Papers by Xin Ye

IM^2: an Interpretable and Multi-category Integrated Metric Framework for Automatic Dialogue Evaluation (2022.emnlp-main)

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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Zhihua Jiang, Guanghui Ye, Dongning Rao, Di Wang, Xin Miao

Dialogue Systems & Conversational Agents

Challenge:	Evaluation metrics for dialogue systems are expensive and time-consuming . current evaluation metrics focus on a single quality or several qualities .
Approach:	They propose an interpretable, multi-faceted, and controllable framework to combine dialogue metrics which are good at measuring different qualities.
Outcome:	The proposed framework integrates a large number of evaluation metrics to improve the performance of the model.

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

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

Dialogue Systems & Conversational Agents

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

RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation (2024.emnlp-demo)

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Qinyu Luo, Yining Ye, Shihao Liang, Zhong Zhang, Yujia Qin, Yaxi Lu, Yesai Wu, Xin Cong, Yankai Lin, Yingli Zhang, Xiaoyin Che, Zhiyuan Liu, Maosong Sun

A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation (2024.acl-long)

Challenge:	Xia et al., 2018) demonstrate that a large language model can generate and maintain high-quality code documentation.
Approach:	They propose a large language model powered open-source framework for generating, maintaining, and updating code documentation.
Outcome:	The proposed framework generates high-quality documentation for the entire project.

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Kai Chen, Ye Wang, Yitong Li, Aiping Li, Han Yu, Xin Song

Responsible & Trustworthy NLP

Challenge:	Existing methods for temporal knowledge graphs de-emphasize temporal correlations between facts sequences and ignore inferring clues from missing facts.
Approach:	They propose a Temporal PAth-based reasoning model that is robust to ambiguous temporal data.
Outcome:	The proposed model outperforms SOTA methods on the link prediction task.

Temporal Scaling Law for Large Language Models (2025.emnlp-main)

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Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Zhenpeng Su, Wei Huang, Jianwei Niu, Jungong Han, Guiguang Ding

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)

Challenge:	Existing studies have found that the test loss of LLMs scales as power-laws with model size, computational budget, and dataset size.
Approach:	They propose a concept of Temporal Scaling Law to study test loss of LLMs . they break down test loss into fine-grained token positions and develop a dynamic hyperbolic-law .
Outcome:	The proposed model predicts the test loss of LLMs as the training steps scale up.

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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu

StructGPT: A General Framework for Large Language Model to Reason over Structured Data (2023.emnlp-main)

Challenge:	Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output.
Approach:	They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities.
Outcome:	The proposed model can be trained stably without any alterations to existing models or training paradigms.

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Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Xin Zhao, Ji-Rong Wen

Generative Psycho-Lexical Approach for Constructing Value Systems in Large Language Models (2025.acl-long)

Challenge:	Experiments conducted on three types of structured data show that StructGPT greatly improves the performance of LLMs.
Approach:	They propose an iterative Reading-then-Reasoning framework to solve question answering tasks based on structured data.
Outcome:	The proposed framework improves the reasoning ability of large language models over structured data under the few-shot and zero-shot settings.

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Haoran Ye, TianZe Zhang, Yuhang Xie, Liyuan Zhang, Yuanyi Ren, Xin Zhang, Guojie Song

MusKGC: A Flexible Multi-source Knowledge Enhancement Framework for Open-World Knowledge Graph Completion (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have raised concerns regarding their intrinsic values.
Approach:	They propose a psychologically grounded five-factor value system for Large Language Models that integrates psychological principles with cutting-edge AI priorities.
Outcome:	The proposed value system meets standard psychological criteria, improves LLM safety prediction, and enhances Llm alignment, when compared to the canonical Schwartz’s values.

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Xin Song, Liu Haiyan, Haiyang Wang, Ye Wang, Kai Chen, Bin Zhou

Structured Data in NLP

Challenge:	Open-world knowledge graph completion (KGC) aims to infer novel facts by enriching existing graphs with external knowledge sources while maintaining semantic consistency under the open-world assumption (OWA).
Approach:	They propose a multi-source knowledge enhancement framework based on an open-world assumption (OWA) that integrates external knowledge sources and a new evaluation strategy to validate new facts.
Outcome:	The proposed model achieves SOTA performance across benchmarks and the evaluation strategy effectively assesses new facts under OWA.

Anchor-based Large Language Models (2024.findings-acl)

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Jianhui Pang, Fanghua Ye, Derek Wong, Xin He, Wanshun Chen, Longyue Wang

SalaMAnder: Shapley-based Mathematical Expression Attribution and Metric for Chain-of-Thought Reasoning (2025.findings-emnlp)

Challenge:	Large language models (LLMs) require massive GPU memory due to their size and parameter count.
Approach:	They propose to use anchor-based self-attention network and anchor-basic inference strategy to compress sequence information into an anchor token, reducing the keys/values cache and enhancing inference efficiency.
Outcome:	The proposed model reduces the key/value cache and improves inference efficiency by 99% while maintaining similar accuracy levels.

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Yue Xin, Chen Shen, Shaotian Yan, Xiaosong Yuan, Yaoming Wang, Xiaofeng Zhang, Chenxi Huang, Jieping Ye

Responsible & Trustworthy NLP

Challenge:	Chain-of-Thought prompting improves the math reasoning capability of large language models.
Approach:	They propose a method for attribution of component-level contributions in CoT reasoning using Shapley value and a stratified sampling algorithm that significantly reduces computational complexity.
Outcome:	The proposed method reduces computational complexity and provides robust correlations with model performance.

HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks (2026.findings-acl)

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Yiming Zeng, Jinghan Cao, Zexin Li, Wanhao Yu, Zhankai Ye, Dawei Xiang, Ting Hua, Xin Liu, Shangqian Gao, Tingting Yu

Text Generation

Challenge:	Existing approaches treat instruction-based text editing as a generic text generation problem. Existing methods either over-edit or fail to apply modifications consistently.
Approach:	They propose a framework that processes each editing request to best align with it.
Outcome:	The proposed framework achieves 9% improvement over the state-of-the-art model.

UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model (2023.findings-emnlp)

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Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, Qin Jin, Liang He, Xin Lin, Fei Huang

SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)

Challenge:	Existing studies for visually-situated language understanding have shown shallow zero-shot visual text recognition ability when fed a low-resolution image with salient text information.
Approach:	They propose a model for universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM) their model is jointly finetuned on a wide range of visually situated language understanding tasks via a unified instruction format.
Outcome:	The proposed model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks across 5 domains: documents, tables, charts, natural images, and webpage screenshots.

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Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li

Challenge:	Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy.
Approach:	They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration.
Outcome:	The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent .

DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population (2022.emnlp-demos)

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Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li

Information Extraction & Text Mining

Challenge:	Existing knowledge extraction tools are not complete due to emerging entities and relations in real-world applications.
Approach:	They propose an open-source knowledge extraction toolkit DeepKE that supports low-resource, document-level and multimodal scenarios in the knowledge base population.
Outcome:	The proposed toolkit supports low-resource, document-level and multimodal scenarios in the knowledge base population.

Fast Quiet-STaR: Thinking Without Thought Tokens (2025.findings-emnlp)

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Wei Huang, Yizhe Xiong, Xin Ye, Zhijie Deng, Hui Chen, Zijia Lin, Guiguang Ding

Reasoning

Challenge:	Large Language Models have achieved impressive performance across a range of tasks, but further gains require more than scaling up model sizes or training data.
Approach:	They propose a method that gradually reduces the number of thought tokens . this method allows models to internalize more abstract reasoning processes .
Outcome:	The proposed framework preserves the benefits of token-level reasoning while reducing computational cost.

MMAD:Multi-modal Movie Audio Description (2024.lrec-main)

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Xiaojun Ye, Junhao Chen, Xiang Li, Haidong Xin, Chao Li, Sheng Zhou, Jiajun Bu

Truth or Sophistry? LoFa: A Benchmark for LLM Robustness Against Logical Fallacies (2026.acl-long)

Challenge:	Current methods of creating accessible movies rely on manual work, resulting in high costs and limited scalability.
Approach:	They propose a multi-modal movie audio description pipeline that generates narrations of information that is not accessible through unimodal hearing in movies.
Outcome:	The proposed pipeline surpasses existing baselines in performance on widely used datasets.

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Xudong Shen, li Yuan, Ye Chen, Xin Wu, Yi Cai, Zhiyong Wu

Robustness in NLP

Challenge:	Prior work has focused on the ability of Large Language Models to identify or classify fallacies, but their robustness against these fallacias in persuasive contexts remains largely unexplored.
Approach:	They propose a new metric to assess LLM robustness against fallacies by pairing factual questions with fallacious arguments and developing a multi-round debate framework to assess model resilience.
Outcome:	The proposed metric disentangles robustness from a model’s knowledge limitations and demonstrates unique vulnerability profiles across models.

Schema-adaptable Knowledge Graph Construction (2023.findings-emnlp)

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Hongbin Ye, Honghao Gui, Xin Xu, Xi Chen, Huajun Chen, Ningyu Zhang

Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning (2025.acl-long)

Challenge:	Existing Knowledge Graph Construction (KGC) tasks rely on static information extraction with a closed set of pre-defined schemas.
Approach:	They propose a static knowledge Graph Construction task that extracts entity, relation, and event based on dynamically changing schema graph without retraining.
Outcome:	The proposed system outperforms existing methods but still has room for improvement . it can extract entity, relation, and event based on dynamically changing schema graph without re-training .

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Chenxi Huang, Shaotian Yan, Liang Xie, Binbin Lin, Sinan Fan, Yue Xin, Deng Cai, Chen Shen, Jieping Ye

Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions (N18-1)

Challenge:	Representation Fine-tuning (ReFT) is a proposed method for improving parameter efficiency . however, it yields suboptimal performance, as fixed-position representations have uncertain impact on outputs .
Approach:	They propose a method that fine-tunes critical representations in a low-rank linear subspace while freezing the base model.
Outcome:	The proposed method improves accuracy of LLaMA-2-7B and ReFT by 18.2 and 3.8 on GSM8K.

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Hai Ye, Xin Jiang, Zhunchen Luo, Wenhan Chao

Explainability & Interpretability in NLP

Challenge:	Existing work on court view generation from fact descriptions has improved the working efficiency of legal assistant systems.
Approach:	They propose to decode court views conditioned on encoded charge labels from the fact description in a criminal case to improve interpretability of charge prediction systems.
Outcome:	The proposed model can generate court views conditioned on encoded charge labels.

Conversational Education at Scale: A Multi-LLM Agent Workflow for Procedural Learning and Pedagogic Quality Assessment (2025.findings-emnlp)

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Jiahuan Pei, Fanghua Ye, Xin Sun, Wentao Deng, Koen Hindriks, Junxiao Wang

Dialogue Systems & Conversational Agents

Challenge:	Existing work on large language models lacks scalability and assesses pedagogic quality.
Approach:	They propose a multi-agent workflow leveraging large language models to simulate interactive teaching-learning conversations.
Outcome:	The proposed workflow integrates teacher and learner agents, an interaction manager, and an evaluator to facilitate procedural learning and assess pedagogic quality.

ValueBench: Towards Comprehensively Evaluating Value Orientations and Understanding of Large Language Models (2024.acl-long)

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Yuanyi Ren, Haoran Ye, Hanjun Fang, Xin Zhang, Guojie Song

DebugBench: Evaluating Debugging Capability of Large Language Models (2024.findings-acl)

Challenge:	Large Language Models (LLMs) are transforming diverse fields and gaining increasing influence as human proxies.
Approach:	They propose a psychometric evaluation pipeline grounded in realistic human-AI interactions to probe value orientations and novel tasks for evaluating value understanding in an open-ended value space.
Outcome:	The proposed evaluation pipeline is grounded in realistic human-AI interactions and performs tasks that approximate expert conclusions in value-related extraction and generation tasks.

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Runchu Tian, Yining Ye, Yujia Qin, Xin Cong, Yankai Lin, Yinxu Pan, Yesai Wu, Hui Haotian, Liu Weichuan, Zhiyuan Liu, Maosong Sun

Interpretable Rationale Augmented Charge Prediction System (C18-2)

Challenge:	Large language models (LLMs) have demonstrated exceptional coding capabilities, but their debugging capabilities remain relatively unexplored.
Approach:	They propose a debugging benchmark consisting of 4,253 LLMs with four major bug categories and 18 minor types in C++, Java, and Python.
Outcome:	The proposed benchmark covers four major bug categories and 18 minor types in C++, Java, and Python.

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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.

MNER-MI: A Multi-image Dataset for Multimodal Named Entity Recognition in Social Media (2024.lrec-main)

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Shizhou Huang, Bo Xu, Changqun Li, Jiabo Ye, Xin Lin

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

Challenge:	Recent research has focused on multimodal named entity recognition (MNER) but current approaches focus on text and a single accompanying image, leaving a significant research gap in multi-image scenarios.
Approach:	They propose to construct a human-annotated MNER dataset with multiple images called MNER-MI and a temporal prompt model with multiple image to address the new challenges in multi-image scenarios.
Outcome:	The proposed method achieves state-of-the-art results on both MNER-MI and MNER -MI-Plus, demonstrating its effectiveness.

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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

Robustness in NLP

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.

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

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

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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