Papers by Yao Wei

Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models (2024.findings-emnlp)

Challenge:	Large Language Models (LLMs) are increasingly integrated into our daily lives, raising ethical concerns, especially about perpetuating stereotypes.
Approach:	They propose a method that incorporates a neutral word semantics-based loss function to alleviate the deterioration of the LMS during debiasing.
Outcome:	The proposed method alleviates the deterioration of the Language Modeling Score (LMS) by incorporating a neutral word semantics-based loss function.

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Kai Yao, Penglei Gao, Lichun Li, Yuan Zhao, Xiaofeng Wang, Wei Wang, Jianke Zhu

Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA (2023.emnlp-main)

Challenge:	Parameter-Efficient Fine-Tuning (PEFT) methods have gained popularity for adapting pre-trained Large Language Models (LLMs) to downstream tasks.
Approach:	They propose a method to optimize the importance of full layers with layer-wise importance scoring by leveraging the estimated importance scores.
Outcome:	The proposed method is compatible with PEFT methods that operate on a per-layer basis and achieves better performance.

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David Heineman, Yao Dou, Mounica Maddela, Wei Xu

GMH: A General Multi-hop Reasoning Model for KG Completion (2021.emnlp-main)

Challenge:	Traditional human evaluation methods for text simplification often relies on individual, shallow sentence-level ratings, easily affected by the annotator's preference or bias.
Approach:	They propose an edit-based human annotation framework that enables holistic and fine-grained text simplification evaluation.
Outcome:	The proposed framework is able to predict sentence- and word-level quality simultaneously and report promising results.

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Yao Zhang, Hongru Liang, Adam Jatowt, Wenqiang Lei, Xin Wei, Ning Jiang, Zhenglu Yang

Structured Data in NLP

Challenge:	Knowledge graphs are incomplete with many facts missing, causing performance bottlenecks in many applications.
Approach:	They propose a general multi-hop reasoning task that can be formulated as a search process and can be extended to long-distance reasoning scenarios.
Outcome:	The proposed model improves on baselines in short and long distance reasoning scenarios.

ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching (2026.findings-acl)

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Han Zhu, Wei Kang, Liyong Guo, Zengwei Yao, Fangjun Kuang, Weiji Zhuang, Zhaoqing Li, Zhifeng Han, Dong Zhang, Xin Zhang, Xingchen Song, Lingxuan Ye, Long Lin, Daniel Povey

ShifCon: Enhancing Non-Dominant Language Capabilities with a Shift-based Multilingual Contrastive Framework (2025.acl-long)

Challenge:	Existing autoregressive models for dialogue generation suffer from high latency and stability issues.
Approach:	They propose a non-autoregressive (NAR) zero-shot spoken dialogue generation model based on flow-matching.
Outcome:	The proposed model outperforms existing models in speech generation due to poor speech intelligibility and turn-taking precision.

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Hengyuan Zhang, Chenming Shang, Sizhe Wang, Dongdong Zhang, Yiyao Yu, Feng Yao, Renliang Sun, Yujiu Yang, Furu Wei

Multilinguality

Challenge:	Experiments show that ShifCon significantly enhances the performance of non-dominant languages due to the imbalance in training data across languages.
Approach:	They propose a Shift-based multilingual Contrastive framework that aligns the internal forward process of other languages toward that of the dominant one.
Outcome:	The proposed framework significantly improves performance of non-dominant languages, particularly for low-resource ones.

Learning by Analogy: Diverse Questions Generation in Math Word Problem (2023.findings-acl)

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Zihao Zhou, Maizhen Ning, Qiufeng Wang, Jie Yao, Wei Wang, Xiaowei Huang, Kaizhu Huang

Reasoning

Challenge:	Existing methods for solving math word problem (MWP) use shortcut learning to train solvers based on samples with a single question.
Approach:	They propose to generate diverse yet consistent questions from a common scenario . they then feed the equations to a question generator to obtain the diverse questions . their method leads to performance improvement on the current benchmark Math23K .
Outcome:	The proposed method generates diverse yet consistent questions with a variety of equations and questions . it improves on the current benchmark, which is based on the proposed method .

OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving (2026.findings-acl)

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Xinyu Zhang, Boxuan Zhang, Yuchen Wan, Lingling Zhang, YiXing Yao, Bifan Wei, Yaqiang Wu, Jun Liu

LLMs Trust Humans More, That’s a Problem! Unveiling and Mitigating the Authority Bias in Retrieval-Augmented Generation (2025.acl-long)

Challenge:	Existing benchmarks focus on Mathematical Programming and Combinatorial Optimization, hindering comprehensive evaluation.
Approach:	They propose a benchmarking tool that compares 1,000 curated optimization problems across three difficulty levels.
Outcome:	The proposed model improves performance on hard problems while maintaining 27% accuracy.

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Yuxuan Li, Xinwei Guo, Jiashi Gao, Guanhua Chen, Xiangyu Zhao, Jiaxin Zhang, Quanying Liu, Haiyan Wu, Xin Yao, Xuetao Wei

Information Retrieval

Challenge:	Large language models (LLMs) generate outputs that stray from user input or contravene established knowledge.
Approach:	They propose a new phenomenon, Authority Bias, where LLMs favor one knowledge source over the other . they propose atomic information that generates conflicts and a Conflict Detection Enhanced Query framework .
Outcome:	The proposed framework reduces Authority bias in large language models . it detects conflicts, performs credibility assessment on conflicting paragraphs, and detects perturbed text .

Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension (2022.acl-long)

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Huibin Zhang, Zhengkun Zhang, Yao Zhang, Jun Wang, Yufan Li, Ning Jiang, Xin Wei, Zhenglu Yang

Mitigating the Alignment Tax of RLHF (2024.emnlp-main)

Challenge:	Procedural Multimodal Documents organize textual instructions and corresponding images step by step.
Approach:	They propose a novel temporal-modal entity Graph for comprehending PMDs . they propose encoding and reasoning modules to capture textual and visual entities .
Outcome:	The proposed model can capture textual and visual entities and trace their temporal-modal evolution.

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Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang

Improving Alignment in LVLMs with Debiased Self-Judgment (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax.
Approach:	They propose to use a model averaging technique to find the most powerful alignment-forging Pareto front among RLHF algorithms.
Outcome:	The proposed method achieves the strongest alignment-forging Pareto front among competing methods.

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Sihan Yang, Chenhang Cui, Zihao Zhao, Yiyang Zhou, Weilong Yan, Ying Wei, Huaxiu Yao

Walking in Others’ Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias (2024.emnlp-main)

Challenge:	Existing methods for aligning LVLMs rely on external datasets, human annotations or complex post-processing.
Approach:	They propose a method that generates a debiased self-judgment score for LVLMs . this self-evaluation metric is created internally by the model without external resources .
Outcome:	The proposed approach outperforms existing methods in reducing hallucinations and safety concerns.

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Rongwu Xu, Zian Zhou, Tianwei Zhang, Zehan Qi, Su Yao, Ke Xu, Wei Xu, Han Qiu

AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)

Challenge:	Existing prompting methods that require white-box access to the model or substantial training fail to simultaneously lessen toxicity and bias.
Approach:	They propose a strategy that encourages LLMs to integrate diverse human perspectives and self-regulate their responses by incorporating diverse human viewpoints.
Outcome:	The proposed approach can significantly diminish toxicity (up to 89%) and bias (up 73%) in LLMs’ responses.

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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun

Improving Large-scale Paraphrase Acquisition and Generation (2022.emnlp-main)

Challenge:	Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs.
Approach:	They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction.
Outcome:	The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI.

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Yao Dou, Chao Jiang, Wei Xu

Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)

Challenge:	Existing Twitter-based paraphrase datasets lack quality definitions for identification and generation tasks.
Approach:	They propose to use two separate definitions of paraphrase for identification and generation tasks in existing Twitter-based paraphrase datasets.
Outcome:	The proposed model achieves state-of-the-art performance of 84.2 F1 for automatic paraphrase identification compared to other models fine-tuned on other corpora such as Quora, MSCOCO, and ParaNMT.

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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan

Natural Language Interfaces

Challenge:	Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems.
Approach:	They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations.
Outcome:	The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods.

MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)

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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen

Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition (2026.acl-long)

Challenge:	Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed .
Approach:	They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities.
Outcome:	The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech .

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Tiejin Chen, Huaiyuan Yao, Jia Chen, Evangelos E. Papalexakis, Hua Wei

Reasoning

Challenge:	Existing methods for MAS fail to address the unique complexities of multi-step reasoning . Existing uncertainty quantification methods struggle with cascading uncertainty .
Approach:	They propose a framework that quantifies uncertainty through tensor decomposition . they show that MATU effectively estimates holistic and robust uncertainty .
Outcome:	The proposed framework disentangles and quantifies distinct sources of uncertainty . it is generalizable across different agent structures and can be used for scientific discovery, education, healthcare and transportation.

PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models (2022.emnlp-main)

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Yuan Yao, Qianyu Chen, Ao Zhang, Wei Ji, Zhiyuan Liu, Tat-Seng Chua, Maosong Sun

Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models (2026.acl-long)

Challenge:	Recent advances on self-supervised learning have led to powerful vision-language pre-training models that achieve state-of-the-art performance on a wide range of cross-modal tasks.
Approach:	They propose a vision-language pre-training framework that reformulates discretized object positions and language in a unified language modeling framework.
Outcome:	The proposed model improves performance on position-sensitive vision-language (VL) tasks and also improves on position insensitive tasks.

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Wei Wu, Liyi Chen, Congxi Xiao, Tianfu Wang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Hui Xiong

Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models (2024.findings-acl)

Challenge:	Existing efficient reasoning methods rely on explicit length penalties for excessive verbosity on simple queries.
Approach:	They propose a training-time intervention that selectively suppresses redundant tokens . they find length shift occurs when models generate unnecessary reasoning on trivial inputs - a phenomenon that is often unexplored .
Outcome:	The proposed method reduces inference token usage by 78% while increasing accuracy compared to the initial policy and surpasses state-of-the-art efficient reasoning methods.

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Chen Qian, Jie Zhang, Wei Yao, Dongrui Liu, Zhenfei Yin, Yu Qiao, Yong Liu, Jing Shao

GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models (2025.acl-long)

Challenge:	Existing studies focus on pre-trained LLMs to better understand and improve their trustworthiness.
Approach:	They apply linear probing to LLMs to explore five key dimensions of trustworthiness: reliability, privacy, toxicity, fairness, and robustness.
Outcome:	The proposed model can distinguish concepts in each trustworthiness dimension, suggesting that it can be trained in early pre-training.

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Kai Yao, Zhaorui Tan, Penglei Gao, Lichun Li, Kaixin Wu, Yinggui Wang, Yuan Zhao, Yixin Ji, Jianke Zhu, Wei Wang

TailorKV: A Hybrid Framework for Long-Context Inference via Tailored KV Cache Optimization (2025.findings-acl)

Challenge:	Existing methods for offsite-tuning of large language models require high computational costs and lack theoretical analysis.
Approach:	They propose an offsite-tuning approach that selectively applies compression techniques such as rank compression and channel pruning to preserve the gradients of fine-tuned adapters while ensuring privacy.
Outcome:	The proposed method surpasses existing OT methods in privacy protection and model performance.

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Dingyu Yao, Bowen Shen, Zheng Lin, Wei Liu, Jian Luan, Bin Wang, Weiping Wang

Challenge:	Existing work mitigates memory overhead by offloading or compressing the Key-Value cache.
Approach:	They propose a method that integrates quantization and offloading into a generative large language model by using a hybrid compression method.
Outcome:	The proposed method outperforms the state-of-the-art in long-context evaluations.

DeepWell-Adol: A Scalable Expert-Based Dialogue Corpus for Adolescent Positive Mental Health and Wellbeing Promotion (2025.emnlp-main)

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Wenyu Qiu, Yuxiong Wang, Jiajun Tan, Hanchao Hou, Qinda Liu, Wei Yao, Shiguang Ni

Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method (D18-1)

Challenge:	Promoting positive mental health and well-being is a critical yet underexplored area in natural language processing.
Approach:	They propose a Chinese dialogue corpus grounded in positive psychology and coaching that integrates human expert-written seed data with automated data augmentation to ensure high quality and scalability.
Outcome:	The proposed corpus meets general standards for psychological dialogue and emotional support while also showing superior performance across multiple models in promoting positive psychological processes, character strengths, interpersonal relationships, and healthy behaviors.

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Yahui Liu, Wei Bi, Jun Gao, Xiaojiang Liu, Jian Yao, Shuming Shi

Can LLMs Hear the Dogwhistle? (2026.findings-acl)

Challenge:	Experimental results show that Sequence-to-sequence models tend to generate generic/dull responses .
Approach:	They propose a statistical re-weighting method that assigns different weights for multiple responses of the same query.
Outcome:	The proposed method improves acceptance rate of generated responses and significantly reduces generated generic responses.

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Yifan Liu, Yi Lin, Xinwei Guo, Ziwei Wang, Jiaxin Zhang, Guanhua Chen, Haiyan Wu, Xiangyu Zhao, Xin Yao, Xuetao Wei

EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation (2025.findings-emnlp)

Challenge:	Existing safety benchmarks focus on explicitly harmful content, but ignore context-dependent expressions such as dogwhistles.
Approach:	They propose a benchmark for evaluating LLM safety under dogwhistle-driven prompts . their findings expose a blind spot in current safety evaluation practices .
Outcome:	The proposed benchmark compared safety performance with toxic terms using dogwhistle-driven prompts.

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Xinyi Mou, Chen Qian, Wei Liu, Ling Yan, Yao Hu, Xuanjing Huang, Zhongyu Wei

Green & Sustainable NLP

Challenge:	Large language models (LLMs) have demonstrated impressive ability to role-play humans and replicate complex social dynamics.
Approach:	They propose an efficient agent communication language induction for social simulations that reduces token consumption by over 20%.
Outcome:	The proposed model reduces token consumption by over 20% while preserving human language.

P²Net: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts (2025.findings-acl)

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Kaiwen Wei, Jie Yao, Jiang Zhong, Yangyang Kang, Jingyuan Zhang, Changlong Sun, Xin Zhang, Fengmao Lv, Li Jin

Information Extraction & Text Mining

Challenge:	Existing methods for key information extraction are based on a limited set of entity categories and fixed layouts.
Approach:	They propose a large-scale, human-annotated dataset for key information extraction . it is based on a human-annotated layout and 1,162 entity categories . they propose 'parallel pointer-based network' that leverages implicit relationships .
Outcome:	Experiments on widely-used datasets show that the proposed model outperforms state-of-the-art methods while maintaining fast inference speeds.

Assist Non-native Viewers: Multimodal Cross-Lingual Summarization for How2 Videos (2022.emnlp-main)

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Nayu Liu, Kaiwen Wei, Xian Sun, Hongfeng Yu, Fanglong Yao, Li Jin, Guo Zhi, Guangluan Xu

DeepPlanner: Scaling Planning Capability for Deep Research Agents via Advantage Shaping (2026.findings-acl)

Challenge:	Existing multimodal summarization methods are limited to monolingual videos . a proposed task aims to generate cross-lingual summaries from multimodal inputs .
Approach:	They propose a task to generate cross-lingual summaries from multimodal inputs of videos . they propose fusion network that integrates multimodal and cross-linguistic information .
Outcome:	The proposed task outperforms existing methods on a reorganized How2 dataset on the reorganized How2 data set.

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Wei Fan, Wenlin Yao, Zheng Li, Feng Yao, Xin Liu, Liang Qiu, Qingyu Yin, Yangqiu Song, Bing Yin

Reasoning

Challenge:	Existing approaches to planning involve implicit planning or introduce explicit planners without systematically optimizing the planning stage.
Approach:	They propose an end-to-end RL framework that enhances the planning capabilities of deep research agents.
Outcome:	Experiments show that DeepPlanner improves planning quality and achieves state-of-the-art results under a lower training budget.

WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning (2025.emnlp-main)

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Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li

Challenge:	Existing work on reinforcement learning has focused on single-turn tasks such as solving math problems.
Approach:	They propose a framework that learns directly from online interactions by asynchronously generating diverse trajectories, guided by binary rewards depending on task success.
Outcome:	Experiments on the WebArena-Lite benchmark show that the framework outperforms state-of-the-art methods and strong proprietary models.

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)

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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song

PURE: Aligning LLM via Pluggable Query Reformulation for Enhanced Helpfulness (2024.findings-emnlp)

Challenge:	Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage.
Approach:	They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas.
Outcome:	The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality.

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Wenjin Yao, Yidong Wang, Zhuohao Yu, Rui Xie, Shikun Zhang, Wei Ye

SimulatorArena: Are User Simulators Reliable Proxies for Multi-Turn Evaluation of AI Assistants? (2025.emnlp-main)

Challenge:	Large language models (LLMs) depend on vast amounts of text data sourced from the Internet for their training.
Approach:	They propose a new alignment paradigm that reformulates risky queries into highly relevant yet harmless ones before feeding them into LLMs.
Outcome:	The proposed approach eliminates the high costs of training base LLMs and achieves a promising balance of harmlessness and helpfulness.

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Yao Dou, Michel Galley, Baolin Peng, Chris Kedzie, Weixin Cai, Alan Ritter, Chris Quirk, Wei Xu, Jianfeng Gao

Does Memory Need Graphs? A Unified Framework and Empirical Analysis for Long-Term Dialog Memory (2026.acl-long)

Challenge:	Large language models (LLMs) are increasingly used in interactive applications, and human evaluation remains the gold standard for assessing their performance in multi-turn conversations.
Approach:	They propose to use large language models to simulate users for automatic assistant evaluation.
Outcome:	The proposed model outperforms human evaluations on two interactive tasks and achieves Spearman’s of 0.7 on both tasks.

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Sen Hu, Yuxiang Wei, Jiaxin Ran, Xueran Han, Zhiyuan Yao, Huacan Wang, Ronghao Chen, Lei Zou

Natural Language Interfaces

Challenge:	Existing literature on dialog memory systems is inconsistent on their effectiveness . empirical findings on graph structures are difficult to attribute to specific design choices .
Approach:	They propose a framework that decomposes dialog memory systems into core components . they conduct stage-wise experiments on LongMemEval and HaluMeM, and compare implementation details .
Outcome:	The proposed framework compares graph-based and non-graph memory architectures on long-term dialog memory systems.

HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level (2023.acl-long)

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Haoran Luo, Haihong E, Yuhao Yang, Yikai Guo, Mingzhi Sun, Tianyu Yao, Zichen Tang, Kaiyang Wan, Meina Song, Wei Lin

Random Smooth-based Certified Defense against Text Adversarial Attack (2024.findings-eacl)

Challenge:	Existing research on HKGs rarely models the graphical and sequential structure of HKG, limiting their representation.
Approach:	They propose a Hierarchical Attention model for HKG Embedding that includes global-level and local-level attention to model the graphical structure of HKGs.
Outcome:	The proposed model achieves state-of-the-art performance on HKG standard datasets and addresses the issue of HKG multi-position prediction for the first time.

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Zeliang Zhang, Wei Yao, Susan Liang, Chenliang Xu

Implicit Relation Linking for Question Answering over Knowledge Graph (2022.findings-acl)

Challenge:	Textual adversarial examples train models on the worst-case text generated by substituting words in original texts with synonyms, but due to the discrete word embedding representations, the large search space hinders the robust training efficiency.
Approach:	They propose to treat the word substitution as a continuous perturbation on the word embedding representation and apply random smooth techniques to approximate the word replacement operation.
Outcome:	The proposed method outperforms conventional methods and improves the robustness in training.

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Yao Zhao, Jiacheng Huang, Wei Hu, Qijin Chen, XiaoXia Qiu, Chengfu Huo, Weijun Ren

SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling (2025.findings-acl)

Challenge:	Existing methods rely on textual similarities between NL and KG to build relation links.
Approach:	They propose an implicit relation linking method called ImRL which links relation phrases in NL to relation paths in KG.
Outcome:	The proposed method significantly outperforms state-of-the-art methods on two benchmarks and a newly-created datasets.

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Haoran Wang, Zhenyu Hou, Yao Wei, Jie Tang, Yuxiao Dong

Challenge:	Large language models (LLMs) have advanced rapidly from conversational problem solving to addressing real-world tasks involving tool use, such as software engineering (SWE).
Approach:	They propose to build an LLM-based software engineering agent that synthesizes test cases and scales up agent trajectories to build training data.
Outcome:	The proposed model outperforms state-of-the-art models on the SWE-bench-Verified benchmark.

Libra-VLA: Achieving Learning Equilibrium via Asynchronous Coarse-to-Fine Dual-System (2026.acl-long)

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Yifei Wei, Linqing Zhong, Yi Liu, Yuxiang Lu, Xindong He, Maoqing Yao, Guanghui Ren

Challenge:	Vision-Language-Action models ground high-level semantic instructions into executable physical actions.
Approach:	They propose a Coarse-to-Fine Dual-System VLA architecture that decouples learning complexity into a coarse-to fine hierarchy while leveraging structural modularity to implement an asynchronous execution strategy.
Outcome:	The proposed architecture decouples learning complexity into a coarse-to-fine hierarchy while leveraging structural modularity to implement an asynchronous execution strategy.

Joint Pre-Encoding Representation and Structure Embedding for Efficient and Low-Resource Knowledge Graph Completion (2024.emnlp-main)

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Chenyu Qiu, Pengjiang Qian, Chuang Wang, Jian Yao, Li Liu, Fang Wei, Eddie Eddie

Don’t Eclipse Your Arts Due to Small Discrepancies: Boundary Repositioning with a Pointer Network for Aspect Extraction (2020.acl-main)

Challenge:	Existing knowledge graph completion models require longer training and inference times as well as increased memory usage.
Approach:	They propose to encode textual descriptions into semantic representations before training and integrate structural embedding with pre-encoded semantic description to improve model's prediction performance on 1-N relations.
Outcome:	The proposed model increases inference speed by 30x and reduces training memory by approximately 60% on the WN18RR and UMLS datasets.

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Zhenkai Wei, Yu Hong, Bowei Zou, Meng Cheng, Jianmin Yao

Sentiment Analysis

Challenge:	Existing aspect extraction methods suffer from boundary errors, but they hurt performance severely.
Approach:	They propose to use a pointer network to reposition the boundaries of extracted aspects . they conduct experiments on laptop and restaurant benchmark datasets .
Outcome:	The proposed method outperforms state-of-the-art methods on benchmark datasets . it achieves substantial improvements over baseline and outperformed existing methods .

PAFT: Prompt-Agnostic Fine-Tuning (2025.emnlp-main)

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Chenxing Wei, Yao Shu, Mingwen Ou, Ying He, Fei Yu

Decomposed Prompt Tuning via Low-Rank Reparameterization (2023.findings-emnlp)

Challenge:	Prompt-agnostic fine-tuning (PAFT) improves performance by reducing overfitting to specific prompts.
Approach:	They propose a method that enhances robustness through dynamic prompt variation during training.
Outcome:	The proposed method achieves higher generalization accuracy on unseen prompts than standard methods with similar training efficiency.

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Yao Xiao, Lu Xu, Jiaxi Li, Wei Lu, Xiaoli Li

E-ViC: Reasoning Beyond Text via Embodied Visual Chain for Spatial Intelligence (2026.acl-long)

Challenge:	Pre-trained language models have achieved remarkable performance on various tasks.
Approach:	They propose a decomposed prompt tuning approach that utilizes low-rank matrices to initialize the soft prompt.
Outcome:	The proposed method significantly reduces the number of trainable parameters while maintaining effectiveness.

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Junbo Qi, Yi Zhang, Hanchu Ni, Che Liu, Zhimin Yao, Ruilin Yang, Xiancong Ren, Liangjian Wen, Wei Ge, Yuya Ieiri, Osamu Yoshie, Yong Dai, Xiaozhu Ju

From Discrimination to Generation: Low-Resource Intent Detection with Language Model Instruction Tuning (2024.findings-acl)

Challenge:	Existing Vision-Language Models (VLMs) lack spatial reasoning, despite text-based CoTs . e-ViC reframes spatial intelligence as a verifiable, tool-using capability, argues a new study.
Approach:	They propose a framework that moves reasoning beyond text into the visual domain . they ground reasoning in pixel-level interactions to enable human-like "look-and-confirm" strategies .
Outcome:	The proposed framework outperforms existing Vision-Language Models with an average gain of 10.1%.

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Feng Zhang, Wei Chen, Fei Ding, Meng Gao, Tengjiao Wang, Jiahui Yao, Jiabin Zheng

Uncertainty Propagation on LLM Agent (2025.acl-long)

Challenge:	Existing studies fine-tune discriminative models on specific defined intent classes, preventing them from being directly adopted to new intent domains.
Approach:	They propose to use a pre-trained generative intent model to detect new intents from different domains with no parameter updates.
Outcome:	The proposed model outperforms baselines that need further fine-tuning or domain-specific samples.

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Qiwei Zhao, Dong Li, Yanchi Liu, Wei Cheng, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Huaxiu Yao, Chen Zhao, Haifeng Chen, Xujiang Zhao

Challenge:	Existing methods for estimating uncertainty in large language models (LLMs) focus on final-step outputs, which fail to account for cumulative uncertainty over multi-step decision-making process and dynamic interactions between agents and their environments.
Approach:	They propose a framework that propagates uncertainty through each step of an LLM-based agent’s reasoning process.
Outcome:	Extensive experiments on benchmark datasets show that the proposed framework outperforms state-of-the-art methods by 20%.

Revisiting Weak-to-Strong Generalization in Theory and Practice: Reverse KL vs. Forward KL (2025.findings-acl)

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Wei Yao, Wenkai Yang, Ziqiao Wang, Yankai Lin, Yong Liu

Linguistics & Cognitive NLP

Challenge:	Weak-to-strong generalization is a promising approach to guide stronger systems, but its effectiveness is constrained by the inherent imperfections of weak model supervision.
Approach:	They propose a theoretically grounded approach that replaces forward KL divergence with reverse KL, which prioritizes high-confidence predictions.
Outcome:	The proposed approach replaces forward KL divergence with reverse KL, reducing the influence of unreliable weak supervision.

LENS: A Learnable Evaluation Metric for Text Simplification (2023.acl-long)

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Mounica Maddela, Yao Dou, David Heineman, Wei Xu

VecInfer: Efficient LLM Inference with Low-Bit KV Cache via Outlier-Suppressed Vector Quantization (2026.acl-long)

Challenge:	Existing metrics for text simplification are based on unitary or outdated models, making them unsuitable for this approach.
Approach:	They present a learnable evaluation metric for text simplification using language models . they also introduce a human evaluation framework that rates simplifications from several models a list-wise manner .
Outcome:	The proposed model correlates much better with human judgment than existing metrics.

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Dingyu Yao, Chenxu Yang, Zhengyang Tong, Zheng Lin, Wei Liu, Jian Luan, Weiping Wang

Green & Sustainable NLP

Challenge:	Existing quantization methods for large language models suffer performance degradation at ultra-low bit-widths due to key cache outliers.
Approach:	They propose a vector quantization method that suppresses outliers in the key cache and reduces memory access overhead.
Outcome:	The proposed method outperforms baseline quantization methods across long-context understanding and mathematical reasoning tasks while minimizing memory access overhead.

MessToClean: Evidence-Grounded Structure-Preserving Reconstruction for Real-World Degraded Exam Paper Images (2026.acl-long)

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Jiayi Tuo, Cheng Tang, Zihan Wang, Chenyue Zhou, Yao Li, Yanbiao Ma, Chao Wang, Wei Dai, Mingxuan Wang, Shitong Qin, Ziwei Zhao

SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2022.acl-long)

Challenge:	Existing Multimodal Large Language Models (MLLMs) fail under RDEI, leading to disrupted structure and evidence-unsupported hallucinations.
Approach:	They propose a backbone-agnostic, evidence-driven pipeline that treats off-the-shelf MLLMs as interchangeable components to improve stem consistency and figure consistency.
Outcome:	The proposed pipeline improves stem consistency by 1.01-3.18%, figure consistency by 0.50-49.16%, and refusal F1 by 1.06-10.88% across question types.

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Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei

Thresh: A Unified, Customizable and Deployable Platform for Fine-Grained Text Evaluation (2023.emnlp-demo)

Challenge:	Existing work shows that pre-trained models can improve in various natural language processing tasks.
Approach:	They propose a unified-modal encoder-decoder framework that pre-trains speech-text representations using large-scale unlabeled speech and text data.
Outcome:	The proposed framework is superior to existing models on speech-to-text processing tasks.

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David Heineman, Yao Dou, Wei Xu

Challenge:	Existing tools for fine-grained human evaluation lack adaptability to different domains or languages, or modify annotation settings according to user needs.
Approach:	They propose a unified platform for fine-grained evaluation that is customizable and deployable with a single YAML configuration file.
Outcome:	The proposed frameworks are based on a single YAML configuration file and can be easily extended to different domains or languages.

Clear Up Confusion: Iterative Differential Generation for Fine-grained Intent Detection with Contrastive Feedback (2025.coling-main)

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Feng Zhang, Wei Chen, Meng Gao, Fei Ding, Tengjiao Wang, Jiahui Yao, Jiabin Zheng

Sentiment Analysis

Challenge:	Recent studies on fine-grained intent detection have focused on collecting large-scale and high-quality samples via crowdsourcing resulting in data scarcity.
Approach:	They propose an iterative differential generation framework with contrastive feedback to generate high-quality pseudo samples and accurately capture the crucial nuances in target class distribution.
Outcome:	The proposed framework generates high-quality pseudo samples and captures crucial nuances in target class distribution.

Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains (2021.findings-acl)

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Yunzhi Yao, Shaohan Huang, Wenhui Wang, Li Dong, Furu Wei

Breaking the Ceiling: Exploring the Potential of Jailbreak Attacks through Expanding Strategy Space (2025.findings-acl)

Challenge:	Large pre-trained models suffer from domain shift and are not optimal for specific domains.
Approach:	They propose a general approach to developing small, fast and effective pretrained models for specific domains by adapting off-the-shelf general pretrained model and performing task-agnostic knowledge distillation in target domains.
Outcome:	The proposed approach achieves better performance over the BERT BASE model in domain-specific tasks while 3.3 smaller and 5.1 faster than the BRT BASE.

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Yao Huang, Yitong Sun, Shouwei Ruan, Yichi Zhang, Yinpeng Dong, Xingxing Wei

Robustness in NLP

Challenge:	Existing methods to exploit black-box jailbreaks fail to capture key attack patterns . a novel framework decomposes jailbreak strategies into essential components .
Approach:	They propose a framework that decomposes jailbreak strategies into essential components and develops genetic-based optimization with intention evaluation mechanisms.
Outcome:	The proposed framework achieves 90% success rate on Claude-3.5, where prior methods completely fail . it also surpasses specialized safeguard models in evaluation accuracy .

Flexora: Flexible Low-Rank Adaptation for Large Language Models (2025.acl-long)

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Chenxing Wei, Yao Shu, Ying Tiffany He, Fei Yu

iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation (2025.acl-demo)

Challenge:	Large language models (LLMs) have revolutionized artificial intelligence, but performance on specific tasks is limited by knowledge boundaries.
Approach:	They propose a method that automatically selects the most critical layers for fine-tuning to optimize performance across diverse downstream tasks.
Outcome:	The proposed method outperforms baseline models and natural language tasks.

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Zheyong Xie, Shaosheng Cao, Zuozhu Liu, Zheyu Ye, Zihan Niu, Chonggang Lu, Tong Xu, Enhong Chen, Zhe Xu, Yao Hu, Wei Lu

Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision) (2024.acl-tutorials)

Challenge:	Existing approaches to role-playing emotional companion products lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings.
Approach:	They propose a virtual pet agent that can enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences.
Outcome:	The proposed system has been deployed in a real-world, non-commercial product for 200 days and has demonstrated its effectiveness in practical applications.

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Yao Dou, Philippe Laban, Claire Gardent, Wei Xu

Improving Minimum Bayes Risk Decoding with Multi-Prompt (2024.emnlp-main)

Challenge:	In this tutorial, we focus on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria.
Approach:	This tutorial focuses on text-to-text generation, a class of natural language generation tasks that takes a piece of text as input and generates a revision that is improved according to some specific criteria.
Outcome:	This tutorial focuses on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and generates a revision that is improved according to some specificcriteria.

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David Heineman, Yao Dou, Wei Xu

PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models (2024.acl-long)

Challenge:	Existing methods to generate LLMs with a single ‘best’ prompt are unstable and sub-optimal in practice.
Approach:	They propose to decode multiple candidate generations from a prompt bank at inference-time and use Minimum Bayes Risk (MBR) to select a final output.
Outcome:	The proposed method improves MBR across a set of conditional generation tasks and models.

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Haoran Li, Dadi Guo, Donghao Li, Wei Fan, Qi Hu, Xin Liu, Chunkit Chan, Duanyi Yao, Yuan Yao, Yangqiu Song

Reducing Privacy Risks in Online Self-Disclosures with Language Models (2024.acl-long)

Challenge:	generative large language models (LLMs) exhibit surprising capability and integrate previous tasks into a unified text generation formulation.
Approach:	They propose a privacy evaluation benchmark to quantify the privacy leakage of language models.
Outcome:	The proposed benchmark compares PPLMs with different privacy implementations to find out how privacy leakage is handled.

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Yao Dou, Isadora Krsek, Tarek Naous, Anubha Kabra, Sauvik Das, Alan Ritter, Wei Xu

KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models (2024.acl-long)

Challenge:	Disclosure is a social media activity that can be rewarding but also poses privacy risks.
Approach:	They propose to detect and abstract online self-disclosures using a large corpus of 4.8K annotated disclosure spans and a language model to fine-tune for detection.
Outcome:	The proposed model can detect and abstract self-disclosures with 80% accuracy, on-par with GPT-3.5.

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Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Wei Ye, Jindong Wang, Xing Xie, Yue Zhang, Shikun Zhang

Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design (2026.eacl-long)

Challenge:	Existing methods to detect contaminated texts focus on quantifying contamination status instead of accurately gauging model performance.
Approach:	They propose a Knowledge-grounded Interactive Evaluation framework which incorporates an LLM-powered “interactor” role for the first time to accomplish a dynamic contamination-resilient evaluation.
Outcome:	The proposed framework is based on a question in a standard LLM benchmark and can be used to evaluate models in real-world conversations.

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Huaiyuan Yao, Wanpeng Xu, Justin Turnau, Nadia Kellam, Hua Wei

FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models (2024.emnlp-demo)

Challenge:	Existing AI-assisted educational tools focus on isolated tasks, but lack end-to-end workflows for instructional design.
Approach:	They propose a multi-agent large language model framework to automate end-to-end course material generation.
Outcome:	The proposed framework reduces development time and human workload while reducing human involvement.

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Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Zhengran Zeng, Wei Ye, Jindong Wang, Yue Zhang, Shikun Zhang

DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)

Challenge:	Large language models (LLMs) have revolutionized natural language processing with impressive performance across various tasks.
Approach:	They propose a framework for automated evaluations of large language models . they open-source their code at https://github.com/WisdomShell/FreeEval .
Outcome:	The framework is open-source and can be used to develop and validate new evaluation methods.

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Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan

CodeM: Less Data Yields More Versatility via Ability Matrix (2024.findings-acl)

Challenge:	Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP).
Approach:	They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses.
Outcome:	The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets.

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Daoguang Zan, Ailun Yu, Wei Liu, Bo Shen, Shaoxin Lin, Yongshun Gong, Yafen Yao, Yan Liu, Bei Guan, Weihua Luo, Yongji Wang, Qianxiang Wang, Lizhen Cui

Challenge:	Recent efforts to train code large language models have been booming recently . however, this will incur significant costs in constructing data and training model considering the countless downstream scenarios.
Approach:	They propose a data construction strategy which decouples code LLMs’ abilities into two dimensions and constructs a lightweight training corpus that only covers a subset of target scenarios.
Outcome:	The proposed model can train a multilingual multitasking model using less data and training data.

GPT-4 Jailbreaks Itself with Near-Perfect Success Using Self-Explanation (2024.emnlp-main)

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Govind Ramesh, Yao Dou, Wei Xu

NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries (2024.findings-eacl)

Challenge:	Using large language models to jailbreak is important for testing safety and security issues.
Approach:	They propose an approach that leverages the reflective capabilities of large language models for jailbreaking with only black-box access.
Outcome:	The proposed method achieves jailbreak success rates of 98% on GPT-4, 92% on GTP-4 Turbo, and 94% on Llama-3.1-70B in under 7 queries.

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Wei Zhao, Zhitao Hou, Siyuan Wu, Yan Gao, Haoyu Dong, Yao Wan, Hongyu Zhang, Yulei Sui, Haidong Zhang