Papers by Lei Ye
NeurST: Neural Speech Translation Toolkit (2021.acl-demo)
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| Challenge: | a toolkit for speech translation is available for free and provides step-by-step recipes for feature extraction, data preprocessing, distributed training, and evaluation. |
| Approach: | They propose to use NeurST to facilitate speech translation research for NLP researchers . they show experimental results for different benchmark datasets which can be regarded as reliable baselines . |
| Outcome: | The proposed framework provides reliable benchmarks for speech translation research. |
IEPile: Unearthing Large Scale Schema-Conditioned Information Extraction Corpus (2024.acl-short)
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| Challenge: | Large Language Models exhibit a significant performance gap in Information Extraction (IE) high-quality instruction data is the vital key for enhancing LLMs' specific capabilities . |
| Approach: | They propose a bilingual (English and Chinese) IE instruction corpus that contains 0.32B tokens. |
| Outcome: | The proposed model improves the performance of LLMs for IE with zero-shot generalization. |
Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering (2025.acl-long)
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| Challenge: | Existing retrieval-augmented generation methods are insufficient for multi-hop question answering . however, they tend to generate hallucinations due to semantic mismatching . |
| Approach: | They propose to optimize question semantic space for dynamic retrieval-augmented multi-hop question answering by optimizing the semantic embeddings. |
| Outcome: | The proposed method outperforms existing RAG methods in both in- and out-of-domain settings. |
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)
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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
| 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. |
Cross-modal Contrastive Learning for Speech Translation (2022.naacl-main)
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| Challenge: | Existing approaches for speech translation focus on using additional data from MT and automatic speech recognition (ASR). |
| Approach: | They propose a cross-modal contrastive learning method for end-to-end speech-totext translation. |
| Outcome: | The proposed method outperforms existing methods on a popular benchmark MuST-C. |
Multi-Agent Collaboration via Cross-Team Orchestration (2025.findings-acl)
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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. |
TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System (2025.findings-naacl)
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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
| 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. |
Alleviating Hallucinations from Knowledge Misalignment in Large Language Models via Selective Abstention Learning (2025.acl-long)
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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
| 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. |
CAGenMol: Condition-Aware Diffusion Language Model for Goal-Directed Molecular Generation (2026.findings-acl)
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| Challenge: | Existing methods to optimize target-directed molecular generation fail to reconcile conflicting objectives without compromising structural validity. |
| Approach: | They propose a condition-aware discrete diffusion framework that allows for conditional denoising guided by heterogeneous structural and property signals. |
| Outcome: | The proposed framework improves on structure-conditioned, property-conditioned and dual-conditioned benchmarks in binding affinity, drug-likeness, and success rate. |
CogEmp:A Cognitive Empathy-Oriented Dialogue System for Structured Psychological Counseling (2026.findings-acl)
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| Challenge: | Existing models lack accurate modeling of cognitive empathy, especially the ability to understand users’ emotions and their underlying psychological causes. |
| Approach: | They propose a model tailored for the Chinese cultural context that integrates cognitive empathy into LLMs. |
| Outcome: | The proposed model outperforms existing models in key evaluation metrics, particularly in empathy, comprehensibility, and professionalism. |
R2AG: Incorporating Retrieval Information into Retrieval Augmented Generation (2024.findings-emnlp)
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| Challenge: | Existing approaches to augment large language models with external documents are lacking in the semantic gap between LLMs and retrievers due to differences in their training objectives and architectures. |
| Approach: | They propose to integrate R2AG into R2etrieval augmented generation framework by using a R2-Former to capture retrieval information. |
| Outcome: | The proposed framework fills the semantic gap between LLMs and retrievers due to differences in their training objectives and architectures. |
Tuning Less, Prompting More: In-Context Preference Learning Pipeline for Natural Language Transformation (2025.emnlp-main)
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| 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. |
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. |
SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)
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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
| 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. |
Prompt-fused Framework for Inductive Logical Query Answering (2024.lrec-main)
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| Challenge: | Existing methods for addressing logical queries on knowledge graphs neglect missing edges in KGs . Existing approaches focus on addressing missing edges, thereby neglecting the emergence of new entities . |
| Approach: | They propose a query-aware prompt-fused framework that addresses embedding of emerging entities . they propose to use a symbolic query to gather information relevant to the query . |
| Outcome: | The proposed framework addresses embedding of emerging entities through contextual information aggregation. |
BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation (RAG) extends the capabilities of large language models (LLMs) by providing access to external knowledge. |
| Approach: | They propose a framework that emulates human interactive reading through annotation and re-reading by integrating a thought bubble module that offloads internal cognition into external bookmark tokens, which are then annotated back into the context. |
| Outcome: | The proposed framework offloads internal cognition into external bookmark tokens, which are then annotated back into the context. |
T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback (2025.acl-long)
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Zehan Wang, Ke Lei, Chen Zhu, Jiawei Huang, Sashuai Zhou, Luping Liu, Xize Cheng, Shengpeng Ji, Zhenhui Ye, Tao Jin, Zhou Zhao
| Challenge: | Text-to-audio (T2A) models still struggle to satisfy human preferences for prompt-following and acoustic quality when generating complex multi-event audio. |
| Approach: | They propose to use AI feedback learning to enhance basic capabilities of text-to-audio models . they use a large audio preference dataset to evaluate the model's capabilities . |
| Outcome: | The proposed model improves in simple and complex scenarios with AI feedback learning. |
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. |
Nature-Inspired Population-Based Evolution of Large Language Models (2026.acl-long)
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| 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. |
Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)
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| 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 . |
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)
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Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun
| Challenge: | Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically. |
| Approach: | They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance. |
| Outcome: | The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs. |
x1: Learning to Think Adaptively Across Languages and Cultures (2026.findings-acl)
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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
| 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. |
DORA: A Dual-Objective Reinforcement Learning Framework for Effective and Efficient Multimodal Agentic Search (2026.acl-long)
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Guangming Qin, Yuhao Deng, Yukun Zhao, Zhenyang Li, Junfeng Wang, Dawei Yin, Ye Yuan, Guoren Wang, Yizhou Yan, Chengliang Chai, Lei Cao
| Challenge: | Existing methods to train large language models overlook quality of intermediate search results . existing methods often invoke search calls during reasoning, making inference inefficient . |
| Approach: | They propose a dual-objective reinforcement learning framework to improve search strategies of MLLMs . DORA outperforms state-of-the-art methods, achieving up to 8.4% higher accuracy . |
| Outcome: | The proposed model outperforms state-of-the-art methods while reducing search calls by 9.7%. |
LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement (2025.acl-long)
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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
| 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. |
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)
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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
| 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. |
Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play (2026.acl-long)
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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
| 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. |
Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling (2025.acl-long)
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Yang Yuguang, Yu Pan, Jixun Yao, Xiang Zhang, Jianhao Ye, Hongbin Zhou, Lei Xie, Lei Ma, Jianjun Zhao
| 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. |
A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement (2026.acl-long)
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Shengji Tang, Jianjian Cao, Weihao Lin, Jiale Hong, Bo Zhang, Shuyue Hu, Lei Bai, Tao Chen, Wanli Ouyang, Peng Ye
| Challenge: | Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks. |
| Approach: | They propose a Scalable Multi-LLM Collaboration System to coordinate multiple open-source LLMs. |
| Outcome: | The proposed system outperforms prevailing closed-source LLMs on eight mainstream benchmarks on multiple tasks. |
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
| 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. |
STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation (2022.acl-long)
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| 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. |
WACO: Word-Aligned Contrastive Learning for Speech Translation (2023.acl-long)
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| Challenge: | Existing ST methods perform poorly when only a limited amount of parallel data are available for training. |
| Approach: | They propose a Word-Aligned COntrastive learning method for low-resource speech-to-text translation that bridges word-level representations for both speech and text modalities via contrastive learning. |
| Outcome: | The proposed method outperforms the best baseline by 9+ BLEU points with only 1-hour parallel ST data. |
VLMInferSlow: Evaluating the Efficiency Robustness of Large Vision-Language Models as a Service (2025.acl-long)
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| Challenge: | Existing studies evaluate efficiency robustness of vision-language models under unrealistic assumptions, requiring access to model architecture and parameters. |
| Approach: | They propose a novel approach to evaluate VLM efficiency robustness in a realistic black-box setting. |
| Outcome: | The proposed approach generates adversarial images with imperceptible perturbations, increasing the computational cost by up to 128.47%. |
Towards Reverse Engineering of Language Models: A Survey (2025.findings-emnlp)
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| Challenge: | Due to the vast amounts of data and computational resources required for model development, protecting the model’s parameters and training data has become an urgent and crucial concern. |
| Approach: | They define "reverse engineering" techniques as attacks on large language models and provide an in-depth analysis of them. |
| Outcome: | The proposed attacks are described as “reverse engineering” techniques on LMs and provide an introduction to existing protective strategies. |
Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis (2025.coling-main)
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| 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. |
SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution (2026.findings-acl)
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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
| 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. |
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
| Challenge: | Existing frameworks for retrieval-augmented large language models (LLMs) are lacking in LFQA faithfulness testing. |
| Approach: | They propose a framework to teach retrieval-augmented large language models to explicitly discriminate between faithful and unfaithful generations. |
| Outcome: | The proposed framework outperforms GPT-4o in LFQA scenarios and outperformed existing benchmarks. |