Papers by Lei Xue
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning (2026.acl-long)
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Zekai Lin, Chao Xue, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Lei Jiang, Yu Lu, Bob Simons, Shuang Liang, Minlong Peng
| Challenge: | Recent approaches to fine-tuning of large language models suffer from task interference and catastrophic forgetting. |
| Approach: | They propose a fine-tuning framework that adapts isolation decisions based on online estimates of parameter importance. |
| Outcome: | The proposed framework reduces interference and forgetting while releasing outdated parameters to recover plasticity. |
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)
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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Incomplete learning is widespread and heterogeneous in large language models . authors identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Approach: | They propose a diagnostic-first framework that maps incomplete learning to causes . they identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between supervision and pre-training knowledge, internal inconsistencies, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Outcome: | The proposed framework maps incomplete learning to causes using observable training and inference signals. |
Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation (2023.emnlp-main)
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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Jie Fu, Jian Lan, Mei Li, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv
| Challenge: | Existing unsupervised paraphrase generation methods require large-scale, manually annotated paraphrase datasets, which are labor-intensive to build. |
| Approach: | They propose a self-supervised pseudo-data construction method that generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
| Outcome: | The proposed method generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
Attention Consistency for LLMs Explanation (2025.findings-emnlp)
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| Challenge: | Existing interpretability methods face limitations such as low resolution and high computational cost. |
| Approach: | They propose a multi-layer attention consistency score to estimate the importance of input tokens in large language models. |
| Outcome: | The proposed heuristic achieves a favorable trade-off between interpretability quality and computational efficiency . |
Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026.findings-acl)
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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Existing approaches to generating reward models rely on voting-based mechanisms to evaluate CoT outputs. |
| Approach: | They propose an efficient generative reward modeling framework grounded in model-internal uncertainty. |
| Outcome: | The proposed framework reduces inference cost while improving answer accuracy. |
GCPG: A General Framework for Controllable Paraphrase Generation (2022.findings-acl)
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Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Haibo Zhang, Xue Zhao, Wenqing Yao, Boxing Chen
| Challenge: | Existing studies highlight a special condition under two indispensable aspects of controllable paraphrase generation (CPG) individually, lacking a unified circumstance to explore and analyze their effectiveness. |
| Approach: | They propose a general controllable paraphrase generation framework that integrates lexical and syntactical conditions into a text sequence and uniformly processes them in an encoder-decoder paradigm. |
| Outcome: | The proposed framework can combine lexical and syntactical conditions and improve paraphrase generation. |
Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach (2020.findings-emnlp)
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| Challenge: | Generating natural language under complex constraints is a principled formulation towards controllable text generation. |
| Approach: | They propose a method to specify combinatorial constraints for sentence generation . they use a tree search algorithm embedded into the proposal process of the Markov Chain Monte Carlo . |
| Outcome: | The proposed method achieves consistent and significant improvement on multiple language generation tasks. |
Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation (2023.acl-long)
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| Challenge: | Existing work focuses on generating sentences satisfying pre-specified attributes such as topic and sentiment, yet suffers from increases in storage and inference time. |
| Approach: | They propose a method that uses a pre-trained continuous vector to generate a fixed pre-trainable language model to satisfy a specified attribute. |
| Outcome: | The proposed model can achieve improvements on eleven attribute-specific generation tasks with 0.08% extra training parameters. |
ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks (2025.emnlp-main)
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| Challenge: | Multi-agent systems (MAS) are limited by poor flexibility and scalability, with underdeveloped optimization strategies. |
| Approach: | They propose a task graph generation and a reward-driven two-stage agent selection process to integrate multi-agent systems to improve their reasoning capabilities. |
| Outcome: | The proposed model outperforms existing methods on Math-MAS and SciBench-MAS SciBech, while other methods completely fail. |
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. |
Dynamic Voting for Efficient Reasoning in Large Language Models (2023.findings-emnlp)
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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Xingzhang Ren, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv
| Challenge: | Multi-path voting methods generate multiple reasoning paths for each problem, causing factual errors and illusion generation. |
| Approach: | They propose a multi-path voting technique that effectively reduces the number of reasoning paths during multi-path voting while preserving accuracies. |
| Outcome: | The proposed method outperforms Self-consistency using 24.7% of the number of paths on the LetterConcat task. |
CKDST: Comprehensively and Effectively Distill Knowledge from Machine Translation to End-to-End Speech Translation (2023.findings-acl)
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| Challenge: | End-to-end speech-totext translation (ST) data are limited due to the limited resources. |
| Approach: | They propose a knowledge distillation framework for speech translation that integrates knowledge from machine translation and decouples knowledge from non-target class knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art models on a benchmark dataset. |
WenetSpeech-Wu: Datasets, Benchmarks, and Models for a Unified Chinese Wu Dialect Speech Processing Ecosystem (2026.findings-acl)
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Chengyou Wang, Mingchen Shao, Jingbin Hu, Zeyu Zhu, Hongfei Xue, Bingshen Mu, Xin Xu, Xingyi Duan, Binbin Zhang, Zhu Pengcheng, Chuang Ding, Xiaojun Zhang, Hui Bu, Lei Xie
| Challenge: | despite its linguistic significance, the Wu dialect of Chinese has long been hindered by the lack of large-scale speech data, standardized evaluation benchmarks, and publicly available models. |
| Approach: | They propose to use WenetSpeech-Wu as a large-scale, multi-dimensionally annotated open-source speech corpus for the Wu dialect of Chinese. |
| Outcome: | The proposed dataset includes 8,000 hours of speech data and strong open-source models . the proposed dataset is competitive and empirically validated . |
AtTGen: Attribute Tree Generation for Real-World Attribute Joint Extraction (2023.acl-long)
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| Challenge: | Attribute extraction aims to identify attribute names and the corresponding attribute values from descriptive texts. |
| Approach: | They propose a unified formulation for real-world attribute extraction application, where closed-world, open-world and semi-open attribute extraction tasks are modeled uniformly. |
| Outcome: | The proposed model outperforms existing methods on three datasets and outperformed existing methods by a large margin. |
BayesKD: Bayesian Knowledge Distillation for Compact LLMs in Constrained Fine-tuning Scenarios (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have revolutionized various domains with their remarkable capabilities, but their massive parameter sizes pose significant challenges for fine-tuning and inference. |
| Approach: | They propose a Bayesian Knowledge Distillation framework for compact Large Language Models in resource-constrained fine-tuning scenarios that employs Logits Dual-Scaling, Knowledge Alignment Module, and Bayes Distillations Optimization. |
| Outcome: | The proposed framework outperforms baseline methods on various state-of-the-art LLMs, including LLaMA, Qwen2, Bloom, and Vicuna. |
How Good Are LLMs at Out-of-Distribution Detection? (2024.lrec-main)
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| Challenge: | Out-of-distribution (OOD) detection is crucial for ensuring AI safety . large language models (LLMs) are becoming more prevalent due to their scale, pre-training objectives, and paradigms used for inference. |
| Approach: | They propose to use large language models to investigate out-of-distribution (OOD) detection in machine learning. |
| Outcome: | The proposed method outperforms other OOD detectors in zero-grad and fine-tuning scenarios. |
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning (2024.naacl-long)
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| Challenge: | Prompt tuning on a few data samples presents security issues, e.g., Trojan attacks. |
| Approach: | They propose a method to transfer established data poisoning attacks directly to few-shot prompt tuning, a technique to address the poisoned imbalance issue. |
| Outcome: | The proposed method achieves an ASR of over 99% while maintaining negligible decreases in CDA. |
Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning (2026.acl-long)
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Zelin Tan, Hejia Geng, Xiaohang Yu, Mulei Zhang, Guancheng Wan, Yifan Zhou, Qiang He, Xiangyuan Xue, Heng Zhou, Yutao Fan, Zhong-Zhi Li, Zaibin Zhang, Guibin Zhang, Chen Zhang, Zhenfei Yin, Philip Torr, Lei Bai
| Challenge: | elucidating scaling laws for large language models (LLMs) during pre-training remains unexplored. |
| Approach: | They characterize how model scale, data, and compute interact during pre-training . they find that large models consistently demonstrate superior compute and data efficiency . |
| Outcome: | The proposed scaling laws offer practical guidance for scaling reasoning capabilities through reinforcement learning post-training. |
Self-attention-based Graph-of-Thought for Math Problem Solving (2025.findings-acl)
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| Challenge: | Existing methods for generating reasoning paths in a chain structure are inefficient and non-human-like. |
| Approach: | They propose a decoding method for a chain-based LLM that constructs a thought graph simultaneously as an LLM inference and generates reasoning steps with a graph-structured self-attention mechanism. |
| Outcome: | The proposed method improves reasoning accuracy without huge computational over-expensive LLMs and avoids performance degradation issues when the LLM is too small to comprehend complex prompts. |
Unified Hallucination Detection for Multimodal Large Language Models (2024.acl-long)
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Xiang Chen, Chenxi Wang, Yida Xue, Ningyu Zhang, Xiaoyan Yang, Qiang Li, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen
| Challenge: | despite significant strides in multimodal tasks, MLLMs are plagued by the critical issue of hallucination. |
| Approach: | They propose a meta-evaluation benchmark to facilitate evaluation of advancements in hallucination detection methods. |
| Outcome: | The proposed framework validates hallucinations robustly and provides strategic insights . MHaluBench is a meta-evaluation benchmark designed to facilitate evaluation . |
Beyond Layout Embedding: Layout Attention with Gaussian Biases for Structured Document Understanding (2023.findings-emnlp)
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| Challenge: | Existing methods for encoding layout information rely on millions of learnable parameters . polar coordinates provide superior choice for layout modeling, study finds . |
| Approach: | They propose to model layout attention with Gaussian biases by feeding polar coordinates into 2-D Gausssian kernels. |
| Outcome: | The proposed model improves on three widely used benchmarks. |