improves on WMT14 EnglishGerman and EnglishFrench translation tasks.

AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang

Multi-Programming Language Sandbox for LLMs (2025.acl-demo)

Challenge:	Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents.
Approach:	They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
Outcome:	The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.

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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang

Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation (2025.findings-emnlp)

Challenge:	MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs).
Approach:	They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models.
Outcome:	The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs).

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Qingsong Wang, Tao Wu, Wang Lin, Yueying Feng, Gongsheng Yuan, Chang Yao, Jingyuan Chen

Information Retrieval

Challenge:	Large Language Models struggle to adapt content to users with differing cognitive capacities, leading to cognitive misalignment.
Approach:	They propose a cognitive-level alignment framework that aligns both knowledge complexity and presentation style with user cognition.
Outcome:	The proposed framework aligns knowledge complexity and presentation style with user cognition.

NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better (2022.acl-short)

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SPARK: Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning (2026.acl-long)

Challenge:	Existing methods for finetuning pretrained language models (PLMs) have risks in overfitting the pretraining tasks and data, which may lead to suboptimal performance.
Approach:	They propose a method which adds noise to parameters of PLMs before fine-tuning.
Outcome:	The proposed method can be used on GLUE English and XTREME multilingual benchmarks.

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Jinyang Wu, Shuo Yang, Yuhao Shen, Shuai Zhang, Zhengqi Wen, Jianhua Tao

Challenge:	Existing methods for training large language models waste computation budget on trivial steps while failing to guarantee sample quality.
Approach:	They propose a framework that selectively branches at critical decision states for resource-efficient exploration.
Outcome:	The proposed framework activates adaptive branching exploration at critical decision states to probe promising trajectories, thereby achieving precise resource allocation that prioritizes sampling quality over blind coverage.

Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems (P19-1)

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Jiazhan Feng, Chongyang Tao, Wei Wu, Yansong Feng, Dongyan Zhao, Rui Yan

PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings (2022.emnlp-main)

Challenge:	Existing methods for learning a robust matching model from noisy training data are retrieval-based or generation-based.
Approach:	They propose a general co-teaching framework that learns matching models from noisy training data.
Outcome:	The proposed learning framework can improve existing models on two public data sets.

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Qiyu Wu, Chongyang Tao, Tao Shen, Can Xu, Xiubo Geng, Daxin Jiang

The Role of Visual Modality in Multimodal Mathematical Reasoning: Challenges and Insights (2025.acl-long)

Challenge:	Existing approaches to learning sentence embeddings in unsupervised manner depend on mono-augmenting . existing approaches depend on augmenting biases and thus corrupt the quality of sentence embeds.
Approach:	They propose a method to augment a sentence with a semantically-close positive instance to construct contrastive pairs in unsupervised manner.
Outcome:	The proposed method improves performance on STS benchmarks and compares with existing methods.

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Yufang Liu, Yao Du, Tao Ji, Jianing Wang, Yang Liu, Yuanbin Wu, Aimin Zhou, Mengdi Zhang, Xunliang Cai

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)

Challenge:	Existing models that leverage visual information do not improve math reasoning performance . authors suggest that visual information is important for multimodal reasoning .
Approach:	They propose a dataset to require image reliance for problem-solving and challenge models with similar, yet distinct, images that change the correct answer.
Outcome:	The proposed model performance is unaffected by changes to or removal of images in the dataset.

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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan

RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents (2026.acl-long)

Challenge:	Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures.
Approach:	They propose a toolkit that supports pre-training models of different modalities.
Outcome:	The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks.

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Jize Wang, Han Wu, Zhiyuan You, Yiming Song, Yijun Wang, Zifei Shan, Yining Li, Songyang Zhang, Xinyi Le, Cailian Chen, Xinping Guan, Dacheng Tao

Neural Response Generation with Meta-words (P19-1)

Challenge:	Existing methods for mixing-of-agents (MoA) lack model selection criteria and struggle with large model pools.
Approach:	They propose a mixture-of-agents framework with dynamic routing that uses a lightweight scorer to perform initial screening and refines the model scores through self- and cross-assessment.
Outcome:	The proposed framework outperforms existing methods for large model pools and tasks . it reduces cost by 89.8% and latency by 63.6% in the large-scale model pool.

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Can Xu, Wei Wu, Chongyang Tao, Huang Hu, Matt Schuerman, Ying Wang

MolXPT: Wrapping Molecules with Text for Generative Pre-training (2023.acl-short)

Challenge:	Experimental results show that meta-words can be used to generate open domain dialogues . human-machine conversation is a fundamental problem in NLP .
Approach:	They propose a goal-tracking memory network that formalizes meta-word expression as a target in response generation and manages the generation process with a state memory panel and a controller.
Outcome:	The proposed model outperforms state-of-the-art generation models in response relevance, response diversity, and accuracy.

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Zequn Liu, Wei Zhang, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Ming Zhang, Tie-Yan Liu

Low-code LLM: Graphical User Interface over Large Language Models (2024.naacl-demo)

Challenge:	Experimental results show that Generative pre-trained Transformers (GPT) have great success in natural language processing.
Approach:	They propose a unified language model of text and molecules pre-trained on SMILES wrapped by text.
Outcome:	The proposed model outperforms strong baselines of molecular property prediction on MoleculeNet and performs comparably to the best model in text-molecule translation while using less than half of its parameters.

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Yuzhe Cai, Shaoguang Mao, Wenshan Wu, Zehua Wang, Yaobo Liang, Tao Ge, Chenfei Wu, WangYou WangYou, Ting Song, Yan Xia, Nan Duan, Furu Wei

Less Is Better: Recovering Intended-Feature Subspace to Robustify NLU Models (2022.coling-1)

Challenge:	Low-code LLM is a visual programming interface that allows users to incorporate their ideas into the process without writing trivial prompts.
Approach:	They propose a human-LLM interaction framework that incorporates low-code visual programming interactions to achieve more controllable and stable responses.
Outcome:	The proposed framework enables users to incorporate ideas into the process without writing trivial prompts.

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Ting Wu, Tao Gui

A Study of Reinforcement Learning for Neural Machine Translation (D18-1)

Challenge:	Existing approaches to debiase datasets rely on knowledge of bias attributes . current approaches focus on how to leverage kinds of supervision effectively .
Approach:	They propose to extend the supervision on bias by extending it into feature space.
Outcome:	Empirical results show that a low-dimensional subspace with intended features can represent biased datasets.

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Lijun Wu, Fei Tian, Tao Qin, Jianhuang Lai, Tie-Yan Liu

Enhancing Contrastive Learning with Noise-Guided Attack: Towards Continual Relation Extraction in the Wild (2024.acl-long)

Challenge:	Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation systems.
Approach:	They propose to leverage reinforcement learning to boost the performance of NMT systems trained with monolingual data.
Outcome:	The proposed method achieves competitive results on translation tasks in English-German, Chinese-English and English-English systems.

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Ting Wu, Jingyi Liu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang

Representation Learning with Ordered Relation Paths for Knowledge Graph Completion (D19-1)

Challenge:	Existing methods for continual relation extraction (CRE) excel in preserving old knowledge but falter when confronted with contaminated data streams.
Approach:	They propose a noise-resistant contrastive framework for continual relation extraction (CRE) that preserves old knowledge while learning incremental corrupted relations.
Outcome:	The proposed framework outperforms state-of-the-art methods on various benchmarks with increasing noise rates.

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Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yang Song, Tao Zhang

MimicLM: Zero-Shot Voice Imitation through Autoregressive Modeling of Pseudo-Parallel Speech Corpora (2026.findings-acl)

Challenge:	Existing knowledge graphs are incomplete and lack the order of relations in paths.
Approach:	They propose a method which takes relation paths into account but ignores order of relations in paths which is important for reasoning.
Outcome:	The proposed method performs better than state-of-the-art methods on two benchmark datasets.

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Tao Feng, Yuxiang Wang, Yuancheng Wang, Xueyao Zhang, Dekun Chen, Chaoren Wang, Xun Guan, Zhizheng Wu

UNComp: Can Matrix Entropy Uncover Sparsity? — A Compressor Design from an Uncertainty-Aware Perspective (2025.emnlp-main)

Challenge:	Existing approaches to voice imitation use complex model design and a quality ceiling when synthetic speech is used as training sources.
Approach:	They propose a model that uses synthetic speech as training sources while retaining real recordings as targets.
Outcome:	The proposed model outperforms existing methods in naturalness while maintaining competitive similarity scores across speaker identity, accent, and emotion dimensions.

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Jing Xiong, Jianghan Shen, Fanghua Ye, Chaofan Tao, Zhongwei Wan, Jianqiao Lu, Xun Wu, Chuanyang Zheng, Zhijiang Guo, Min Yang, Lingpeng Kong, Ngai Wong

Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates (2025.emnlp-main)

Challenge:	Deploying large language models (LLMs) for long-context inference remains challenging due to their substantial memory and computational demands.
Approach:	They propose an uncertainty-aware framework that leverages truncated matrix entropy to identify areas of low information content.
Outcome:	The proposed framework reduces the KV cache size to 4.74% of the original and achieves a 6% speedup.

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Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang

MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models (2026.findings-acl)

Challenge:	Large language models (LLMs) have strong reasoning and tool-use capabilities, yet fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent.
Approach:	They propose a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function calls.
Outcome:	The proposed framework reduces tool-use errors and improves interpretability and transparency of tool-using agents.

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Junjie Ye, Caishuang Huang, Zhuohan Chen, Wenjie Fu, Chenyuan Yang, Leyi Yang, Yilong Wu, Peng Wang, Meng Zhou, Xiaolong Yang, Tao Gui, Qi Zhang, Zhongchao Shi, Jianping Fan, Xuanjing Huang

Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks (2022.coling-1)

Challenge:	Existing research has focused on constraint categories, offering little guidance for improving instruction following abilities.
Approach:	They propose a multi-dimensional constraint framework that allows for instruction following . they construct 9,106 code-verifiable samples and evaluate 18 LLMs .
Outcome:	The proposed framework improves instruction following performance without compromising general performance.

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Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu

Typology Guided Multilingual Position Representations: Case on Dependency Parsing (2023.findings-acl)

Challenge:	Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications.
Approach:	They propose a framework that re-uses existing parameter-efficient methods with a unified classifier.
Outcome:	The proposed framework improves the efficiency of existing parameter-efficient methods with a unified classifier.

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Tao Ji, Yuanbin Wu, Xiaoling Wang

Syntactic Text Processing

Challenge:	Recent multilingual models benefit from strong unified semantic representation models, but conflicting linguistic regularities may break the effectiveness of word position features in multilingual learning.
Approach:	They propose to combine prior knowledge from typology features and existing position vectors to create a position generation network which combines prior knowledge of a language's position space and typological characterization.
Outcome:	The proposed model can achieve the best multilingual parsing results by combining prior knowledge from typology features and existing position vectors.

LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models (2026.acl-long)

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Ming Zhang, Yujiong Shen, Jingyi Deng, Yuhui Wang, Huayu Sha, Kexin Tan, Qiyuan Peng, Yue Zhang, Junzhe Wang, Shichun Liu, Yueyuan Huang, Jingqi Tong, Changhao Jiang, Yilong Wu, Zhihao Zhang, Mingqi Wu, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang

Reinforcement Learning on Pre-Training Data (2026.acl-long)

Challenge:	Existing evaluation of Large Language Models on static benchmarks is vulnerable to data contamination and leaderboard overfitting.
Approach:	LLMEval-Fair framework provides a framework for dynamic evaluation of Large Language Models . evaluators use a proprietary bank of 220k graduate-level questions to analyze model data .
Outcome:	LLMEval-Fair provides robust and credible evaluation framework for Large Language Models . it provides a strong empirical validation for the dynamic evaluation paradigm .

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems (D19-1)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Jia Li, Chongyang Tao, Wei Wu, Yansong Feng, Dongyan Zhao, Rui Yan

CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation (2022.acl-long)

Challenge:	Existing studies focus on constructing a matching model with sophisticated neural architectures, but do little to how to effectively learn such architectures from data.
Approach:	They propose to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems by using four sampling strategies.
Outcome:	The proposed learning method improves the performance of matching models on two benchmarks with three matching models.

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Zichu Fei, Qi Zhang, Tao Gui, Di Liang, Sirui Wang, Wei Wu, Xuanjing Huang

Should I Believe in What Medical AI Says? A Chinese Benchmark for Medication Based on Knowledge and Reasoning (2025.acl-short)

Challenge:	Current models can not ensure the complexity of generated questions, so they may generate shallow questions that can be answered without multi-hop reasoning.
Approach:	They propose a controlled framework to generate multi-hop questions that contain key entities in multi- hop reasoning chains and a novel Transformer-based decoder to guarantee that key entities appear in the questions.
Outcome:	The proposed model outperforms the state-of-the-art model 25% on HotpotQA.

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Yue Wu, Yangmin Huang, Qianyun Du, Lixian Lai, Zhiyang He, Jiaxue Hu, Xiaodong Tao

Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems (2025.coling-main)

Challenge:	Large language models (LLMs) generate hallucinations when handling unfamiliar information.
Approach:	They propose a Chinese benchmark to evaluate large language models' knowledge and reasoning capabilities in medication tasks.
Outcome:	The proposed benchmark evaluates models in indication, dosage and administration, contraindicated population, mechanisms of action, drug recommendation, and drug interaction across six datasets.

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Xuyang Wu, Shuowei Li, Hsin-Tai Wu, Zhiqiang Tao, Yi Fang

Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs (2026.findings-acl)

Challenge:	Retrieval-Augmented Generation (RAG) models address fairness concerns with respect to sensitive attributes such as gender, geographic location, and other demographic factors.
Approach:	They propose a framework to evaluate fairness in RAG using scenario-based questions and analyzing disparities across demographic attributes.
Outcome:	The proposed framework analyzes disparities across demographic attributes and identifies fairness issues in retrieval and generation stages.

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Yubo Gao, Haotian Wu, Hong Chen, Junquan Huang, Yibo Yan, Jungang Li, Zihao Dongfang, Sicheng Tao, PS Tan, Jie Zhang, Xuming Hu

PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection (2023.findings-emnlp)

Challenge:	Existing efficiency methods for Chain-of-Thought (CoT) generate excessively long rationales without commensurate accuracy gains.
Approach:	They propose a training framework that operationalizes this principle through coarse-to-fine budgeting.
Outcome:	Experiments on GSM8K and MATH500 show that HAB surpasses standard CoT in accuracy and reduces token usage, achieving stronger performance-efficiency trade-off than baselines.

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Tao Yang, Tianyuan Shi, Fanqi Wan, Xiaojun Quan, Qifan Wang, Bingzhe Wu, Jiaxiang Wu

Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)

Challenge:	Recent advances in large language models (LLMs) have demonstrated remarkable zero-shot performance across various NLP tasks.
Approach:	They propose a method which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner and prompts an LLM to rate individual items at each turn.
Outcome:	The proposed method improves the performance and robustness of the standard GPT-3.5 personality detection task on two benchmark datasets.

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Xueliang Zhao, Wei Wu, Can Xu, Chongyang Tao, Dongyan Zhao, Rui Yan

LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging (2025.findings-emnlp)

Challenge:	Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment.
Approach:	They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues.
Outcome:	The proposed model outperforms state-of-the-art methods in evaluation and human judgment.

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Zehua Liu, Han Wu, Yuxuan Yao, Xiaojin Fu, Ruifeng She, Xiongwei Han, Tao Zhong, Mingxuan Yuan

XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser (2025.coling-main)

Challenge:	a framework for model merging is proposed without additional training . task vectors from fine-tuned models exhibit a limited number of dominant singular values .
Approach:	They propose a framework for model merging based on low-rank estimation of task vectors without access to the base model.
Outcome:	The proposed framework improves models without additional training without additional inputs.

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Xianfu Cheng, Hang Zhang, Jian Yang, Xiang Li, Weixiao Zhou, Fei Liu, Kui Wu, Xiangyuan Guan, Tao Sun, Xianjie Wu, Tongliang Li, Zhoujun Li

EvoR: Evolving Retrieval for Code Generation (2024.findings-emnlp)

Challenge:	Document AI parsing semi-structured image form is a key information extraction task.
Approach:	They propose a multimodal and multilingual semi-structured FORM PARSER which integrates SER and relation extraction into a unified framework.
Outcome:	The proposed framework achieves up to 1.79% improvement on RE tasks in multilingual and zero-shot settings.

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Hongjin Su, Shuyang Jiang, Yuhang Lai, Haoyuan Wu, Boao Shi, Che Liu, Qian Liu, Tao Yu

Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration (2024.naacl-long)

Challenge:	Existing pipelines for retrieval-augmented code generation (RACG) use static knowledge bases with a single source, limiting adaptation capabilities of Large Language Models (LLMs) Extensive experiments demonstrate that EVOR achieves two to four times of execution accuracy compared to other methods such as Reflexion.
Approach:	They propose a retrieval-augmented code generation pipeline that employs the synchronous evolution of queries and diverse knowledge bases.
Outcome:	The proposed pipeline achieves two to four times of execution accuracy compared to other methods.

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Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji

RadialRouter: Structured Representation for Efficient and Robust Large Language Models Routing (2025.findings-emnlp)

Challenge:	Existing work on LLMs that only enhance reasoning abilities, but which lack factual hallucination and slow-thinking capabilities, argues that SPP is a cognitive synergist.
Approach:	They propose a Solo Performance Prompting (SPP) that transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas.
Outcome:	The proposed model reduces factual hallucination and maintains strong reasoning abilities on three challenging tasks .

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Ruihan Jin, Pengpeng Shao, Zhengqi Wen, Jinyang Wu, Mingkuan Feng, Shuai Zhang, Jianhua Tao

Modeling the Q-Diversity in a Min-max Play Game for Robust Optimization (2023.findings-acl)

Challenge:	Current routing methods are limited in exploring the connection between query and LLM characteristics.
Approach:	They propose a framework for LLM routing that uses a transformer-based backbone and a radial structure to articulate the query-LLMs relationship.
Outcome:	The proposed framework outperforms existing routing methods by 9.2% and 5.8% on RouterBench.

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Ting Wu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang

Attentive Pooling with Learnable Norms for Text Representation (2020.acl-main)

Challenge:	Existing methods for minimizing the worst-case loss of annotated groups are lacking in practice due to expensive annotations and privacy issues.
Approach:	They propose a distributionally robust optimization framework that relaxes group identification into direct parameterization by using an interactive training mode.
Outcome:	The proposed method outperforms state-of-the-art methods on synthetic and real-world text classification tasks.

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Chuhan Wu, Fangzhao Wu, Tao Qi, Xiaohui Cui, Yongfeng Huang

HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation (2021.acl-long)

Challenge:	Existing pooling methods that use fixed pooling norms may not be optimal for learning text representations in different tasks.
Approach:	They propose to learn pooling norms in an end-to-end manner to automatically find the optimal ones for text representation in different tasks.
Outcome:	The proposed approach improves on four benchmark datasets on a neural NLP model.

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Tao Qi, Fangzhao Wu, Chuhan Wu, Peiru Yang, Yang Yu, Xing Xie, Yongfeng Huang

PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack (2022.coling-1)

Challenge:	Existing news recommendation methods learn a single user embedding for each user from their previous behaviors to represent their overall interest. Existing methods only learn 'one' embeddable representation vectors to model user interest.
Approach:	They propose a news recommendation method with hierarchical user interest modeling that captures user interest in news rather than a single user embedding.
Outcome:	The proposed method can better capture multi-grained user interest in news.

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Rui Zheng, Rong Bao, Qin Liu, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu

CReSE: Benchmark Data and Automatic Evaluation Framework for Recommending Eligibility Criteria from Clinical Trial Information (2024.findings-eacl)

Challenge:	Recent studies have shown that adversarial examples can be easily fooled by adversarially perturbed examples.
Approach:	They propose a pluggable defense module PlugAT to provide robust predictions by adding a few trainable parameters to the model inputs while keeping the original model frozen.
Outcome:	The proposed model improves robustness over several strong baselines whilst training only 9.1% parameters.

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Siun Kim, Jung-Hyun Won, David Lee, Renqian Luo, Lijun Wu, Tao Qin, Howard Lee

CoEvo: Coevolution of LLM and Retrieval Model for Domain-Specific Information Retrieval (2025.emnlp-main)

Challenge:	Eligibility criteria (EC) are defined as a set of conditions an individual must meet to participate in a clinical trial.
Approach:	They propose to recommend EC based on clinical trial information, including trial titles, and introduce an automatic evaluation framework to assess clinical validity of the EC recommendation model.
Outcome:	The proposed model outperforms existing language models pre-trained on the biomedical domain in EC clustering.

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Ang Li, Yiquan Wu, Yinghao Hu, Lizhi Qing, Shihang Wang, Chengyuan Liu, Tao Wu, Adam Jatowt, Ming Cai, Fei Wu, Kun Kuang

Information Retrieval

Challenge:	Recent methods to enhance queries by generating intermediary elements can degrade retrieval performance . combining LLMs and retrievers can be difficult, resulting in unreliable or irrelevant intermediaries .
Approach:	They propose a framework that facilitates the coevolution of large language models and retrieval models.
Outcome:	The proposed framework facilitates the coevolution of LLMs and retrieval models.

Robust Knowledge Editing via Explicit Reasoning Chains for Distractor-Resilient Multi-Hop QA (2025.findings-emnlp)

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Yuchen Wu, Liang Ding, Li Shen, Dacheng Tao

DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling (2021.acl-long)

Challenge:	Large language models encode vast amounts of knowledge but remain static once trained, making timely integration of emerging facts prohibitively expensive via full retraining.
Approach:	They introduce a reasoning-chain-based editing framework that steers a pretrained LLM through four structured stages to filter distractors in a single pass.
Outcome:	The proposed framework steers a pretrained LLM through four structured stages to filter distractors in a single pass.

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Lanqing Xue, Kaitao Song, Duocai Wu, Xu Tan, Nevin L. Zhang, Tao Qin, Wei-Qiang Zhang, Tie-Yan Liu

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

Challenge:	Existing systems for rap generation focus on rhyming lyrics but ignore rhythmic beats . rap lyrics need to be semantically meaningful and fashionable to convey interesting stories .
Approach:	They develop a Transformer-based rap generation system that can model both rhymes and rhythms.
Outcome:	The proposed system generates high-quality raps with rhymes and rhythms . it is based on a Transformer-based language model .

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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

Nested Browser-Use Learning for Agentic Information Seeking (2026.acl-long)

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.

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Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li, Zhongwang Zhang, Huifeng Yin, Zhengwei Tao, Liwen Zhang, Pengjun Xie, Jingren Zhou, Yong Jiang, Wentao Zhang, Zhiqiang Gao

Challenge:	Existing information-seeking (IS) agents rely on the web for their information acquisition.
Approach:	They propose a browser-action framework that decouples interaction control from page exploration through a nested structure.
Outcome:	Empirical results show that NestBrowse offers clear benefits in practice.

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

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

TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning (2026.findings-acl)

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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Jinyang Wu, Chonghua Liao, Mingkuan Feng, Shuai Zhang, Zhengqi Wen, Haoran Luo, Ling Yang, Huazhe Xu, Jianhua Tao

MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding (2024.findings-emnlp)

Challenge:	Existing RL methods rely on unstructured self-sampling to fit scalar rewards, resulting in inefficient rollouts.
Approach:	They propose a structured template-guided RL framework that augments policy optimization with explicit template guidance.
Outcome:	Experiments show that TemplateRL outperforms GRPO and GRPI by 99% on AIME and 41% on AMC with superior stability on weak models and remarkable cross-domain generalization.

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Qinzhuo Wu, Weikai Xu, Wei Liu, Tao Tan, Liujian Liujianfeng, Ang Li, Jian Luan, Bin Wang, Shuo Shang

Graph-based Dependency Parsing with Graph Neural Networks (P19-1)

Challenge:	Recent mobile AI agents based on VLMs lack basic mobile capabilities due to their pre-trained nature.
Approach:	They propose a mobile AI agent based on VLMs that includes additional pre-training stages to enhance both intra- and inter-UI understanding.
Outcome:	The proposed model outperforms existing VLMs on the Chinese mobile dataset Mobile3M .

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Tao Ji, Yuanbin Wu, Man Lan

Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching (2022.findings-naacl)

Challenge:	In graph-based dependency parsers, learning representations is gaining in importance, and we use graph neural networks to learn the representations.
Approach:	They propose to use graph neural networks to learn dependency tree nodes and propose to add a new aggregation function to the system.
Outcome:	The proposed model achieves the best UAS and LAS on PTB (96.0%, 94.3%) without using external resources.

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Kunbo Ding, Weijie Liu, Yuejian Fang, Zhe Zhao, Qi Ju, Xuefeng Yang, Rong Tian, Zhu Tao, Haoyan Liu, Han Guo, Xingyu Bai, Weiquan Mao, Yudong Li, Weigang Guo, Taiqiang Wu, Ningyuan Sun

ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages (2024.acl-long)

Challenge:	Existing studies have shown that cross-lingual knowledge distillation can improve the performance of pre-trained models for cross-linguistic similarity matching tasks.
Approach:	They propose a multi-stage distillation framework for constructing a small-size but high-performance cross-lingual model using contrastive learning, bottleneck, and parameter recurrent strategies.
Outcome:	The proposed model can compress the size of XLM-R and MiniLM by more than 50% while the performance is only reduced by about 1%.

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Junjie Ye, Sixian Li, Guanyu Li, Caishuang Huang, Songyang Gao, Yilong Wu, Qi Zhang, Tao Gui, Xuanjing Huang

Smart Word Suggestions for Writing Assistance (2023.findings-acl)

Challenge:	Existing research focuses on enhancing LLMs capabilities through tool utilization.
Approach:	They propose a framework to investigate safety issues in large language models in tool learning . they propose malicious queries and jailbreak attacks in the input stage .
Outcome:	The proposed framework investigates six safety scenarios for LLMs in tool learning . the data will be released upon acceptance of the proposed framework .

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Chenshuo Wang, Shaoguang Mao, Tao Ge, Wenshan Wu, Xun Wang, Yan Xia, Jonathan Tien, Dongyan Zhao

Challenge:	Using word suggestions, writing assistance is a widely used application of natural language processing (NLP) . a task is performed to identify words or phrases that require improvement and provide substitution suggestions for each improvable target.
Approach:	They propose a task and benchmark to help writers improve word usage . they use human-labeled data and a distantly supervised dataset for testing .
Outcome:	The proposed task and benchmark aims to improve word usage in writing aids.

Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers (2024.naacl-long)

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Yuan Wang, Xuyang Wu, Hsin-Tai Wu, Zhiqiang Tao, Yi Fang

V-GameGym: Visual Game Generation for Code Large Language Models (2026.findings-acl)

Challenge:	Recent studies have shown that Large Language Models (LLMs) are more efficient in natural language understanding tasks.
Approach:	They evaluate large language models (LLMs) using a TREC Fair Ranking dataset . they assess fairness from both user and content perspectives .
Outcome:	The proposed model outperforms the existing models in the fair ranking task.

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Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou

Robust Lottery Tickets for Pre-trained Language Models (2022.acl-long)

Challenge:	Existing code-related benchmarks focus on single modality rather than visual game development.
Approach:	They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis.
Outcome:	The proposed framework assesses code generation and visual game generation using a sandbox environment.

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Rui Zheng, Bao Rong, Yuhao Zhou, Di Liang, Sirui Wang, Wei Wu, Tao Gui, Qi Zhang, Xuanjing Huang

Meta-Task Prompting Elicits Embeddings from Large Language Models (2024.acl-long)

Challenge:	Recent studies have shown that pre-trained language models contain smaller matching subnetworks that are not robust to adversarial examples.
Approach:	They propose a method to find robust tickets hidden in pre-trained language models by learning binary weight masks and an adversarial loss objective to guide the search.
Outcome:	The proposed method improves on previous work on adversarial robustness evaluation.

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Yibin Lei, Di Wu, Tianyi Zhou, Tao Shen, Yu Cao, Chongyang Tao, Andrew Yates

A Unified Encoding of Structures in Transition Systems (2021.emnlp-main)

Challenge:	Existing methods for large language modeling are based on task-related instructions or prompts.
Approach:	They propose a method for generating high-quality sentence embeddings from Large Language Models (LLMs) using meta-task prompts.
Outcome:	The proposed method produces high-quality sentences without fine-tuning . it excels on STS benchmarks and in downstream tasks, surpassing models with similar prompts .

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Tao Ji, Yong Jiang, Tao Wang, Zhongqiang Huang, Fei Huang, Yuanbin Wu, Xiaoling Wang

Machine Translation With Weakly Paired Documents (D19-1)

Challenge:	Existing approaches to encode dynamic structures only encode partial information of structures.
Approach:	They propose a new attention-based encoder unifying all structures in a transition system.
Outcome:	The proposed method significantly improves the test speed and achieves the best transition-based model.

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Lijun Wu, Jinhua Zhu, Di He, Fei Gao, Tao Qin, Jianhuang Lai, Tie-Yan Liu

Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models (2026.acl-long)

Challenge:	Recent studies explore the possibility of unsupervised machine translation with monolingual data only.
Approach:	They propose a method to mine bilingual sentences from weakly paired documents . they use word distribution-level alignments to constrain word distributions of two weakly-paired documents.
Outcome:	The proposed method outperforms previous results on six translation tasks using weakly paired bilingual documents and a large number of bilingual sentences.

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Jinyang Wu, Mingkuan Feng, Shuai Zhang, Feihu Che, Zhengqi Wen, Chonghua Liao, Ling Yang, Haoran Luo, Zheng Lian, Jianhua Tao

Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling (2021.acl-short)

Challenge:	In-context learning (ICL) struggles with complex reasoning due to superficial, example-level implicit imitation.
Approach:	They propose an automated method that shifts from surface-level examples to more guidance-oriented thought patterns.
Outcome:	The proposed method achieves 80.6% accuracy on MATH and 62.5% on AMC, surpassing GPT-4o’s 77.2% and 57.5% accuracy.

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Revisiting Model Interpolation for Efficient Reasoning (2026.acl-long)

Challenge:	Existing approaches to model long documents are difficult due to the quadratic complexity of text length.
Approach:	They propose a hierarchical interactive Transformer for efficient long document modeling.
Outcome:	Extensive experiments on three benchmark datasets validate the efficiency and effectiveness of Hi-Transformer in long document modeling.

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Taiqiang Wu, Runming Yang, Tao Liu, Jiahao Wang, Ngai Wong

Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment (2026.acl-long)

Challenge:	Existing models that interpolate weights of two specialized models can be abused for efficient reasoning.
Approach:	They propose to merge two specialized models and create a model that combines efficiency and efficiency.
Outcome:	The proposed method outperforms existing models on efficiency and effectiveness.

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Yuming Yang, Mingyoung Lai, Wanxu Zhao, Xiaoran Fan, Zhiheng Xi, Mingqi Wu, Chiyue Huang, Jun Zhao, Haijun Lv, Jian Tong, Yunhua Zhou, Yicheng Zou, Qipeng Guo, Tao Gui, Qi Zhang, Xuanjing Huang

Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation (2022.findings-acl)

Challenge:	Existing methods assess suitability primarily through student likelihood, favoring trajectories that align closely with the student model’s current behavior but overlooking more informative ones.
Approach:	They propose a Rank–Surprisal Ratio metric that captures both alignment and informativeness to assess the suitability of a reasoning trajectory.
Outcome:	The proposed metric captures both alignment and informativeness to assess the suitability of a reasoning trajectory.

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Edit Once, Update Everywhere: A Simple Framework for Cross-Lingual Knowledge Synchronization in LLMs (2025.findings-acl)

Challenge:	Existing news recommender systems conduct news recall and ranking separately with different models, but maintaining multiple models leads to high computational cost and high latency.
Approach:	They propose a unified method for recall and ranking in news recommendation that uses historical news click behaviors to extract user embeddings for ranking from the user's attention query.
Outcome:	The proposed method improves recall and ranking efficiency and effectiveness on a benchmark dataset.

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Yuchen Wu, Liang Ding, Li Shen, Dacheng Tao

Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter (D18-1)

Challenge:	Existing methods to update large language models focus on single-language editing or basic multilingual editing, failing to achieve true cross-linguistic knowledge synchronization.
Approach:	They propose a cross-linguistic knowledge democracy edit technique to improve cross-lingual performance.
Outcome:	The proposed method improves cross-lingual performance while maintaining high accuracy in monolingual settings.

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Lijun Wu, Xu Tan, Di He, Fei Tian, Tao Qin, Jianhuang Lai, Tie-Yan Liu

Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models (2024.emnlp-main)

Challenge:	Neural machine translation suffers from exposure bias and error propagation problem.
Approach:	They conduct a series of analyses to deeply understand the accuracy drop problem . they find that the left part of the translated sentence is often better than its right part .
Outcome:	The results show that the left part of the translated sentence is often better than its right part in left-to-right decoding models.

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Yufang Liu, Tao Ji, Changzhi Sun, Yuanbin Wu, Aimin Zhou

Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models (2025.coling-main)

Challenge:	Existing studies have revealed that Large Vision-Language Models suffer from hallucinations in practice, including object hallucines, spatial hallucinos, attribute hallucinications, etc.
Approach:	They propose to use CLIP model to mitigate object hallucinations by using a data augmentation method to create negative samples with a variety of hallucinian issues.
Outcome:	The proposed method mitigates object hallucinations and can be used as a visual encoder, effectively alleviating the object halluination issue in LVLMs.

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Taiqiang Wu, Chaofan Tao, Jiahao Wang, Runming Yang, Zhe Zhao, Ngai Wong

Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study (2025.coling-main)

Challenge:	Large Language Models (LLMs) have been used in Knowledge Distillation (KD) to compress large models.
Approach:	They propose a Kullback-Leiber divergence method which adaptively allocates weights to combine RKL and FKL to reduce the size of Large Language Models (LLMs).
Outcome:	The proposed method outperforms baselines and improves diversity and quality of generated responses.

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Yulin Fei, Yuhui Gao, Xingyuan Xian, Xiaojin Zhang, Tao Wu, Wei Chen

TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use (2025.findings-emnlp)

Challenge:	a new benchmark evaluates video-based optical character recognition (Video OCR) performance of multi-modal models in videos . the benchmark aims to improve video LLMs' ability to extract text from video content . previous benchmarks have focused on video QA, but not video-related QA.
Approach:	They propose to evaluate the video OCR performance of multi-modal models in videos . they use a semi-automated approach that integrates the OCR ability of image LLMs with manual refinement .
Outcome:	The proposed benchmark includes 1,028 videos and 2,961 question-answer pairs . it integrates the OCR ability of image LLMs with manual refinement .

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Junjie Ye, Yilong Wu, Sixian Li, Yuming Yang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan, Zhengyin Du

UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction (2021.findings-acl)

Challenge:	a new approach to training large language models (LLMs) overlooks task-specific characteristics in tool use, leading to performance bottlenecks.
Approach:	They propose a task-feature-based framework that mitigates the effects of suboptimal training data . they use a dataset to train large-scale LLMs and a reward mechanism tailored to error categories .
Outcome:	The proposed framework matches or surpasses open- and closed-source LLMs in tool-use performance using only 1,217 training data points.

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Huanqin Wu, Wei Liu, Lei Li, Dan Nie, Tao Chen, Feng Zhang, Di Wang

PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision (2020.findings-emnlp)

Challenge:	Mainstream methods that ignore the diversity among keyphrases or weakly capture the relation between tasks implicitly ignore keyphrase diversity.
Approach:	They propose a novel end-to-end learning framework that jointly learns to extract and generate keyphrases by exploiting latent semantic relation between extraction and generation.
Outcome:	The proposed approach outperforms mainstream methods on a benchmarked document on keyphrase prediction.

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Chuhan Wu, Fangzhao Wu, Tao Qi, Jianxun Lian, Yongfeng Huang, Xing Xie

Pandora’s Box or Aladdin’s Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models (2025.acl-long)

Challenge:	Existing methods for user modeling cannot exploit useful information in unlabeled data . Existing models only model task-specific user information and do not exploit universal user information encoded in user behaviors.
Approach:	They propose to pre-train user models from large-scale unlabeled user behavior data.
Outcome:	The proposed method can model relatedness between historical and future behaviors on two real-world datasets.

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Jinyang Wu, Shuai Zhang, Feihu Che, Mingkuan Feng, Pengpeng Shao, Jianhua Tao

ALYMPICS: LLM Agents Meet Game Theory (2025.coling-main)

Challenge:	Retrieval-Augmented Generation (RAG) has emerged as a promising approach to address hallucinations in large language models (LLMs).
Approach:	They define seven distinct noise types from a linguistic perspective and establish a Noise RAG Benchmark (NoiserBench) they propose to evaluate noise that is beneficial to LLMs and noise that's harmful to LRMs.
Outcome:	The proposed framework consists of seven distinct noise types from a linguistic perspective and includes multiple datasets and reasoning tasks.

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Shaoguang Mao, Yuzhe Cai, Yan Xia, Wenshan Wu, Xun Wang, Fengyi Wang, Qiang Guan, Tao Ge, Furu Wei

Linguistics & Cognitive NLP

Challenge:	Alympics provides a framework for simulating human-like strategic interactions with Large Language Model (LLM) agents.
Approach:	They propose a framework utilizing Large Language Models (LLM) agents for empirical game theory research.
Outcome:	The proposed framework can be used to study human-like strategic interactions with large language model (LLM) agents in a game on the multi-round auction of scarce survival resources.

ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios (2025.coling-main)

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Junjie Ye, Guanyu Li, SongYang Gao, Caishuang Huang, Yilong Wu, Sixian Li, Xiaoran Fan, Shihan Dou, Tao Ji, Qi Zhang, Tao Gui, Xuanjing Huang

Playing 20 Question Game with Policy-Based Reinforcement Learning (D18-1)

Challenge:	Existing evaluations of tool learning focus on validation of tools for large language models with expected outcomes, but this focus ignores the complex capabilities required for LLMs to effectively use tools.
Approach:	They propose a fine-grained system for evaluation of large language models’ tool learning capabilities in authentic scenarios.
Outcome:	The proposed system examines seven real-world scenarios, analyzing five dimensions crucial to LLMs in tool learning: format alignment, intent comprehension, behavior planning, tool selection, and answer organization.

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Huang Hu, Xianchao Wu, Bingfeng Luo, Chongyang Tao, Can Xu, Wei Wu, Zhan Chen

PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity (2021.acl-long)

Challenge:	The 20 Questions (Q20) game encourages deductive reasoning and creativity.
Approach:	They propose a policy-based Reinforcement Learning method which learns optimal question selection . the method is robust to noisy answers and uses a reward network to estimate the more informative reward .
Outcome:	The proposed method outperforms an entropy-based engineering system and has competitive performance in noisy-free simulation environment.

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Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang

Challenge:	Existing personalized news recommendation methods have difficulties in making accurate recommendations to cold-start users.
Approach:	They propose to incorporate news popularity information to improve cold-start recommendations . they propose to use a popularity-aware user encoder to eliminate popularity bias .
Outcome:	The proposed method improves accuracy and diversity of personalized news recommendation on two real-world datasets.

ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection (2026.findings-acl)

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Tao Liu, Taiqiang Wu, Runming Yang, Shaoning Sun, Junjie Wang, Yujiu Yang

Challenge:	Traditional fine-tuning ignores one-to-many nature of language, leading to overfitting . authors propose a method to fine- tune LLMs by leveraging tokens.
Approach:	They propose a method to fine-tune Large Language Models by leveraging tokens to mask low-probability tokens.
Outcome:	The proposed method outperforms baselines on general reasoning and mathematical benchmarks.

DVAGen: Dynamic Vocabulary Augmented Generation (2025.emnlp-demos)

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Wei Du, Nuowei Liu, Jie Wang, Jiahao Kuang, Tao Ji, Xiaoling Wang, Yuanbin Wu

SentiRec: Sentiment Diversity-aware Neural News Recommendation (2020.aacl-main)

Challenge:	Existing dynamic vocabulary approaches struggle to generalize to novel or out-of-vocabulary words, limiting their flexibility in handling diverse token combinations.
Approach:	They propose an open-source framework for training, evaluation, and visualization of dynamic vocabulary-augmented language models.
Outcome:	The proposed framework validates the effectiveness of dynamic vocabulary-augmented language models on modern LLMs and shows support for batch inference significantly improving inference throughput.

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ZeroGen: Efficient Zero-shot Learning via Dataset Generation (2022.emnlp-main)

Sentiment Analysis

Challenge:	Existing news recommendation methods rank candidate news based on relevance to users’ historical browsed news, but if browsed data is dominated by certain kinds of sentiment, the model may recommend news with the same sentiment.
Approach:	They propose a sentiment diversity-aware neural news recommendation approach which can recommend news with more diverse sentiment without performance sacrifices.
Outcome:	The proposed approach can improve the sentiment diversity in news recommendation without performance sacrifice.

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Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, Lingpeng Kong

Neural News Recommendation with Multi-Head Self-Attention (D19-1)

Challenge:	Existing approaches to generate training data with pre-trained language models have been found effective in various scenarios.
Approach:	They propose an unsupervised zero-shot learning method that generates a dataset from scratch and trains a tiny task model under supervision of the synthesized dataset.
Outcome:	The proposed method is annotated-free and efficient, but can provide useful insights from the perspective of data-free model-agnostic knowledge distillation and unreferenced text generation evaluation.

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Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, Xing Xie

Learning to Express in Knowledge-Grounded Conversation (2022.naacl-main)

Challenge:	Precisely modeling news and users is critical for news recommendation, and capturing the contexts of words and news is important to learn news and user representations.
Approach:	They propose a neural news recommendation approach with multi-head self-attention to model the interactions between words and news and use multi-headed self- attention to capture relatedness between the news.
Outcome:	The proposed approach can learn representations from news titles by modeling the interactions between words and users and capture relatedness between the news.

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Xueliang Zhao, Tingchen Fu, Chongyang Tao, Wei Wu, Dongyan Zhao, Rui Yan

RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning (2024.emnlp-main)

Challenge:	Existing models focus on synthesizing a dialogue with proper knowledge, but neglect that the same knowledge could be expressed differently even under the same context.
Approach:	They propose a model that ground dialogue generation by extra knowledge by analyzing the structure of the response and the content style of each part.
Outcome:	The proposed model can learn the structure style defined by a few examples and generate responses in desired content style.

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Junjie Ye, Yilong Wu, Songyang Gao, Caishuang Huang, Sixian Li, Guanyu Li, Xiaoran Fan, Qi Zhang, Tao Gui, Xuanjing Huang

"Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations (2026.acl-long)

Challenge:	Current research emphasizes LLMs’ capacity to utilize tools in well-structured environments while overlooking their stability when confronted with the inevitable noise of the real world.
Approach:	They propose a multi-level benchmark to evaluate the robustness of large language models in tool learning by establishing five external environments with varying levels of noise.
Outcome:	The proposed model outperforms the GPT-4 model in tool learning in three critical phases: tool selection, parameter identification, and content filling.

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Yang Wu, Jinhong Yu, Jingwei Xiong, Zhimin Tao, Xiaozhong Liu

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)

Challenge:	Existing large language models (LLMs) are reactive and respond only when prompted, limiting their effectiveness in collaborative settings.
Approach:	They introduce a proactive LLM assistant designed to enhance biomedical collaboration between AI systems and human experts through timely, context-aware interventions.
Outcome:	The proposed model outperforms baselines in intervention precision and collaborative task utility, highlighting the potential of proactive LLMs as intelligent scientific assistants.

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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu

Exploiting Monolingual Data at Scale for Neural Machine Translation (D19-1)

Challenge:	Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately .
Approach:	They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes .
Outcome:	The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show .

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Lijun Wu, Yiren Wang, Yingce Xia, Tao Qin, Jianhuang Lai, Tie-Yan Liu

Measuring Human Contribution in AI-Assisted Content Generation (2026.acl-long)

Challenge:	Neural machine translation (NMT) is a well-known and expensive task.
Approach:	They propose a method to use target-side monolingual data for neural machine translation and propose 'synthetic bitext' they propose generating synthetic bitext by translating monolingual into the other domain using models pretrained on genuine bitext.
Outcome:	The proposed approach achieves state-of-the-art results on WMT16, WMT17, WTM18 EnglishGerman translations and WTM19 GermanFrench translations.

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Yueqi Xie, Tao Qi, Jingwei Yi, Xiyuan Yang, Ryan Whalen, Junming Huang, Qian Ding, Yu Xie, Xing Xie, Fangzhao Wu

ATLAS: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning (2026.findings-acl)

Challenge:	generative AI has created a new way to generate content with humans . varying degrees of human contribution in content generation poses significant challenges for the delineation of originality .
Approach:	They propose a framework to measure human contribution in AI-assisted content generation by calculating mutual information between human input and AI-aided output relative to self-information of AI-assist output.
Outcome:	The proposed measure discriminates between varying degrees of human contribution across multiple creative domains and is validated in real-world applications.

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Jinyang Wu, Guocheng Zhai, Ruihan Jin, Jiahao Yuan, Yuhao Shen, Shuai Zhang, Zhengqi Wen, Jianhua Tao

Efficient Prompting for Continual Adaptation to Missing Modalities (2025.naacl-long)

Challenge:	Existing approaches to optimize large language models with external tools are limited.
Approach:	They propose a dual-path framework for dynamic tool usage in cross-domain complex reasoning . they exploit empirical priors for domain-specific alignment and RL-based multi-step routing .
Outcome:	The proposed framework outperforms closed-source models and existing methods on in-distribution and out-of-distortion tasks.

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Zirun Guo, Shulei Wang, Wang Lin, Weicai Yan, Yangyang Wu, Tao Jin

UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost (2021.naacl-main)

Challenge:	Existing methods combine various missing cases to train recovery modules or align multimodal features, resulting in suboptimal performance, high computational costs, and catastrophic forgetting.
Approach:	They propose a continual multimodal missing modality task that uses prompts to learn modalities . existing methods often aggregate various missing cases to train recovery modules . authors conduct extensive experiments on three public datasets .
Outcome:	The proposed method consistently outperforms state-of-the-art methods on three public datasets.

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Zhen Wu, Lijun Wu, Qi Meng, Yingce Xia, Shufang Xie, Tao Qin, Xinyu Dai, Tie-Yan Liu

Towards Economical Inference: Enabling DeepSeek’s Multi-Head Latent Attention in Any Transformer-based LLMs (2025.acl-long)

Challenge:	Existing approaches to improve the performance of natural language processing models are over-parameterized and overfitted.
Approach:	They propose an approach to integrate dropout techniques into the training of Transformer models.
Outcome:	The proposed approach can achieve 1.5 BLEU improvement on IWSLT14 translation tasks and better accuracy for the classification even using strong pre-trained RoBERTa as backbone.

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Tao Ji, Bin Guo, Yuanbin Wu, Qipeng Guo, Shenlixing Shenlixing, Chenzhan Chenzhan, Xipeng Qiu, Qi Zhang, Tao Gui

MTO: A Multi-turn Conversational Text-to-OverpassQL Dataset for Enhanced OpenStreetMap Query Generation (2026.findings-acl)

Challenge:	Multi-head Latent Attention (MLA) is an innovative architecture designed to ensure efficient and economical inference by significantly compressing the Key-Value (KV) cache into a latent vector.
Approach:	They propose a data-efficient fine-tuning method for transitioning from MHA to MLA using a latent vector cache.
Outcome:	The proposed architecture reduces the KV cache size of Llama2-7B by 92.19%, with only 1% drop in LongBench performance.

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Haodi Zhang, Xinrui Zhu, Mingze Kong, Zhidan Liu, Tao Fan, Kaishun Wu, Yuanfeng Song

Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents (2025.acl-long)

Challenge:	a framework for constructing multi-turn Text-to-OverpassQL dialogue datasets is proposed . a dataset of over 7,800 dialogues contains more than 20,000 individual utterances .
Approach:	They propose a framework for constructing multi-turn Text-to-OverpassQL dialogue datasets . they convert Overpass queries into syntax trees using a custom parser based on OverpassQl .
Outcome:	The proposed dataset includes over 7,800 dialogues, each containing 2 to 4 user utterances . it is the first multi-turn Text-to-OverpassQL dataset built upon the OverpassNL corpus .

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Tao Wu, Jingyuan Chen, Wang Lin, Mengze Li, Yumeng Zhu, Ang Li, Kun Kuang, Fei Wu

Challenge:	Large language models (LLMs) are becoming increasingly popular in education, enabling researchers to simulate students' learning patterns and learning patterns.
Approach:	They propose a training-free framework for student simulation that takes into account student cognitive diversity and realism.
Outcome:	The proposed model outperforms baseline models and achieves 100% improvement in simulation accuracy and realism.

Trust Within? Seek Beyond? Knowledge Boundary Aware Policy Optimization for Agentic Search (2026.acl-long)

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Tao Feng, Xinke Jiang, Xinyan Hu, Yonggang Zhang, Zhen Tao, Wentao Zhang, Boyang Liu, Wenhao Jiang, Chao Wu

MIND: A Large-scale Dataset for News Recommendation (2020.acl-main)

Challenge:	Existing approaches to augment large language models with external knowledge suffer from a lack of calibration regarding the model’s knowledge boundary.
Approach:	They propose a reinforcement learning framework that explicitly aligns retrieval decisions with quantified knowledge states.
Outcome:	The proposed framework outperforms strong baselines while exhibiting reduced hallucination rates.

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Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou

Soft Contextual Data Augmentation for Neural Machine Translation (P19-1)

Challenge:	Personalized news recommendation is an important technique for personalized news service.
Approach:	They propose to build a large-scale news recommendation dataset from Microsoft News . they demonstrate that news recommendation relies on the quality of news content understanding .
Outcome:	The proposed dataset contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body.

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Fei Gao, Jinhua Zhu, Lijun Wu, Yingce Xia, Tao Qin, Xueqi Cheng, Wengang Zhou, Tie-Yan Liu

Machine Translation

Challenge:	Existing methods for enhancing training data are limited in natural language tasks due to text characteristics.
Approach:	They propose a data augmentation method that softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words.
Outcome:	The proposed method outperforms baseline methods on small and large scale machine translation datasets.

OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)

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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu

Rectifying the Emotional Flow: Aligning Priors and Dynamic Guidance for High-Arousal Text-to-Speech (2026.acl-long)

Challenge:	a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide.
Approach:	They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions .
Outcome:	The proposed agents are based on operating systems (OS) and operating systems frameworks.

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Fangming Feng, Dongjie Fu, Zequn Xie, Yu Zhang, Yangyang Wu, Zhou Zhao, Tao Jin

Context-Aware Cross-Attention for Non-Autoregressive Translation (2020.coling-main)

Challenge:	Existing systems suffer from linguistic collapse when pursuing high intensity or fail to meet target emotional levels.
Approach:	They propose an inference framework that introduces a neutral prosody bias and a uniform Classifier-Free Guidance that distorts the acoustic manifold, leading to artifacts.
Outcome:	The proposed framework achieves superior linguistic accuracy and expressiveness without model retraining.

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Liang Ding, Longyue Wang, Di Wu, Dacheng Tao, Zhaopeng Tu

Machine Translation

Challenge:	Existing studies have shown that non-autoregressive translation models can predict all tokens independently and simultaneously.
Approach:	They propose to enhance signals of neighbour source tokens into conventional cross-attention to address a locality perception problem in NAT cross- attention.
Outcome:	The proposed approach improves translation quality over strong NAT baselines on representative datasets.

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)

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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang

Named Entity Recognition in Multi-level Contexts (2020.aacl-main)

Challenge:	Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction.
Approach:	They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack.
Outcome:	The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses.

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Yubo Chen, Chuhan Wu, Tao Qi, Zhigang Yuan, Yongfeng Huang

BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning (2024.findings-acl)

Challenge:	Existing methods for named entity recognition are unsatisfactory for recognizing entities in limited or ambiguous sentence-level contexts.
Approach:	They propose a framework to incorporate multi-level contexts for named entity recognition using TagLM as a baseline model and an auxiliary task to mine word-level contextual information.
Outcome:	The proposed framework is based on a set of sentence-level contexts and a document-level task to mine word-level contextual information.

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Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, Rui Yan

NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application (2021.findings-emnlp)

Challenge:	BioT5+ is an extension of the BioT5, but lacked a nuanced understanding of molecular structures.
Approach:	They propose a new bio-entity modeling framework, BioT5+, which integrates IUPAC names and molecule data.
Outcome:	The proposed model bridges the gap between molecular representations and textual descriptions and improves the grounded reasoning of bio-text and bio-sequences.

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Chuhan Wu, Fangzhao Wu, Yang Yu, Tao Qi, Yongfeng Huang, Qi Liu

Multi-grained Named Entity Recognition (P19-1)

Challenge:	Existing language models are pre-trained and distilled on general corpus like Wikipedia, which has gaps with the news domain and may be suboptimal for news intelligence.
Approach:	They propose a method to distill existing language models on Wikipedia to enable efficient news intelligence.
Outcome:	The proposed model can be used to build and test a news intelligence application on Wikipedia and Wikipedia.

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Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip Yu

Generation with Dynamic Vocabulary (2024.emnlp-main)

Challenge:	Existing approaches treat Named Entity Recognition (NER) as a sequence labeling task.
Approach:	They propose a framework for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.
Outcome:	The proposed framework outperforms current state-of-the-art frameworks by 4.4% in terms of the F1 score among nested/non-overlapping NER tasks.

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Yanting Liu, Tao Ji, Changzhi Sun, Yuanbin Wu, Xiaoling Wang

ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback (2022.findings-emnlp)

Challenge:	Using static vocabulary, vocabulary is ignored in advanced generation tasks.
Approach:	They propose a dynamic vocabulary that can involve arbitrary text spans during generation.
Outcome:	The proposed vocabulary can be deployed in a plug-and-play way, thus is attractive for various downstream applications.

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Jiacheng Ye, Jiahui Gao, Zhiyong Wu, Jiangtao Feng, Tao Yu, Lingpeng Kong

Low-Resource NLP

Challenge:	Recent work on dataset-generation-based zero-shot learning has shown promising results by training a task-specific model with a dataset synthesized from large pre-trained language models (PLMs).
Approach:	They propose a progressive zero-shot dataset generation framework which leverages feedback from the task-specific model to guide the generation of new training data via in-context examples.
Outcome:	The proposed framework achieves on-par or superior performance with only 1% synthetic dataset size, when compared to baseline methods without in-context feedback.

FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information (2022.emnlp-main)

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Yijia Shao, Mengyu Zhou, Yifan Zhong, Tao Wu, Hongwei Han, Shi Han, Gideon Huang, Dongmei Zhang