Papers by Xiaoqing Li
FastMCTS: A Simple Sampling Strategy for Data Synthesis (2025.acl-long)
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| Challenge: | Existing methods for generating multi-step reasoning data rely on rejection sampling, which generates trajectories independently and suffers from inefficiency and imbalanced sampling across problems of varying difficulty levels. |
| Approach: | They propose a data synthesis strategy inspired by Monte Carlo Tree Search . it offers step-level evaluation signals and promotes balanced sampling . |
| Outcome: | Experiments show that FastMCTS generates 30% more correct reasoning paths than rejection sampling. |
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution (2021.emnlp-main)
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Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, Cho-Jui Hsieh
| Challenge: | Existing methods to defend against adversarial word-substitution attacks have not been evaluated or compared in a systematic manner. |
| Approach: | They propose to compare different defense methods under representative adversarial attacks . they propose a method that improves the robustness of neural text classifiers against such attacks a . |
| Outcome: | The proposed method improves robustness of neural text classifiers against such attacks by a significant margin. |
Aligning Large Language Models with Human Preferences through Representation Engineering (2024.acl-long)
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Wenhao Liu, Xiaohua Wang, Muling Wu, Tianlong Li, Changze Lv, Zixuan Ling, Zhu JianHao, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Existing methods for achieving this alignment involve employing reinforcement learning from human feedback (RLHF) Existing approaches involve using RLHF to fine-tune LLMs based on human labels . however, RLRF is susceptible to instability during fine- tuning and presents challenges in implementation. |
| Approach: | They propose to use reinforcement learning from human feedback to fine-tune large language models with human preferences to achieve precise control of model behavior. |
| Outcome: | Experiments show that RAHF can be used to capture and manipulate representations to align with a broad spectrum of human preferences or values rather than being confined to a single concept or function. |
Parameter Efficient Multi-task Fine-tuning by Learning to Transfer Token-wise Prompts (2023.findings-emnlp)
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Muling Wu, Wenhao Liu, Jianhan Xu, Changze Lv, Zixuan Ling, Tianlong Li, Longtao Huang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Prompt tuning has been proven to be successful on various tasks by incorporating a small number of trainable parameters while freezing large pre-trained language models. |
| Approach: | They propose a token-wise prompt tuning method that uses a bank of finer-grained soft prompt tokens to generate an instance-dependent prompt. |
| Outcome: | The proposed method performs far better than full parameter fine-tuned models and achieves state-of-the-art by tuning only 0.035% parameters on 14 datasets. |
Tell Me What You Don’t Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing (2025.findings-acl)
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Wenhao Liu, Siyu An, Junru Lu, Muling Wu, Tianlong Li, Xiaohua Wang, Changze Lv, Xiaoqing Zheng, Di Yin, Xing Sun, Xuanjing Huang
| Challenge: | Role-playing Agents (RPAs) struggle to recognize and respond to hard queries that conflict with their role-play knowledge. |
| Approach: | They propose a lightweight representation editing approach that conveniently shifts conflicting requests to the rejection region, thereby enhancing the model’s refusal accuracy. |
| Outcome: | The proposed model improves RPAs’ refusal ability of conflicting requests while maintaining their general role-playing capabilities. |
Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains. |
| Approach: | They propose several strategies for deploying RAG that balance performance and efficiency. |
| Outcome: | The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy. |
One Sentence One Model for Neural Machine Translation (L18-1)
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| Challenge: | Neural machine translation (NMT) is a new state of the art that can produce better results than traditional statistical machine translation. |
| Approach: | They propose a dynamic neural network which learns a general network as usual and fine-tunes it for each test sentence. |
| Outcome: | The proposed method improves translation performance when similar sentences are available. |
Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models (2021.acl-short)
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| Challenge: | Experimental results show that a sequence-to-sequence learning framework with neural networks can be effective for Chinese Spelling Correction (CSC) |
| Approach: | They propose a sequence-to-sequence learning framework with neural networks that generates more valuable training instances and adds task-specific examples to enhance the model. |
| Outcome: | The proposed method improves generalization and robustness of multiple CSC models across three datasets. |
CAMIEval: Enhancing NLG Evaluation through Multidimensional Comparative Instruction-Following Analysis (2025.naacl-long)
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| Challenge: | Evaluating the quality of texts generated by language models has always been a challenging task in natural language processing (NLP). |
| Approach: | They propose a multidimensional comparative evaluation method based on instruction-following that combines relevance, factuality, and adherence with a concrete Chain-of-Thoughts process to enhance the accuracy of evaluations. |
| Outcome: | The proposed method outperforms existing methods in correlation with human evaluations on two NLG evaluation benchmarks. |
Weight Perturbation as Defense against Adversarial Word Substitutions (2022.findings-emnlp)
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| Challenge: | Existence and pervasiveness of textual adversarial examples have raised serious concerns to security-critical applications. |
| Approach: | They propose to perform weight perturbations in the parameter space rather than the input feature space to improve adversarial robustness of NLP models. |
| Outcome: | The proposed method improves adversarial robustness of models by performing weight perturbations in the parameter space rather than the input feature space. |
FiNE: Filtering and Improving Noisy Data Elaborately with Large Language Models (2025.naacl-long)
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| Challenge: | Currently, there are two mainstream methods for improving data integrity: data filtering and data augmentation. |
| Approach: | They propose a method to improve data integrity by combining data filtering and data augmentation with LLMs. |
| Outcome: | The proposed method surpasses the open-source chat version on HalluQA by 8.45 on the open source version. |
From Scaffolding to Assimilation: Progressive Structural Internalization for Format-Constrained Creative Text Generation (2026.findings-acl)
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| Challenge: | Existing paradigms rely on unreliable prompting or rigid constrained decoding strategies to achieve aesthetic unity. |
| Approach: | They propose a framework to embed external constraints into the model’s intrinsic intuition and use it to generate open-ended creative texts. |
| Outcome: | The proposed framework surpasses baselines in both strict constraint adherence and literary aesthetics. |
UPLex: Fine-Grained Personality Control in Large Language Models via Unsupervised Lexical Modulation (2025.findings-emnlp)
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Tianlong Li, Wenhao Liu, Muling Wu, Shihan Dou, Zhenghua Wang, Changze Lv, Xiaohua Wang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Personality is a crucial factor that shapes human communication patterns, thereby regulating the personalities of large language models (LLMs). |
| Approach: | They propose a method that uses an Unsupervisedly-Built Personalized Lexicon (UPL) during the decoding phase to manipulate LLM’s personality traits. |
| Outcome: | The proposed method can modulate the personality expression of large language models by dynamically altering their predicted probability of upcoming words in a pluggable fashion. |
Altogether: Image Captioning via Re-aligning Alt-text (2024.emnlp-main)
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Hu Xu, Po-Yao Huang, Xiaoqing Tan, Ching-Feng Yeh, Jacob Kahn, Christine Jou, Gargi Ghosh, Omer Levy, Luke Zettlemoyer, Wen-tau Yih, Shang-Wen Li, Saining Xie, Christoph Feichtenhofer
| Challenge: | Existing captioning models ignore existing alt-text metadata and lack transparency if training data is unknown. |
| Approach: | They propose an approach to edit and re-align alt-texts associated with images using human annotation. |
| Outcome: | The proposed approach improves image captions and improves text-to-image generation and zero-shot image classification tasks. |
Advancing Parameter Efficiency in Fine-tuning via Representation Editing (2024.acl-long)
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Muling Wu, Wenhao Liu, Xiaohua Wang, Tianlong Li, Changze Lv, Zixuan Ling, Zhu JianHao, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Parameter Efficient Fine-Tuning (PEFT) has gained significant attention for its ability to achieve competitive results while updating only a small subset of trainable parameters. |
| Approach: | They propose a new approach to fine-tuning neural models that scales and biases the representation produced at each layer. |
| Outcome: | The proposed approach reduces the number of trainable parameters by a factor of 25,700 compared to full parameter fine-tuning and by . 32 compared with LoRA. |
Mitigating Position Bias in Transformers via Layer-Specific Positional Embedding Scaling (2026.findings-acl)
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Zhenghua Wang, Yiran Ding, Changze Lv, Yixin Wu, Tianlong Li, Zhibo Xu, Muling Wu, Tianyuan Shi, Shizheng Li, Qi Qian, Xuanjing Huang, Xiaoqing Zheng
| Challenge: | Existing methods to address the "lost-in-the-middle" problem suffer from high latency or suboptimal hand-crafted scaling strategies. |
| Approach: | They propose a layer-specific positional embedding scaling method that assigns distinct scaling factors to each layer. |
| Outcome: | Experiments show that the proposed method mitigates positional attention bias and delivers consistent improvements across multiple long-context benchmarks. |
Revisiting Jailbreaking for Large Language Models: A Representation Engineering Perspective (2025.coling-main)
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Tianlong Li, Zhenghua Wang, Wenhao Liu, Muling Wu, Shihan Dou, Changze Lv, Xiaohua Wang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Recent surge in jailbreaking attacks has revealed significant vulnerabilities in Large Language Models (LLMs) however, limited research into the underlying mechanisms that make LLMs vulnerable to such attacks has been conducted. |
| Approach: | They propose that LLMs' self-safeguarding capability is linked to specific activity patterns within their representation space. |
| Outcome: | The proposed models can be detected with a few pairs of contrastive queries, and the robustness can be manipulated by weakening or strengthening these patterns. |
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples (2022.findings-acl)
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| Challenge: | Existing defense methods improve the adversarial robustness by making models adapt to training set augmented with some adversarials. |
| Approach: | They propose to introduce a reweighting mechanism to calibrate the training distribution to obtain robust models. |
| Outcome: | The proposed method minimizes the loss of validation set mixed with clean examples and adversarial ones in an online learning manner. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
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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
| 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. |
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA (2024.findings-emnlp)
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Zhu JianHao, Changze Lv, Xiaohua Wang, Muling Wu, Wenhao Liu, Tianlong Li, Zixuan Ling, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Existing federated learning frameworks require substantial data and computational resources to develop large language models. |
| Approach: | They propose a method that distributes a quantized version of the model’s parameters during training and combine it with a popular fine-tuning method to significantly reduce communication costs. |
| Outcome: | The proposed method enables accurate estimations for parameter updates while preventing clients from accessing a model whose performance is comparable to the centrally hosted one. |