Papers by Yang Ye

Jigsaw-Puzzles: From Seeing to Understanding to Reasoning in Vision-Language Models (2025.emnlp-main)

Challenge:	despite growing interest in NL2GQL, benchmarking progress has been constrained by the lack of resources that are simultaneously large-scale, cross-domain, and cross-dialect.
Approach:	They propose a framework that integrates NL2SQL-to-NL2GQL conversion with graph-native data generation.
Outcome:	The proposed framework supports execution-based evaluation on Cypher and ISO-GQL, covering hundreds of graph databases and over 20k natural language questions for each dialect.

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Zesen Lyu, Dandan Zhang, Wei Ye, Fangdi Li, Zhihang Jiang, Yao Yang

VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models (2024.emnlp-main)

Challenge:	Existing vision-language models lack spatial reasoning capability, despite their ability to comprehend spatial arrangements and model structural relations.
Approach:	They propose a benchmark to evaluate vision-language models' spatial perception, structural understanding, and reasoning capabilities by minimizing reliance on domain-specific knowledge.
Outcome:	The proposed benchmark is based on 1,100 carefully curated real-world images with high spatial complexity.

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Yifei Liu, Jicheng Wen, Yang Wang, Shengyu Ye, Li Lyna Zhang, Ting Cao, Cheng Li, Mao Yang

Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation (2021.acl-long)

Challenge:	Recent research has focused on pushing weight-only quantization to extremely low-bit due to numerical representation limitations.
Approach:	They propose a vector-based quantization approach that pushes LLMs to extremely low-bit . they propose scalar-based weight quantization that reduces memory requirements and optimizes storage costs .
Outcome:	The proposed method reduces model quantization perplexity by 0.01-0.34 on LLaMA-2, 0.38-0.68 on mistral-7B, 4.41-7.34, on llaMA-3 on QA tasks on average.

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Xingyi Yang, Muchao Ye, Quanzeng You, Fenglong Ma

HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference (2025.findings-acl)

Challenge:	Existing methods for medical image analysis use predefined template databases or ignore hierarchical nature of medical report generation.
Approach:	They propose a hierarchical retrieval mechanism to extract both report and sentence-level templates for clinically accurate report generation.
Outcome:	The proposed model extracts both report and sentence-level templates for clinically accurate report generation.

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Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, Cheng Li

Green & Sustainable NLP

Challenge:	Existing top-k attention methods struggle to strike a balance between efficiency and accuracy.
Approach:	They propose a top-k attention approach that integrates low-overhead techniques into the Top-k Attention process to achieve 7.2 speedup compared to vanilla full attention.
Outcome:	The proposed approach achieves 7.2 speedup compared to current top-k attention methods while maintaining model accuracy.

FinToolSyn: A forward synthesis Framework for Financial Tool-Use Dialogue Data with Dynamic Tool Retrieval (2026.findings-acl)

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Caishuang Huang, Yang Qiao, Rongyu Zhang, Junjie Ye, Pu Lu, null Wuwenxi, Meng Zhou, Xiku Du, Qi Zhang, Tao Gui, Xuanjing Huang

ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition (2024.acl-long)

Challenge:	Existing data synthesis methods rely on static tools to generate queries . this approach fails to capture the implicit, event-driven nature of real-world needs .
Approach:	They propose a forward synthesis framework to generate high-quality financial dialogues . they construct a repository of 43,066 tools and synthesize over 148k dialogue instances .
Outcome:	Experiments show that models trained on FinToolSyn achieve a 21.06% improvement . the framework is designed to generate high-quality financial dialogues .

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Lu Ye, Ze Tao, Yong Huang, Yang Li

HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System (2022.coling-1)

Challenge:	Experiments show that ChunkAttention can speed up the self-attention kernel by 3.2-4.8 compared to the start-of-the-art implementation.
Approach:	They propose a prefix-aware self-attention module that can detect matching prompt prefixes across multiple requests and share their key/value tensors in memory at runtime.
Outcome:	The proposed module can speed up the self-attention kernel by 3.2-4.8 compared to the start-of-the-art implementation, with the length of the system prompt ranging from 1024 to 4096.

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Zhanyu Ma, Jian Ye, Xurui Yang, Jianfeng Liu

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)

Challenge:	Existing methods to train task-oriented dialogue systems in monolingual datasets are expensive to build.
Approach:	They propose a hierarchical framework to classify intents in high-level and slot filling in low-level . they incorporate sentence-level alignment among different languages to enhance intent detection .
Outcome:	The proposed framework achieves the performance on a public task-oriented dialog dataset.

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Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang

Omni-Chart-600K: A Comprehensive Dataset of Chart Types for Chart Understanding (2025.findings-naacl)

Challenge:	Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified.
Approach:	They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .
Outcome:	Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .

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Shulei Wang, Shuai Yang, Wang Lin, Zirun Guo, Sihang Cai, Hai Huang, Ye Wang, Jingyuan Chen, Tao Jin

GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization (2024.emnlp-main)

Challenge:	Existing chart-related training methods lack capabilities in information extraction, mathematical reasoning, and understanding of multiple chart types.
Approach:	They propose a two-stage training strategy and method for jointly training a vision encoder tailored for multi-type charts to address the deficiencies in chart types and limited scope of chart tasks in existing datasets.
Outcome:	The proposed dataset includes 21 diverse chart types and tasks, including data retrieval and mathematical reasoning.

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Yangfan Ye, Xiachong Feng, Xiaocheng Feng, Weitao Ma, Libo Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin

Text Generation

Challenge:	Current studies focus on single-language or single-document tasks for news summarization . lack of a benchmark inhibits researchers from adequately studying this invaluable problem.
Approach:	They propose a novel task that unifies Multi-lingual, Cross-lingual and Multi-document Summarization into one task.
Outcome:	The proposed task encapsulates the real-world requirements all-in-one and is validated by extensive analysis.

Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models (2025.findings-emnlp)

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Haonan He, Yuchen Ren, Yining Tang, Ziyang Xu, Junxian Li, Minghao Yang, Di Zhang, Yuan Dong, Tao Chen, Shufei Zhang, Yuqiang Li, Nanqing Dong, Wanli Ouyang, Dongzhan Zhou, Peng Ye

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

Challenge:	Biology-Instructions is the first large-scale instruction-tuning dataset for multi-omics biological sequences.
Approach:	They propose a large-scale instruction-tuning dataset for multi-omics biological sequences . they propose 'chatMultiOmics' to overcome limitations of current LLMs on multi-ome tasks .
Outcome:	The proposed dataset bridges LLMs and complex biological sequence-related tasks while maintaining conversational fluency.

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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

Make-A-Voice: Revisiting Voice Large Language Models as Scalable Multilingual and Multitask Learners (2024.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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Rongjie Huang, Chunlei Zhang, Yongqi Wang, Dongchao Yang, Jinchuan Tian, Zhenhui Ye, Luping Liu, Zehan Wang, Ziyue Jiang, Xuankai Chang, Jiatong Shi, Chao Weng, Zhou Zhao, Dong Yu

ExplainaBoard: An Explainable Leaderboard for NLP (2021.acl-demo)

Challenge:	Large language models (LLMs) have been used for general-purpose interfaces across multiple tasks and languages.
Approach:	They propose to use large language models as a general-purpose interface across multiple tasks and languages.
Outcome:	The proposed model performs better on 200K hours of 6-language data for voice generation applications.

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Pengfei Liu, Jinlan Fu, Yang Xiao, Weizhe Yuan, Shuaichen Chang, Junqi Dai, Yixin Liu, Zihuiwen Ye, Graham Neubig

GeAR: Graph-enhanced Agent for Retrieval-augmented Generation (2025.findings-acl)

Challenge:	Using leaderboards, researchers can track the performance of various systems on various NLP tasks.
Approach:	They propose a new conceptualization and implementation of NLP evaluation using a leaderboard.
Outcome:	The ExplainaBoard is an evaluation tool for natural language processing (NLP) it covers more than 400 systems, 50 datasets, 40 languages, and 12 tasks.

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Zhili Shen, Chenxin Diao, Pavlos Vougiouklis, Pascual Merita, Shriram Piramanayagam, Enting Chen, Damien Graux, Andre Melo, Ruofei Lai, Zeren Jiang, Zhongyang Li, Ye Qi, Yang Ren, Dandan Tu, Jeff Z. Pan

NOVA-63: Native Omni-lingual Versatile Assessments of 63 Disciplines (2025.emnlp-main)

Challenge:	Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers struggle with multi-hop retrieval scenarios.
Approach:	They propose a graph expansion mechanism that augments any conventional base retriever and an agent framework that incorporates the resulting graph-based retrieval into a multi-step retrieval framework.
Outcome:	The proposed system achieves state-of-the-art results on three multi-hop question answering datasets while consuming fewer tokens and requiring fewer iterations than existing multi-step retrieval systems.

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Jinyang Zhang, Kexin Yang, Yu Wan, Muyang Ye, Baosong Yang, Fei Huang, Junyang Lin, Dayiheng Liu

Multilinguality

Challenge:	Existing multilingual benchmarks show severe drawbacks, such as overly translated content, the absence of difficulty control, and disciplinary imbalance, making the benchmarking process unreliable and showing low convincingness.
Approach:	They propose a multilingual benchmark that integrates LLM-assisted formatting, expert quality verification, and multi-level difficulty screening to provide a comprehensive, difficult multilingual assessment.
Outcome:	The proposed benchmark features 93,536 questions sourced from native speakers across 14 languages and 63 academic disciplines.

Unearthing Gems from Stones: Policy Optimization with Negative Sample Augmentation for LLM Reasoning (2025.findings-emnlp)

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Zhaohui Yang, Yuxiao Ye, Shilei Jiang, Shihong Deng, Chen Hu, Linjing Li, Daxin Jiang

Learning a Multi-Domain Curriculum for Neural Machine Translation (2020.acl-main)

Challenge:	Recent advances in reasoning language models have witnessed a paradigm shift from short to long CoT pattern.
Approach:	They propose a behavior-constrained policy gradient with negative sample augmented (BCPG-NSA) negative steps are valuable components in long CoT models, authors argue .
Outcome:	The proposed framework outperforms baselines on math/coding reasoning benchmarks using the same training dataset.

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Wei Wang, Ye Tian, Jiquan Ngiam, Yinfei Yang, Isaac Caswell, Zarana Parekh

Multilinguality

Challenge:	Existing data selection methods do not work well for multiple domains . multiple aspects need to be considered for training a multi-domain model .
Approach:	They propose a dynamic data selection method to multi-domain NMT that incorporates instance-level domain-relevance features and a curriculum to gradually focus on multi- domain relevant data batches.
Outcome:	The proposed model outperforms no-curriculum training on multiple domains and reaches or outperformed individual performance.

Visual Evidence Prompting Mitigates Hallucinations in Large Vision-Language Models (2025.acl-long)

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Wei Li, Zhen Huang, Houqiang Li, Le Lu, Yang Lu, Xinmei Tian, Xu Shen, Jieping Ye

MAssistant: A Personal Knowledge Assistant for MOOC Learners (D19-3)

Challenge:	LVLMs have shown impressive progress by integrating visual perception with linguistic understanding to produce contextually grounded outputs.
Approach:	They propose a visual evidence prompting method to mitigate hallucinations in large vision-language models by using small visual models to complement them.
Outcome:	The proposed method reduces hallucinations by reducing false activation and enhancing correct ones.

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Lan Jiang, Shuhan Hu, Mingyu Huang, Zhichun Wang, Jinjian Yang, Xiaoju Ye, Wei Zheng

AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)

Challenge:	Massive Open Online Courses (MOOCs) have experienced a rapid development since 2012 . many MOOC platforms have been launched, including Coursera1 , edX2 , and Udacity3 etc.
Approach:	They present a personal knowledge assistant system called MAssistant for MOOC learners . MAsistants has a large-scale concept graph built from open data . it also provides a browser extension which interacts with users during video lectures .
Outcome:	The proposed system helps users trace the concepts they have learned in MOOCs, and to build their own concept graphs.

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Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei

Linguistics & Cognitive NLP

Challenge:	Large language models are increasingly employed to empower autonomous agents to simulate human behavior.
Approach:	They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts.
Outcome:	The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning.

A Hierarchical N-Gram Framework for Zero-Shot Link Prediction (2022.findings-emnlp)

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Mingchen Li, Junfan Chen, Samuel Mensah, Nikolaos Aletras, Xiulong Yang, Yang Ye

Multi-Agent Collaboration via Cross-Team Orchestration (2025.findings-acl)

Challenge:	Existing approaches to zero-shot link prediction use textual features of relations as auxiliary information to improve the encoded representation.
Approach:	They propose a Hierarchical N-gram framework for Zero-Shot Link Prediction that leverages character n-gram information for ZSLP.
Outcome:	The proposed method achieves state-of-the-art on two standard ZSLP datasets.

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Zhuoyun Du, Chen Qian, Wei Liu, Zihao Xie, YiFei Wang, Rennai Qiu, Yufan Dang, Weize Chen, Cheng Yang, Ye Tian, Xuantang Xiong, Lei Han

Challenge:	Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents.
Approach:	They propose a framework that enables orchestrated teams to jointly propose various task-oriented solutions and interact with their insights in a self-independence while cross-team collaboration environment for superior solutions generation.
Outcome:	Experiments show that the framework can generate better software quality compared to state-of-the-art frameworks.

Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models (2026.findings-acl)

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Teng Wang, Jiang Zhangyi, Zhenqi He, Hailei Gong, Shenyang Tong, Wenhan Yang, Zeyu Li, Yanan Zheng, Zifan He, Zewen Ye, Shengjie Ma, Jianping Zhang

Video-LLaVA: Learning United Visual Representation by Alignment Before Projection (2024.emnlp-main)

Challenge:	Existing Process Reward Models (PRMs) are vulnerable to reward hacking and require expensive, large-scale annotation of reasoning steps.
Approach:	They propose a reward model approach which evaluates both individual and consecutive reasoning steps from fine-grained and coarse-grounded level.
Outcome:	Empirical results show that the proposed model performs better than existing PRMs and is more robust than existing models.

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Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, Li Yuan

Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)

Challenge:	Existing approaches to visual-language understanding lack unified tokenization for images and videos . lack of unified visual representations makes it difficult to learn multi-modal interactions from poor projection layers.
Approach:	They propose to unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM.
Outcome:	The proposed model outperforms Video-ChatGPT on image benchmarks and on 9 image benchmark benchmarks.

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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li

Challenge:	Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences.
Approach:	They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks.
Outcome:	The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge.

AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)

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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang

DESED: Dialogue-based Explanation for Sentence-level Event Detection (2022.coling-1)

Challenge:	Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning.
Approach:	AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks.
Outcome:	Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% .

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Yinyi Wei, Shuaipeng Liu, Jianwei Lv, Xiangyu Xi, Hailei Yan, Wei Ye, Tong Mo, Fan Yang, Guanglu Wan

CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention (2025.acl-long)

Challenge:	Existing methods for sentence-level event detection depend on manual annotations or domain expertise to design sophisticated templates and rules.
Approach:	They propose a dialogue-based explanation paradigm to enhance sentence semantics for event detection.
Outcome:	The proposed method can be applied to two event detection datasets.

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Zekai Ye, Qiming Li, Xiaocheng Feng, Libo Qin, Yichong Huang, Baohang Li, Kui Jiang, Yang Xiang, Zhirui Zhang, Yunfei Lu, Duyu Tang, Dandan Tu, Bing Qin

Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization (2024.findings-emnlp)

Challenge:	Large Vision-Language Models (LVLMs) have impressive multimodal abilities but remain prone to multilingual object hallucination.
Approach:	They propose a cross-lingual attention intervention method to mitigate multilingual object hallucination in LVLMs by aligning attention patterns.
Outcome:	The proposed method improves 13.56% (up to 30%) on the POPE and 21.75% on the hallucination subsets across languages.

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Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia, Zi Ye, Xinyu Duan, Zhefeng Wang, Kehai Chen, Min Zhang

Knowledge Graph Unlearning with Schema (2025.coling-main)

Challenge:	Large language models require a balance between efficiency and performance.
Approach:	They propose a low-rank compression technique that reduces non-essential parameters by decomposing weight matrices into products of two low-ranked matrici.
Outcome:	The proposed method outperforms existing pruning and low-rank compression techniques in maintaining model performance at the same compression ratio.

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Yang Xiao, Ruimeng Ye, Bo Hui

Structured Data in NLP

Challenge:	Unlearning on knowledge graphs has not been extensively studied.
Approach:	They propose a new unlearning method based on schema for knowledge graph (KG) they update the representation of the deleted element’s neighborhood with an unlearning object that regulates the affinity between the affected neighborhood and the instances within the same schema.
Outcome:	The proposed method is evaluated on various KG embedding models with benchmark datasets.

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

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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

MisinfoBench: A Multi-Dimensional Benchmark for Evaluating LLMs’ Resilience to Misinformation (2025.findings-emnlp)

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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Ye Yang, Donghe Li, Zuchen Li, Fengyuan Li, Jingyi Liu, Li Sun, Qingyu Yang

A Bounding Box is Worth One Token - Interleaving Layout and Text in a Large Language Model for Document Understanding (2025.findings-acl)

Challenge:	Existing benchmarks assess factual accuracy in isolated queries but fail to evaluate LLMs’ resilience to misinformation in interactive settings.
Approach:	MisinfoBench is a benchmark designed to assess LLMs’ ability to discern, resist, and reject misinformation.
Outcome:	MisinfoBench assesses large language models’ ability to discern, resist, and reject misinformation in interactive settings.

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Jinghui Lu, Haiyang Yu, Yanjie Wang, Yongjie Ye, Jingqun Tang, Ziwei Yang, Binghong Wu, Qi Liu, Hao Feng, Han Wang, Hao Liu, Can Huang

Social Welfare Function Leaderboard: On the Emergence of LLM Agents as the Welfare Dictator (2026.findings-acl)

Challenge:	Existing methods for integrating spatial layouts with text have limitations . existing methods produce overly long text sequences or lack autoregressive traits of LLMs .
Approach:	They introduce Interleaving Layout and Text in a Large Language Model (LayTextLLM) they use OCR-derived text and spatial layouts to integrate with LLMs for document understanding .
Outcome:	The proposed model shows an increase in performance in KIE and VQA tasks.

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Zhengliang Shi, Ruotian Ma, Jen-tse Huang, Xinbei Ma, Xingyu Chen, Mengru Wang, Qu Yang, Yue Wang, Fanghua Ye, Ziyang Chen, Shanyi Wang, Cixing LI, Wenxuan Wang, Zhaopeng Tu, Xiaolong Li, Zhaochun Ren, Liefeng Bo

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

Challenge:	Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare.
Approach:	They evaluate 20 state-of-the-art Large language models (LLMs) and 20 LLM dictators to create a social welfare function benchmark.
Outcome:	The proposed model creates dilemma between maximizing collective efficiency and ensuring distributive fairness.

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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

CPsyExam: A Chinese Benchmark for Evaluating Psychology using Examinations (2025.coling-main)

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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Jiahao Zhao, Jingwei Zhu, Minghuan Tan, Min Yang, Renhao Li, Yang Di, Chenhao Zhang, Guancheng Ye, Chengming Li, Xiping Hu, Derek F. Wong

Challenge:	CPsyExam prioritizes psychological knowledge and case analysis separately, recognizing the significance of applying psychological knowledge to real-world scenarios.
Approach:	They propose a psychological benchmark, CPsyExam, constructed from questions from Chinese examination systems.
Outcome:	The proposed benchmark prioritizes psychological knowledge and case analysis separately, recognizing the significance of applying psychological knowledge to real-world scenarios.

AISFG: Abundant Information Slot Filling Generator (2022.naacl-main)

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Yang Yan, Junda Ye, Zhongbao Zhang, Liwen Wang

Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition (2025.coling-main)

Challenge:	Existing approaches to zero/few-shot slot filling focus on slot descriptions and examples . AISFG model is based on domain-specific labels, which is not capable of transferring to new domains with little or no data.
Approach:	They propose a model with a query template that incorporates domain descriptions, slot descriptions, and examples with context.
Outcome:	Experimental results show that the proposed model outperforms state-of-the-art approaches in zero/few-shot slot filling task.

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Yuming Yang, Wantong Zhao, Caishuang Huang, Junjie Ye, Xiao Wang, Huiyuan Zheng, Yang Nan, Yuran Wang, Xueying Xu, Kaixin Huang, Yunke Zhang, Tao Gui, Qi Zhang, Xuanjing Huang

Knowledge Image Matters: Improving Knowledge-Based Visual Reasoning with Multi-Image Large Language Models (2025.acl-long)

Challenge:	Current Large Language Models struggle with complex entity taxonomies in open domains and lack NER capabilities.
Approach:	They propose a dataset to guide LLMs' generalization in Open NER under a universal entity taxonomy.
Outcome:	The proposed model outperforms GPT-4 in 3 out-of-domain benchmarks across 15 datasets and 6 languages.

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Guanghui Ye, Huan Zhao, Zhixue Zhao, Xupeng Zha, Yang Liu, Zhihua Jiang

Too Good to be Bad: On the Failure of LLMs to Role-Play Villains (2026.findings-acl)

Challenge:	Knowledge-based visual reasoning (KB-VR) is a challenging task, as it requires machines not only to understand concepts and relationships of visual scenes, but also to associate them with external world knowledge to perform chain of reasoning on open-world questions.
Approach:	They propose a visual knowledge card (VKC) that integrates internal visual knowledge and external world knowledge produced by a knowledge generator into an image.
Outcome:	The proposed model achieves new state-of-the-art results compared to previous top-performing models on three popular KB-VR benchmarks.

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Zihao Yi, Qingxuan Jiang, Ruotian Ma, Xingyu Chen, Qu Yang, Mengru Wang, Fanghua Ye, Ying Shen, Zhaopeng Tu, Xiaolong Li, Liefeng Bo

Challenge:	Large Language Models (LLMs) are increasingly tasked with creative generation, but their ability to portray non-prosocial, antagonistic personas remains largely unexamined.
Approach:	They propose a moral alignment benchmark to test the safety of large language models . they find that models struggle with traits directly antithetical to safety principles .
Outcome:	The proposed model fails to accurately portray morally ambiguous or villainous characters . the model fails most with traits directly antithetical to safety principles .

RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis (2025.findings-acl)

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Pengzuo Wu, Yuhang Yang, Guangcheng Zhu, Chao Ye, Hong Gu, Xu Lu, Ruixuan Xiao, Bowen Bao, Yijing He, Liangyu Zha, Wentao Ye, Junbo Zhao, Haobo Wang

Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric (2025.acl-long)

Challenge:	Existing benchmarks for large language models focus on simple, flat table structures.
Approach:	They propose a benchmark to evaluate the performance of both Large Language Models and Multimodal LLMs across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG.
Outcome:	The proposed benchmark evaluates the performance of LLMs and Multimodal LLM models across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG.

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Yuming Yang, Yang Nan, Junjie Ye, Shihan Dou, Xiao Wang, Shuo Li, Huijie Lv, Tao Gui, Qi Zhang, Xuanjing Huang

ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)

Challenge:	Existing studies have explored various diversity-aware data selection methods to construct high-quality datasets and enhance model performance.
Approach:	They propose to use data diversity to measure instruction tuning of large language models.
Outcome:	The proposed diversity metric outperforms existing methods on simulated and real-world data and shows that it captures diversity variations and achieves a 0.97 correlation with instruction tuning.

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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong

Tuning Less, Prompting More: In-Context Preference Learning Pipeline for Natural Language Transformation (2025.emnlp-main)

Challenge:	Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs).
Approach:	They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models.
Outcome:	The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks.

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Shuyun Yang, Yan Zhang, Zhengmao Ye, Lei Duan, Mingjie Tang

MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning (2026.findings-acl)

Challenge:	Existing approaches to natural language transformation (NLT) tasks face significant challenges, such as the computational costs of leveraging large pre-trained models and the limited generalization ability of fine-tuned smaller models.
Approach:	They propose a framework that combines prompting with fine-tuning to enhance smaller models by integrating In-Context Examples from retrieval.
Outcome:	The proposed framework outperforms existing methods across MT and TST tasks.

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Juexiang Ye, Xue Li, Yang Xinyu, Chengkai Huang, Lanshun Nie, Lina Yao, Dechen Zhan

Information Retrieval

Challenge:	Existing methods rely on unstructured retrieval or coarse abstractions, which lead to temporal conflicts, brittle reasoning, and limited traceability.
Approach:	They propose a unified memory framework that consolidates long-term agent experiences into three interconnected components that combine structured knowledge and evidence to construct compact yet information-dense contexts for reasoning.
Outcome:	The proposed framework significantly improves multi-hop and temporal reasoning accuracy while reducing input context length by over 95% compared to long-context baselines.

News2vec: News Network Embedding with Subnode Information (D19-1)

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Ye Ma, Lu Zong, Yikang Yang, Jionglong Su

Representation Learning

Challenge:	Existing approaches to embed news as vectors do not integrate features and inter-textual knowledge of news.
Approach:	They propose a model that integrates news features and inter-textual knowledge into a dense vector representation.
Outcome:	The proposed model can be used to represent news as a dense vector . it is compared with existing models on stock movement prediction and news recommendation tasks .

LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay (2024.emnlp-main)

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Yihuai Lan, Zhiqiang Hu, Lei Wang, Yang Wang, Deheng Ye, Peilin Zhao, Ee-Peng Lim, Hui Xiong, Hao Wang

Challenge:	Existing studies on LLM agents' social behaviors are lacking . previous studies focused on positive social behaviors, leaving research on negative social behaviors relatively scarce.
Approach:	They propose a framework that features a multi-agent system facilitating efficient communication and interaction with LLM agents.
Outcome:	The proposed framework is based on Avalon and evaluates on game success and analyzes agents’ social behaviors.

Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)

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Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang

Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception (2026.acl-long)

Challenge:	Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications.
Approach:	They propose a reliable strategy for domains to choose more robust LLMs for real-world applications.
Outcome:	The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications.

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Zhen Wan, Chao-Han Huck Yang, Jinchuan Tian, Hanrong Ye, Ankita Pasad, Szu-Wei Fu, Arushi Goel, Ryo Hachiuma, Shizhe Diao, Kunal Dhawan, Sreyan Ghosh, Yusuke Hirota, Zhehuai Chen, Rafael Valle, Chenhui Chu, Shinji Watanabe, Boris Ginsburg, Yu-Chiang Frank Wang

Bloom-Eval: A Hierarchical Evaluation Benchmark for Automatic Survey Generation Based on Bloom’s Taxonomy (2026.acl-long)

Challenge:	naively fine-tuning an omni-model on speech recognition and external sound understanding tasks often degrades performance . Xie and Wu's framework, Speech-Hands, recasts the problem as an explicit self-reflection decision.
Approach:	They propose a voice-agentic framework that learns one critical omni-understanding skill: trusting itself versus external audio perception.
Outcome:	The proposed framework outperforms baseline models on the OpenASR leaderboard by 12.1% WER and high F1 on audio QA decisions.

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Fei Zhang, Zhe Zhao, HaiBin Wen, Tianshuo Wei, Zaixi Zhang, Chao Yang, Ye Wei

Text Generation

Challenge:	Existing evaluation methods suffer from cognitive dimensional simplification and methodological unreliability due to the ”LLM-as-a-Judge” approach.
Approach:	They propose a six-tiered benchmark that evaluates ASG systems by prioritizing deterministic algorithms and introducing a GRADE approach for abstract abilities.
Outcome:	The proposed method provides the ASG field with a systematic, reproducible, and theoretically grounded benchmark to guide future research.

Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction (2024.findings-acl)

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Dingyao Yu, Yang An, Wei Ye, Xiongfeng Xiao, Shaoguang Mao, Tao Ge, Shikun Zhang

Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models (2026.findings-acl)

Challenge:	Chinese Spelling Correction (CSC) lacks large-scale high-quality corpora due to labor-intensive labeling of spelling errors in real-life writing or typing scenarios.
Approach:	They propose to use OCR/ASR-based generation to refine Chinese Spelling Correction models on random replacement-based corpora and filter them based on prediction confidence.
Outcome:	The proposed model outperforms existing models on three widely-used benchmarks while significantly alleviating over-correction.

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Bang Zhang, Ruotian Ma, Qingxuan Jiang, Peisong Wang, Jiaqi Chen, Zheng Xie, Xingyu Chen, Yue Wang, Fanghua Ye, Jian Li, Yifan Yang, Zhaopeng Tu, Xiaolong Li

Boosting Policy and Process Reward Models with Monte Carlo Tree Search in Open-Domain QA (2025.findings-acl)

Challenge:	Large language models (LLMs) have evolved from statistical sequence predictors to sophisticated autonomous agents capable of reasoning, planning, and sustaining multi-turn conversa-tions.
Approach:	They propose a system that instantiates a "Sentient Agent" that simulates human-like emotional changes and inner thoughts to provide a more realistic evaluation of the model in multi-turn conversations.
Outcome:	The proposed framework measures the agent's higher-order social cognition in multi-turn conversations.

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Chi-Min Chan, Chunpu Xu, Junqi Zhu, Jiaming Ji, Donghai Hong, Pengcheng Wen, Chunyang Jiang, Zhen Ye, Yaodong Yang, Wei Xue, Sirui Han, Yike Guo

Question Answering

Challenge:	Experimental results show that our approach can effectively improve the performance of both the policy model and the reward model.
Approach:	They propose to use Monte Carlo Tree Search for both policy model improvement and reward model improvement to bridge it to more subtle open-domain question answering.
Outcome:	The proposed approach surpasses existing methods for annotation and training data with fewer data points and achieves better performance in test-time scaling strategies.

LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing (2026.findings-acl)

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Hao Li, Yiqun Zhang, Zhaoyan Guo, Chenxu Wang, Shengji Tang, Qiaosheng Zhang, Yang Chen, Biqing Qi, Peng Ye, Lei Bai, Zhen Wang, Shuyue Hu

Challenge:	Large language model (LLM) routing assigns each query to the best suitable model from an ensemble.
Approach:	They introduce a large-scale benchmark and unified framework for LLM routing . they find that many routing methods exhibit similar performance under unified evaluation .
Outcome:	The proposed benchmark provides comprehensive metrics for both performance-oriented and performance-cost trade-off routing.

CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling (2024.findings-acl)

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Chenhao Zhang, Renhao Li, Minghuan Tan, Min Yang, Jingwei Zhu, Di Yang, Jiahao Zhao, Guancheng Ye, Chengming Li, Xiping Hu

Nature-Inspired Population-Based Evolution of Large Language Models (2026.acl-long)

Challenge:	Existing datasets lack consulting knowledge, resulting in LLMs lacking professional consulting competence.
Approach:	They propose a report-based multi-turn dialogue reconstruction framework for Chinese psychological counseling that uses large language models to assist counseling.
Outcome:	The proposed framework is open-source and can be used in future research.

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Yiqun Zhang, Peng Ye, Xiaocui Yang, Shi Feng, Shufei Zhang, Lei Bai, Wanli Ouyang, Shuyue Hu

Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)

Challenge:	a new framework for population-based evolution of large language models is emerging . a population-driven evolution of LLMs is a key component of evolution, authors say .
Approach:	They propose a framework that allows for population-based evolution of large language models . they start with a population of parent LLMs and allow this population to evolve .
Outcome:	The proposed framework outperforms existing methods on 12 datasets.

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Wei Zhao, Jianbo Ye, Min Yang, Zeyang Lei, Suofei Zhang, Zhou Zhao

Everyone is unique: Towards Behaviorally Heterogeneous Negotiation Dialogue Systems for Debt Collection (2026.acl-long)

Challenge:	Earlier efforts in text modeling have achieved limited success on word meanings . convolutional neural networks (CNNs) are used to model higher level concepts and facts in texts .
Approach:	They propose three strategies to stabilize dynamic routing process to alleviate disturbance of noise capsules.
Outcome:	The proposed methods achieve state-of-the-art on 4 out of 6 datasets . they show that capsule networks exhibit significant improvement over baseline methods .

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Yuhang Yang, Kai Tang, Chao Ye, Haobo Wang, Qiqi Luo, Jin Guang Zheng, Zhixin Zhang

SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)

Challenge:	Existing models that assume users to be static, rational agents with fixed preferences fail to capture rich behavioral heterogeneity in real-world debt collection scenarios.
Approach:	They propose a public persona-enriched debt collection benchmark that highlights behavioral heterogeneity in negotiation.
Outcome:	The proposed benchmark outperforms existing models in realistic scenarios using 16 state-of-the-art LLMs.

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Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang

DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes (2025.findings-emnlp)

Challenge:	Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases.
Approach:	They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs.
Outcome:	The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs.

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Zhaowei Wang, Hongming Zhang, Tianqing Fang, Ye Tian, Yue Yang, Kaixin Ma, Xiaoman Pan, Yangqiu Song, Dong Yu

Transforming Visual Scene Graphs to Image Captions (2023.acl-long)

Challenge:	Large Vision-Language Models (LVLMs) have achieved significant progress in tasks like visual question answering and document understanding.
Approach:	They introduce DivScene, a large-scale dataset with 4,614 houses across 81 scene types and 5,707 kinds of target objects.
Outcome:	The proposed dataset provides a much greater diversity of target objects and scene types than existing datasets, enabling a comprehensive task evaluation.

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Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu, Qinghao Ye, Chenliang Li, Songfang Huang, Fei Huang, Zhangzikang Li, Yu Zhang

HSCNN: A Hybrid-Siamese Convolutional Neural Network for Extremely Imbalanced Multi-label Text Classification (2020.emnlp-main)

Challenge:	Existing approaches to generate captions using image captioning are based on multi-head attention (MHA)
Approach:	They propose to transform scene graphs into more descriptive captions by using multi-head attention to build a Graph Neural Network (GNN) . they construct a Mixture-of-Expert (MOE)-based decoder where each expert is built on MHA for discriminating the graph embeddings to generate different kinds of words.
Outcome:	The proposed framework can generate captions from multiple visual features and objects . it is based on a mixture-of-expert (MOE)-based decoder based upon MHA .

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Wenshuo Yang, Jiyi Li, Fumiyo Fukumoto, Yanming Ye

Text Classification

Challenge:	Existing approaches to solve the data imbalance problem are limited in extremely imbalanced data.
Approach:	They propose a hybrid approach which adapts general networks for head categories and few-shot techniques for tail categories.
Outcome:	The proposed approach improves the performance of Single networks with diverse loss objectives on tail or entire categories.

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

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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

Robustness in NLP

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.

Multi-Hop Transformer for Document-Level Machine Translation (2021.naacl-main)

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Long Zhang, Tong Zhang, Haibo Zhang, Baosong Yang, Wei Ye, Shikun Zhang

Machine Translation

Challenge:	Existing approaches to document-level neural machine translation (NMT) simply introduce the representations of context sentences without explicitly characterizing the inter-sentence reasoning process.
Approach:	They propose a novel multi-hop Transformer which explicitly models the human-like draft-editing and reasoning process by attending to multiple antecedent sentences iteratively.
Outcome:	Experiments on four widely used document translation tasks show that the proposed model significantly improves document-level translation performance and tackles discourse phenomena such as coreference error and the problem of polysemy.

FluentSpeech: Stutter-Oriented Automatic Speech Editing with Context-Aware Diffusion Models (2023.findings-acl)

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Ziyue Jiang, Qian Yang, Jialong Zuo, Zhenhui Ye, Rongjie Huang, Yi Ren, Zhou Zhao

V-Oracle: Making Progressive Reasoning in Deciphering Oracle Bones for You and Me (2025.acl-long)

Challenge:	Existing methods for speech editing still suffer from over-smoothing problem and lack of robustness due to stutter.
Approach:	They propose a stutter-oriented automatic speech editing model that incorporates sutter information into the hidden sequence.
Outcome:	The proposed model achieves state-of-the-art performance on a speech recording dataset . it can improve fluency of stuttering speech in terms of objective and subjective metrics.

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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Jiapeng Wang, YiFan Zhang, Zhuoma GongQue, Chong Sun, Yida Xu, Yadong Xue, Ye Tian, Zhimin Bao, Lan Yang, Chen Li, Honggang Zhang

Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning (2025.findings-acl)

Challenge:	Deciphering oracle bone scripts using AI technology is not an overnight task due to the evolution of written language over millennia.
Approach:	They propose a framework that utilizes Large Multi-modal Models (LMMs) for interpreting Oracle Bone Script (OBS).
Outcome:	The proposed framework provides quantitative analyses and superior deciphering capability.

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Ziang Ye, Zhenru Zhang, Yang Zhang, Jianxin Ma, Junyang Lin, Fuli Feng

SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning (2024.acl-long)

Challenge:	Existing approaches to enhance agent capabilities for Large Language Models treat all tokens equally . however, reasoning tokens versus boilerplate tokens differ in importance and learning complexity . recent research has focused on enhancing agent capabilities in large language models .
Approach:	They propose a Shuffle-Aware Discriminator (SHAD) for adaptive token discrimination . they propose SHAD method which adaptively emphasizes reasoning tokens during fine-tuning .
Outcome:	The proposed method improves performance over standard fine-tuning methods.

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Guoxin Chen, Kexin Tang, Chao Yang, Fuying Ye, Yu Qiao, Yiming Qian

Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing (2026.findings-acl)

Challenge:	Existing methods focus on single-step reasoning, ignoring logical dependencies between steps.
Approach:	They propose a method that maximizes a structure-based return to facilitate structured reasoning and explanation.
Outcome:	The proposed method outperforms state-of-the-art methods on EntailmentBank and STREET benchmarks.

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Chunyu Ye, Yunhao Zhang, Jingyuan Sun, Chong Li, Yang Zhao, Shaonan Wang

CURE: Critique-Driven Unified Reinforcement Learning for Test-Time Self-Improvement (2026.acl-long)

Challenge:	Current approaches to decoding language from the human brain rely on unimodal representations, neglecting the brain’s inherently multimodal processing.
Approach:	They propose a framework that leverages Multimodal Large Language Models to align brain signals with a shared semantic space encompassing text, images, and audio.
Outcome:	The proposed framework achieves an 8.48% improvement on the most commonly used benchmark on fMRI datasets with textual, visual, and auditory stimuli.

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Guirong Chen, Shuqi Ye, Wenkai Yang, Shiqi Shen, Guangyao Shen, Yankai Lin

Challenge:	Existing critique-guided methods fail to equip models with the autonomous improvement capabilities required for test-time scaling.
Approach:	They propose a framework that jointly optimizes a single policy for standard solving, critiquing, and guided re-exploration.
Outcome:	The proposed framework maintains competitive single-turn performance and unlocks effective inference-time scaling.

MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts (2022.emnlp-main)

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Xiangyu Xi, Jianwei Lv, Shuaipeng Liu, Wei Ye, Fan Yang, Guanglu Wan

A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)

Challenge:	Recent efforts to classify unstructured texts into specific types have been limited in practical scenarios.
Approach:	They propose to use Chinese text conversations and phone conversations to expand event detection to the scenarios involving informal and heterogeneous texts.
Outcome:	The proposed dataset is based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service.

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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo

Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data (2025.findings-acl)

Challenge:	Existing research on reinforcement learning for LLMs under data scarcity has not been unified.
Approach:	They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric.
Outcome:	The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area.

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Xin-Cheng Wen, Yijun Yang, Cuiyun Gao, Yang Xiao, Deheng Ye

DiMo-GUI: Advancing Test-time Scaling in GUI Grounding via Modality-Aware Visual Reasoning (2025.emnlp-main)

Challenge:	Large language models (LLMs) are capable of detecting software vulnerabilities, but lack of reasoning data hinders their ability to capture underlying vulnerability patterns.
Approach:	They propose a framework that excels at mining vulnerability patterns through reasoning data synthesizing and vulnerability-specific preference optimization.
Outcome:	The proposed framework improves on SVEN and PrimeVul datasets and improves 12.24%-22.77% accuracy.

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Hang Wu, Hongkai Chen, Yujun Cai, Chang Liu, Qingwen Ye, Ming-Hsuan Yang, Yiwei Wang

Contextual Domain Classification with Temporal Representations (2021.naacl-industry)

Challenge:	DiMo-GUI is a training-free framework for GUI grounding that splits input into textual elements and iconic elements, allowing the model to reason over each modality independently using general-purpose vision-language models.
Approach:	They propose a training-free framework for GUI grounding that leverages two core strategies: dynamic visual grounding and modality-aware optimization.
Outcome:	The proposed framework splits the input into textual elements and iconic elements, allowing the model to reason over each modality independently using general-purpose vision-language models.

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Tzu-Hsiang Lin, Yipeng Shi, Chentao Ye, Yang Fan, Weitong Ruan, Emre Barut, Wael Hamza, Chengwei Su

Information Retrieval

Challenge:	Existing studies that incorporate context in SLU have focused on domains where context is limited to a few minutes.
Approach:	They propose temporal representations that combine wall-clock second difference and turn order offset information to utilize both recent and distant context in a novel large-scale setup.
Outcome:	The proposed model reduces 13.04% of classification errors compared to baseline . previous studies have focused on domains where context is limited to a few minutes .

PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer (2024.findings-emnlp)

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Huashan Sun, Yixiao Wu, Yizhe Yang, Yinghao Li, Jiawei Li, Yuhao Ye, Yang Gao

Text Generation

Challenge:	Existing methods to transfer text style focus on sentence-level data, limiting performance . current LLMs struggle to generate public speaking texts that align with human preferences .
Approach:	They propose a task to transform official texts into public-speaking styles by analyzing real-world data.
Outcome:	The proposed task aims to transform public speaking texts into public-speaking styles . the proposed framework analyzes characteristics and identifies problems of stylized texts .

ICA-Proto: Iterative Cross Alignment Prototypical Network for Incremental Few-Shot Relation Classification (2023.findings-eacl)

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Wangjie Jiang, Zhihao Ye, Bang Liu, Ruihui Zhao, Jianguang Zheng, Mengyao Li, Zhiyong Li, Yujiu Yang, Yefeng Zheng

MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction (2024.emnlp-main)

Challenge:	Existing methods for relation classification suffer from the scarcity of manually annotated data.
Approach:	They propose a novel relation classification model that incorporates query representation into the encoding of novel prototypes and utilizes iteratively to achieve more interaction.
Outcome:	The proposed model outperforms the state-of-the-art model on two benchmark datasets.

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Qiao Sun, Liujia Yang, Minghao Ma, Nanyang Ye, Qinying Gu

Sentiment Analysis

Challenge:	Existing approaches within the pretraining-finetuning paradigm tend to meticulously craft complex tagging schemes and classification heads, or incorporate external semantic enhancements to enhance performance.
Approach:	They propose to integrate a minimalist tagging scheme and a novel token-level contrastive learning strategy to improve pretrained representations.
Outcome:	The proposed framework achieves comparable or superior performance compared to state-of-the-art techniques while featuring a more compact design and reduced computational overhead.

WebSynthesis: World Model-Guided Monte Carlo Tree Search for Efficient WebAgent Trajectory Synthesis (2026.acl-long)

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Yifei Gao, Junhong Ye, Yifan Yang, Jiaqi Wang, Yi Zhang, Zhang Ruichen, Jitao Sang

Green & Sustainable NLP

Challenge:	Recent advances in large language models have enabled increasingly capable web agents . however, training such agents at scale still relies on high-quality interaction trajectories that are difficult to obtain at scale.
Approach:	They propose a framework for scalable trajectory synthesis that simulates state transitions without network dependencies and integrates Monte Carlo Tree Search to enable reversible exploration over the simulated state space.
Outcome:	Experiments on WebArena, WebVoyager, and Mind2Web-Online show that agents trained exclusively on synthesized trajectories outperform those trained on real-world data.

TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models (2022.emnlp-main)

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Joel Jang, Seonghyeon Ye, Changho Lee, Sohee Yang, Joongbo Shin, Janghoon Han, Gyeonghun Kim, Minjoon Seo

TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks (2026.findings-acl)

Challenge:	Language Models (LMs) become outdated as the world changes, a phenomenon called temporal misalignment.
Approach:	They propose a lifelong benchmark that utilizes the difference between consecutive snapshots of English Wikipedia and English Wikidata for training and evaluation.
Outcome:	The proposed benchmark can be trained on the difference between consecutive snapshots of English Wikipedia and English Wikidata for training and evaluation.

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Yuanze Hu, Xinyu Wang, Zhichao Yang, Gen Li, Ye Qiu, Zhaoxin Fan, Yifan Sun, Wenjun wu, Jin Dong, Xiaotie Deng

Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling (2025.acl-long)

Challenge:	Lightweight Vision-Language Models (VLMs) are indispensable for resource-constrained applications.
Approach:	They propose a framework that retrieves context from a memory bank to enhance alignment . they propose EMI-based approach to align vision and language models .
Outcome:	The proposed framework reduces training loss, accelerates convergence, and enhances task performance with negligible computational overhead.

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Yang Yuguang, Yu Pan, Jixun Yao, Xiang Zhang, Jianhao Ye, Hongbin Zhou, Lei Xie, Lei Ma, Jianjun Zhao

POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion (2025.emnlp-main)

Challenge:	Expressive zero-shot voice conversion (VC) aims to modify source timbre to match unseen speaker . existing zero- shot VC systems struggle to reproduce paralinguistic information in highly expressive speech .
Approach:	They propose a framework for expressive zero-shot voice conversion that uses hybrid content encoding and memory-augmented context-aware timbre modeling.
Outcome:	The proposed framework surpasses state-of-the-art VC systems in speech naturalness, speaker similarity, and speaker similarness.

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Yuan Liu, Zhongyin Zhao, Le Tian, Haicheng Wang, Xubing Ye, Yangxiu You, Zilin Yu, Chuhan Wu, Zhou Xiao, Yang Yu, Jie Zhou

Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels (2025.emnlp-main)

Challenge:	Existing approaches to training document conversion models with manual annotation are costly and time-consuming, and training student models by distilling outputs from teacher models can significantly limit their performance in real-world applications.
Approach:	They propose a fully automated framework for constructing high-quality document extraction datasets and models capable of handling diverse document formats and layouts.
Outcome:	The proposed model outperforms existing models and improves on annotated documents.

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Junjie Ye, Yuming Yang, Yang Nan, Shuo Li, Qi Zhang, Tao Gui, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan

HeteroSpec: Leveraging Contextual Heterogeneity for Efficient Speculative Decoding (2026.acl-long)

Challenge:	Large language models (LLMs) acquire substantial world knowledge during pretraining, which is further shaped by post-training techniques such as supervised fine-tuning (SFT).
Approach:	They evaluate closed-book question answering (CBQA) performance across five LLMs from the LLaMA-2 and LLama-3 families and examine the impact of supervised fine-tuning on model knowledge.
Outcome:	The proposed model performance is 14% worse than models fine-tuned on 1,920 samples and 12% worse on 240 samples.

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Siran Liu, Yang Ye, Qianchao Zhu, Zane Cao, Yongchao He

Green & Sustainable NLP

Challenge:	Autoregressive decoding limits the inference throughput of Large Language Models due to its sequential dependency.
Approach:	They propose a framework that allocates verification effort in proportion to candidate uncertainty.
Outcome:	Speculative decoding achieves an average speedup over state-of-the-art methods . a small subset of high-confidence predictions accounts for most successful verifications .

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

STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation (2022.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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Qingkai Fang, Rong Ye, Lei Li, Yang Feng, Mingxuan Wang

SampleMix: A Sample-wise Pre-training Data Mixing Strategy by Coordinating Data Quality and Diversity (2025.findings-emnlp)

Challenge:	Existing methods to learn speech representations for end-to-end speech-totext translation (ST) neglect the representation discrepancy across modalities.
Approach:	They propose a method to calibrate the representation discrepancy between modalities by mixing up the representation sequences of different modality inputs.
Outcome:	The proposed method alleviates the cross-modal representation discrepancy and improves on a strong baseline on eight translation directions.

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Xiangyu Xi, Deyang Kong, Jian Yang, Jiawei Yang, Zhengyu Chen, Wei Wang, Jingang Wang, Xunliang Cai, Shikun Zhang, Wei Ye

Polymorphic Universal Transformer (2026.acl-long)

Challenge:	Existing methods for pretraining data mixing for large language models neglect significant inter-domain overlaps and commonalities, failing to control the global diversity of the constructed training dataset.
Approach:	They propose a sample-wise data mixture approach that performs global cross-domain sampling by systematically evaluating the quality and diversity of each sample.
Outcome:	The proposed method exceeds existing domain-based methods in multiple downstream tasks and perplexity assessments.

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Yilong Chen, Zitian Gao, Yihao Xiao, Jason Klein Liu, Xinyu Yang, Yifan Luo, Haoming Luo, Zhengmao Ye, Tingwen Liu, Ran Tao, Bryan Dai

BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)

Challenge:	Compute Distribution Skew is a pathological phenomenon in ultra-deep recurrent models . it causes over-smoothing, representation rank collapse, and degraded reasoning performance.
Approach:	They propose a dynamic architecture that redefines recursive computation by decoupling parameter count from depth.
Outcome:	The proposed model significantly improves representation rank and reasoning robustness while reducing computation by 64.7%.

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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang

Speech & Audio in NLP

Challenge:	Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS).
Approach:	They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features.
Outcome:	The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization.

Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge (2020.acl-main)

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Bowen Zhang, Min Yang, Xutao Li, Yunming Ye, Xiaofei Xu, Kuai Dai

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)

Challenge:	Existing methods for stance detection are struggling to cope with the data across targets.
Approach:	They propose a model that uses external knowledge as a bridge to enable knowledge transfer across different targets.
Outcome:	The proposed model outperforms existing methods on a large real-world dataset.

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Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun