Papers by Ye Ma

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

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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Zhanyu Ma, Jian Ye, Xurui Yang, Jianfeng Liu

Songs Across Borders: Singable and Controllable Neural Lyric Translation (2023.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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Longshen Ou, Xichu Ma, Min-Yen Kan, Ye Wang

Machine Translation

Challenge:	Obtaining singable lyric translations can facilitate the globalization of the music publishing industry .
Approach:	They formalize lyric translation into a constrained translation problem and instantiate them to an English-Chinese system.
Outcome:	The proposed model achieves 99.85%, 99.00%, and 95.52% on length accuracy, rhyme accuracy, and word boundary recall.

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

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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.

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

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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

Improving Preference Alignment of LLM with Inference-Free Self-Refinement (2025.findings-emnlp)

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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Fukun Ma, Kaibin Tian, Jieting Xue, Xiaoyi Wang, Ye Ma, Quan Chen, Peng Jiang, Lijie Wen

Challenge:	Large language models (LLMs) develop in-context learning capability through pretraining and instruction tuning.
Approach:	Large language models (LLMs) develop in-context learning capability through pretraining and instruction tuning.
Outcome:	Experiments show that incorporating IFSR into preference alignment yields performance improvement over 10%.

CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction (2023.emnlp-main)

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Jingheng Ye, Yinghui Li, Qingyu Zhou, Yangning Li, Shirong Ma, Hai-Tao Zheng, Ying Shen

Multi-Path Transformer is Better: A Case Study on Neural Machine Translation (2022.findings-emnlp)

Challenge:	Evaluating the performance of Grammatical Error Correction systems is a challenging task due to its subjectivity.
Approach:	They propose a method to evaluate GEC systems in multi-reference evaluation setting . they use consistent edit boundaries to eliminate bias caused by inconsistent edit boundaries .
Outcome:	The proposed evaluation metric eliminates bias caused by inconsistent edit boundaries on six English reference sets.

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Ye Lin, Shuhan Zhou, Yanyang Li, Anxiang Ma, Tong Xiao, Jingbo Zhu

PsyScam: A Benchmark for Psychological Techniques in Real-World Scams (2025.findings-emnlp)

Challenge:	Extensive experiments on 12 WMT tasks show that shallower multi-path models can achieve similar or even better performance than the deeper model.
Approach:	They propose to use a parameter-efficient multi-path structure to fuse features extracted from different paths to achieve better performance.
Outcome:	The proposed model can achieve better performance with the same number of parameters than the deeper model.

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Shang Ma, Tianyi Ma, Jiahao Liu, Wei Song, Zhenkai Liang, Xusheng Xiao, Yanfang Ye

Challenge:	PTs are employed by scammers to manipulate victims and cause lasting psychological trauma.
Approach:	They propose a benchmark to capture the PTs employed in real-worldscam reports and investigate how LLMs can be utilized to generate variants of scams based on the pts and the contexts provided by thesescams.
Outcome:	The proposed model can generate variants of scams based on the PTs employed in real-world scam reports and the contexts provided by these scams.

Exploring Mathematical Extrapolation of Large Language Models with Synthetic Data (2024.findings-acl)

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Haolong Li, Yu Ma, Yinqi Zhang, Chen Ye, Jie Chen

QSpell 250K: A Large-Scale, Practical Dataset for Chinese Search Query Spell Correction (2025.naacl-industry)

Challenge:	Large language models (LLMs) have shown excellent capabilities in language understanding, text generation and many other tasks, but struggle in complex multi-step reasoning problems such as mathematical reasoning.
Approach:	They propose to fine tune an open-llama-3B model to perform well on multi-step reasoning tasks via synthetic data.
Outcome:	The proposed model can reach a zero-shot pass@1 at 0.44 on the in-domain dataset and demonstrates certain generalization capabilities on the out-of-domain data.

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Dezhi Ye, Haomei Jia, Junwei Hu, Tian Bowen, Jie Liu, Haijin Liang, Jin Ma, Wenmin Wang

Information Retrieval

Challenge:	Chinese Search Query Spell Correction is a task designed to identify and correct typographical errors within queries.
Approach:	They propose a large-scale benchmark specifically developed for Chinese Query Spell Correction.
Outcome:	The proposed benchmark covers a broad range of topics, including formal entities, everyday colloquialisms and idiomatic expressions.

AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering (2026.acl-long)

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Zheyuan Zhang, Kaiwen Shi, Zhengqing Yuan, Zehong Wang, Tianyi Ma, Keerthiram Murugesan, Vincent Galassi, Chuxu Zhang, Yanfang Ye

AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness (2025.acl-long)

Challenge:	Existing approaches to agent routing emphasize cost efficiency while overlooking the fine-grained contextual and relational structure inherent in QA tasks.
Approach:	They propose a framework that formulates multi-agent QA as a knowledge-graph-guided routing problem supervised by empirical performance signals.
Outcome:	The proposed framework outperforms single-agent and ensemble baselines while generalizing across benchmarks and LLM backbones.

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Zixin Chen, Hongzhan Lin, Kaixin Li, Ziyang Luo, Zhen Ye, Guang Chen, Zhiyong Huang, Jing Ma

Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering (2026.acl-long)

Challenge:	Existing models that assess mLLMs on harmful meme understanding are inaccurate and lack accuracy.
Approach:	They propose a framework that adaptively probes the reasoning capabilities of mLLMs . their framework systematically reveals the varying performance of different target mllms a .
Outcome:	The proposed framework systematically reveals the performance of different target mLLMs.

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Qiming Li, Xiaocheng Feng, Yixuan Ma, Ruihan Chen, Zihe Tong, Zekai Ye, Xiachong Feng, Libo Qin, Haoyu Ren, Kun Chen, Yunfei Lu, Dandan Tu, Bing Qin

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

Challenge:	Existing approaches to enhance multilingual reasoning capabilities rely on costly multilingual training or employ prompting with external translation tools.
Approach:	They propose a training-free inference-time method to enhance multilingual reasoning capabilities via Representation Engineering without additional training data or tools.
Outcome:	The proposed method outperforms existing methods on four reasoning benchmarks in English and Thai and Swahili.

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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

TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System (2025.findings-naacl)

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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Zeyu Zhang, Jianxun Lian, Chen Ma, Yaning Qu, Ye Luo, Lei Wang, Rui Li, Xu Chen, Yankai Lin, Le Wu, Xing Xie, Ji-Rong Wen

emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation (2024.findings-acl)

Challenge:	Trending topics bring in a new channel for poisoning attacks, resulting in negative impacts on society.
Approach:	They propose an LLM-based multi-agent system to simulate trending topics in social media . they propose a time-aware interaction mechanism, centralized message dissemination, and an interactive system .
Outcome:	The proposed system simulates trending topics under poisoning attacks on social media platforms.

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Ziyang Ma, Zhisheng Zheng, Jiaxin Ye, Jinchao Li, Zhifu Gao, ShiLiang Zhang, Xie Chen

Alleviating Hallucinations from Knowledge Misalignment in Large Language Models via Selective Abstention Learning (2025.acl-long)

Challenge:	Existing models for speech emotion recognition are not suitable for emotional tasks.
Approach:	They propose a universal speech emotion representation model that is pre-trained on open-source emotion data.
Outcome:	euphoria2vec outperforms state-of-the-art models and emotion specialist models . it shows consistent improvements among 10 different languages of speech emotion recognition datasets .

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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yuxuan Gu, Yangfan Ye, Liang Zhao, Weihong Zhong, Baoxin Wang, Dayong Wu, Guoping Hu, Lingpeng Kong, Tong Xiao, Ting Liu, Bing Qin

How Transliterations Improve Crosslingual Alignment (2025.coling-main)

Challenge:	Large language models (LLMs) suffer from severe hallucination issues due to the knowledge misalignment between the pre-training stage and the supervised fine-tuning stage.
Approach:	They propose a training objective with an abstention mechanism that selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token.
Outcome:	The proposed model selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token.

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Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Ayyoob ImaniGooghari, Orgest Xhelili, Haotian Ye, Chunlan Ma, François Yvon, Hinrich Schütze

Multilinguality

Challenge:	Recent studies show that post-aligning multilingual pretrained language models improve crosslingual alignment, but it is unclear how and why this is achieved.
Approach:	They propose to explicitly evaluate crosslingual alignment by adding transliterations to models using original and transliterated data.
Outcome:	The proposed approach improves crosslingual alignment even for random sentences.

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

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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

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

NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning (2025.acl-long)

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Zheyuan Zhang, Yiyang Li, Nhi Ha Lan Le, Zehong Wang, Tianyi Ma, Vincent Galassi, Keerthiram Murugesan, Nuno Moniz, Werner Geyer, Nitesh V Chawla, Chuxu Zhang, Yanfang Ye

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

Challenge:	Diet plays a critical role in human health, but tailoring dietary reasoning to individual health conditions remains a challenge.
Approach:	a new benchmark evaluates dietary reasoning using a national health survey data set.
Outcome:	The NGQA benchmark evaluates dietary reasoning across three tasks using a set of question complexity settings and baseline models.

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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 .

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

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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

TransMI: A Framework to Create Strong Baselines from Multilingual Pretrained Language Models for Transliterated Data (2025.coling-main)

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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Yihong Liu, Chunlan Ma, Haotian Ye, Hinrich Schütze

Multilinguality

Challenge:	Existing mPLMs that handle non-transliterated data are not sufficient to train crosslingual models.
Approach:	They propose a framework to transliterate related languages into a common script by exploiting existing mPLMs and their tokenizer without any training.
Outcome:	The proposed framework can create strong baselines for data that is transliterated into a common script by exploiting an existing mPLM and its tokenizer without any training.

Unsupervised Text Style Transfer for Controllable Intensity (2026.findings-eacl)

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Shuhuan Gu, Wenbiao Tao, Xinchen Ma, Kangkang He, Ye Guo, Xiang Li, Yunshi Lan

Low-Resource NLP

Challenge:	Unsupervised Text Style Transfer (UTST) aims to transfer the stylistic properties of a given text without parallel text pairs.
Approach:	They propose a SFT-then-PPO paradigm to fine-tune an LLM with parallel data and reward functions for distinguishing stylistic intensity in hierarchical levels.
Outcome:	The proposed system can transfer stylistic properties without parallel text pairs even for adjacent levels of intensity.

LLM-Empowered Class Imbalanced Graph Prompt Learning for Online Drug Trafficking Detection (2025.findings-acl)

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Tianyi Ma, Yiyue Qian, Zehong Wang, Zheyuan Zhang, Chuxu Zhang, Yanfang Ye

Shadow-Activated Backdoor Attacks on Multimodal Large Language Models (2025.findings-acl)

Challenge:	Existing approaches to combat illicit drug trafficking are impractical due to the scarcity of labeled samples and imbalance of classes.
Approach:	They propose a Large Language Model-empowered Heterogeneous Graph Prompt Learning framework for illicit drug trafficking detection that leverages LLM to facilitate heterogeneous graph neural networks to effectively identify minority classes.
Outcome:	The proposed framework is able to identify minority classes in class-imbalanced scenarios.

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Ziyi Yin, Muchao Ye, Yuanpu Cao, Jiaqi Wang, Aofei Chang, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma

Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization (2024.findings-naacl)

Challenge:	Existing backdoor attacks on Multimodal Large Language Models are less applicable to open-ended conversations with users.
Approach:	They propose a shadow-activated backdoor attack scenario where attackers inject malicious content into the responses of MLLMs when the responses explicitly relate to the shadowed object.
Outcome:	The proposed framework achieves the desired behaviors by constructing a poisoned dataset and implementing an attention-regularized tuning strategy.

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Tong Ye, Lingfei Wu, Tengfei Ma, Xuhong Zhang, Yangkai Du, Peiyu Liu, Shouling Ji, Wenhai Wang

Learning Autonomous Driving Tasks via Human Feedbacks with Large Language Models (2024.findings-emnlp)

Challenge:	Existing methods to generate source code summaries are coarse-grained and noise-filled . however, they do not capture contextual code semantics and are often outdated in continuous software iteration.
Approach:	They propose a fine-grained Token-level retrieval-augmented mechanism on the decoder side to enhance performance of neural models.
Outcome:	The proposed method produces more low-frequency tokens and is interpretable.

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Yunsheng Ma, Xu Cao, Wenqian Ye, Can Cui, Kai Mei, Ziran Wang

Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks (2026.findings-acl)

Challenge:	Existing systems focus on making autonomous driving decisions without human interaction, but human-like decision-making is still an important factor in designing autonomous driving systems.
Approach:	They propose a framework leveraging Large Language Models for learning human-centered driving decisions from diverse simulation scenarios and environments that incorporate human feedback.
Outcome:	The proposed framework can match baseline extensively trained reinforcement learning models in driving scenarios and store optimal driving programming policy using Retrieval Augmented Generation (RAG).

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Yuxiang Zhang, Jiangming Shu, Ye Ma, Xueyuan Lin, Shangxi Wu, Jitao Sang

Best Practices for Distilling Large Language Models into BERT for Web Search Ranking (2025.coling-industry)

Challenge:	Existing approaches to managing working memory are based on external mechanisms that lack awareness of the agent’s reasoning state, leading to suboptimal decisions.
Approach:	They propose a framework that treats working memory management as learnable policy actions and enables joint optimization of information retention and task performance through end-to-end reinforcement learning.
Outcome:	The proposed framework matches models 16 larger while reducing average context length by 51%, with learned strategies that adapt to model capabilities and generalize across task complexities.

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Dezhi Ye, Junwei Hu, Jiabin Fan, Bowen Tian, Jie Liu, Haijin Liang, Jin Ma

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

Challenge:	Recent studies have highlighted the potential of Large Language Models (LLMs) as zero-shot relevance rankers.
Approach:	They propose to use a ranking loss to transfer ranking knowledge from LLMs to smaller models like BERT.
Outcome:	The proposed model has been successfully integrated into a commercial web search engine as of February 2024.

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

Taxi1500: A Dataset for Multilingual Text Classification in 1500 Languages (2025.naacl-short)

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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Chunlan Ma, Ayyoob Imani, Haotian Ye, Renhao Pei, Ehsaneddin Asgari, Hinrich Schuetze

Information Extraction & Text Mining

Challenge:	a large-scale text classification dataset encompassing 1504 languages is needed to address this gap . low-resource languages are often overlooked due to the scarcity of evaluation datasets.
Approach:	They propose to use translations of the Bible to construct a large-scale text classification dataset that covers 1504 languages and annotate them using crowdsourcing.
Outcome:	The proposed dataset covers 1504 languages and is available to the public.

CPO: Addressing Reward Ambiguity in Role-playing Dialogue via Comparative Policy Optimization (2025.findings-emnlp)

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Jing Ye, Rui Wang, Yuchuan Wu, Victor Ma, Feiteng Fang, Fei Huang, Yongbin Li

Dialogue Systems & Conversational Agents

Challenge:	Comparative Policy Optimization (CPO) redefines the reward evaluation paradigm by shifting from sample-wise scoring to comparative group-wise score.
Approach:	They propose a method to optimize subjective tasks by shifting from sample-wise to comparative group-wise scoring.
Outcome:	The proposed framework shifts from sample-wise scoring to comparative group-wise score . it minimizes contextual bias and enables more robust and fair performance evaluation.

Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models (2025.findings-naacl)

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Jiachen Ma, Yijiang Li, Zhiqing Xiao, Anda Cao, Jie Zhang, Chao Ye, Junbo Zhao

TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language Models (2024.acl-long)

Challenge:	Text-to-image (T2I) models can be used to generate harmful content such as sexually explicit, unfaithful, and misleading or Not-Safe-for-Work (NSFW) images.
Approach:	They propose a more practical and universal attack that does not require the presence of a target model.
Outcome:	The proposed attack bypasses both text and image safety checkers while preserving high semantic alignment with the target prompt.

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Yihong Liu, Chunlan Ma, Haotian Ye, Hinrich Schuetze

x1: Learning to Think Adaptively Across Languages and Cultures (2026.findings-acl)

Challenge:	The world’s more than 7000 languages are written in at least 293 scripts, which poses a difficulty for multilingual pretrained language models in learning crosslingual knowledge through lexical overlap.
Approach:	They propose a framework that optimizes the Transliteration Contrastive Modeling objective to fine-tune an mPLM by contrasting sentences in its training data and transliterations in a unified script.
Outcome:	The proposed model outperforms Glot500-m on zero-shot crosslingual transfer tasks while retaining uniformity across scripts.

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Yangfan Ye, Xiaocheng Feng, Xiachong Feng, Yichong Huang, Zekun Yuan, Lei Huang, Weitao Ma, Qichen Hong, Yunfei Lu, Dandan Tu, Bing Qin

On the Influence of Masking Policies in Intermediate Pre-training (2021.emnlp-main)

Challenge:	Existing large language models (LLMs) ignore this diversity by reasoning in a single dominant language.
Approach:	They propose a family of reasoning models that can adaptively reason in an advantageous language on a per-instance basis.
Outcome:	The proposed model can reason in a single dominant language on a per-instance basis.

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Qinyuan Ye, Belinda Z. Li, Sinong Wang, Benjamin Bolte, Hao Ma, Wen-tau Yih, Xiang Ren, Madian Khabsa

LangSAMP: Language-Script Aware Multilingual Pretraining (2025.acl-long)

Challenge:	Existing studies show that inserting an intermediate pre-training stage improves performance of masked language models.
Approach:	They propose methods to automate the discovery of optimal masking policies via direct supervision or meta-learning.
Outcome:	The proposed method outperforms the heuristic of masking named entities on TriviaQA and can be generalizable beyond that task.

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Yihong Liu, Haotian Ye, Chunlan Ma, Mingyang Wang, Hinrich Schuetze

Can LLMs Convert Graphs to Text-Attributed Graphs? (2025.naacl-long)

Challenge:	Recent multilingual pretrained language models often avoid using language embeddings, which places a significant burden on token representations to encode all language-specific information.
Approach:	They propose a method that incorporates both language and script embeddings into the output of Transformer blocks before passing the final representations to the language modeling head for prediction.
Outcome:	The proposed method outperforms the baseline model in zero-shot crosslingual transfer across diverse downstream tasks.

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Zehong Wang, Sidney Liu, Zheyuan Zhang, Tianyi Ma, Chuxu Zhang, Yanfang Ye

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

Challenge:	Existing approaches to model graph-structured data are limited by the availability of text-attributed graph data.
Approach:	They propose a method to convert existing graphs into text-attributed graphs using large language models.
Outcome:	The proposed method outperforms existing approaches that manually design node features on text-free graphs.

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

LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement (2025.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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Boyi Kang, Xinfa Zhu, Zihan Zhang, Zhen Ye, Mingshuai Liu, Ziqian Wang, Yike Zhu, Guobin Ma, Jun Chen, Longshuai Xiao, Chao Weng, Wei Xue, Lei Xie

Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play (2026.acl-long)

Challenge:	Recent advances in language models have demonstrated strong capabilities in semantic understanding and contextual modeling.
Approach:	They propose a LLaMA-based language model that incentivizes generalization capabilities for speech enhancement.
Outcome:	The proposed language model outperforms prior task-specific discriminative and generative models in acoustic enhancement tasks.

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Xiachong Feng, Deyi Yin, Xiaocheng Feng, Yi Jiang, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Qiming Li, Yuxuan Gu, Bing Qin, Lingpeng Kong

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

Challenge:	Existing self-play approaches to developing general reasoning in language models rely on terminal game outcomes.
Approach:	They propose a game-based reasoning transfer model that addresses two barriers to reasoning transfer.
Outcome:	The proposed model improves mathematical reasoning, general reasoning, and code generation benchmarks.

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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.

Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding (2024.acl-long)

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Zhihan Zhang, Yixin Cao, Chenchen Ye, Yunshan Ma, Lizi Liao, Tat-Seng Chua

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

Challenge:	Existing research in complex event analysis has made significant strides but is constrained by inadequate natural language processing techniques.
Approach:	They propose a novel approach using Large Language Models to extract and analyze the event chain within TCE, characterized by their key points and timestamps.
Outcome:	The proposed model performs comparable to models with long context window and retrieval-augmented generation method in three distinct tasks .

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

Interpretable Rationale Augmented Charge Prediction System (C18-2)

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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Xin Jiang, Hai Ye, Zhunchen Luo, WenHan Chao, Wenjia Ma

Explainability & Interpretability in NLP

Challenge:	Existing studies treat charge prediction as a text classification problem, but in the field of justice, every decision may be a matter of life and death.
Approach:	They propose to extract readable rationales from text and then create a rationale augmented classification model to enhance the prediction accuracy.
Outcome:	The proposed system can extract readable rationales in a high consistency with manual annotation and is comparable with the attention model in prediction accuracy.

CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning (2025.acl-long)

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Yangfan Ye, Xiaocheng Feng, Zekun Yuan, Xiachong Feng, Libo Qin, Lei Huang, Weitao Ma, Yichong Huang, Zhirui Zhang, Yunfei Lu, Xiaohui Yan, Duyu Tang, Dandan Tu, Bing Qin

Multilinguality

Challenge:	Existing fine-tuning approaches that focus on English-centric training corpora often introduce implicit cross-lingual alignment, overlooking the potential for more profound, latent-level cross-linguistic interactions.
Approach:	They propose a multilingual fine-tuning paradigm that explicitly establishes a cross-lingual connection mechanism at the latent level.
Outcome:	The proposed model outperforms vanilla SFT and offers a strong latent-level alternative to data-level augmentation methods.

ScratchEval: Are GPT-4o Smarter than My Child? Evaluating Large Multimodal Models with Visual Programming Challenges (2025.naacl-short)

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Rao Fu, Ziyang Luo, Hongzhan Lin, Zhen Ye, Jing Ma

Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis (2025.coling-main)

Challenge:	Recent advances in large multimodal models (LMMs) have demonstrated impressive code generation capabilities, primarily evaluated through image-to-code benchmarks.
Approach:	They propose a visual programming reasoning benchmark based on Scratch, a block-based visual programming language widely used in children’s programming education.
Outcome:	The proposed framework evaluates the visual programming ability of large multimodal models by integrating visual elements and embedded programming logic.

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Weitao Ma, Xiaocheng Feng, Weihong Zhong, Lei Huang, Yangfan Ye, Xiachong Feng, Bing Qin

SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution (2026.findings-acl)

Challenge:	Existing studies have focused on instance-level unlearning, specifically removing predefined instances containing sensitive content.
Approach:	They propose a task to erase entity-related knowledge from the target model completely by analyzing the forget set and its size.
Outcome:	The proposed task systematically evaluates popular unlearning algorithms and reveals that the knowledge coverage of the forget set and its size play pivotal roles.

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Xiachong Feng, Yi Jiang, Xiaocheng Feng, Deyi Yin, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Yuxuan Gu, Chonghan Qin, Bing Qin, Lingpeng Kong

Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer? (2022.emnlp-main)

Challenge:	Existing approaches to improve social intelligence of AI systems employ retrospective attributions and lack theoretical grounding.
Approach:	They propose a framework that uses Shapley values to ensure fair credit distribution with axiomatic guarantees of efficiency, symmetry, and marginality.
Outcome:	The proposed framework matches or exceeds proprietary models including GPT-4o and Claude-3.5-Sonnet.

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Ningyu Xu, Tao Gui, Ruotian Ma, Qi Zhang, Jingting Ye, Menghan Zhang, Xuanjing Huang

Data to Defense: The Role of Curation in Aligning Large Language Models Against Safety Compromise (2025.emnlp-main)

Challenge:	Multilingual BERT (mBERT) has demonstrated considerable cross-lingual syntactic ability, but it is not well understood what leads to this variation and whether it fairly reflects difference between languages.
Approach:	They propose to use multilingual BERT to enable zero-shot cross-lingual transfer of syntactic knowledge between different languages by generating grammatical relations in 24 different languages.
Outcome:	The results show that the distance between the distributions of different languages is highly consistent with the syntactic difference in terms of linguistic formalisms.

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Xiaoqun Liu, Jiacheng Liang, Luoxi Tang, Muchao Ye, Weicheng Ma, Zhaohan Xi

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

Challenge:	Recent studies have identified a vulnerability in large language models (LLMs) during customization.
Approach:	They propose an adaptive data curation approach that allows any text to be curated to enhance its effectiveness in counteracting harmful samples during customization.
Outcome:	The proposed approach reduces compromising effects and generates 100% safe responses.

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

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

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

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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.

LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation (2026.findings-acl)

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Yuxiao Ye, Yiming Zhang, Yiran Rex Ma, Huiyuan Xie, Huining Zhu, Zhiyuan Liu

Dialogue Systems & Conversational Agents

Challenge:	Large Language Models (LLMs) have enabled Multi-Agent Systems (MASs) where agents interact through natural language to solve complex tasks or simulate multi-party dialogues.
Approach:	They propose a linguistically-grounded game-theoretic paradigm for multi-agent dialogue generation that uses a training-free equilibrium approximation algorithm to model dialogue over communicative intents and strategies.
Outcome:	The proposed framework improves agents’ communication efficiency by helping them convey their intended meaning more effectively through language.

A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing (2023.findings-acl)

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Ye Ma, Mingming Sun, Ping Li

Structured Data in NLP

Challenge:	Existing parsers that capture dependency graphs are lacking in capturing explicit dependencies . graph-based parsing is a popular choice for capturing dependency relationships between words .
Approach:	They propose a semi-autoregressive dependency parser that generates dependency graphs by adding nodes and edge groups autoregressively while pouring out all group elements in parallel.
Outcome:	The proposed method outperforms baselines on Enhanced Universal Dependencies of multiple languages.

Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization (2025.acl-long)

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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yangfan Ye, Weihong Zhong, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Bing Qin