Papers by Xiaocheng Li
Adaptive Backtracking for Privacy Protection in Large Language Models (2026.findings-acl)
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| Challenge: | Existing privacy protection methods are prone to privacy leakage, but they are not effective in ensuring the privacy of users. |
| Approach: | They propose to capture latent leakage tendency of large language models during generation process and to construct a new benchmark for personal information. |
| Outcome: | The proposed method improves privacy by up to 14% over strong baselines against adversarial attacks, avoiding the degradation of response utility. |
Chinese Lexical Substitution: Dataset and Method (2023.emnlp-main)
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| Challenge: | Existing benchmarks for lexical substitution (LS) are limited and limited in coverage . despite extensive research on Lexical Substitution in various languages, there is limited evidence for LS in Chinese. |
| Approach: | They propose to use human and machine collaboration to construct a Chinese LS dataset . they combine four unsupervised LS methods to generate candidate substitutes . |
| Outcome: | The proposed method outperforms existing benchmarks on the Chinese lexical substitution task. |
HomoGraphAdapter: A Homogeneous Graph Neural Network as an Effective Adapter for Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Existing adaptation methods overlook structural knowledge between text and image modalities or create overly complex graphs containing redundant information for alignment. |
| Approach: | They propose a method to adapt visual models to downstream tasks using text and image modalities. |
| Outcome: | The proposed method improves classification accuracy by 1.51% for 1-shot and 0.74% for 16-shot on 11 datasets. |
Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering (2026.acl-long)
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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
| 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. |
CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention (2025.acl-long)
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Zekai Ye, Qiming Li, Xiaocheng Feng, Libo Qin, Yichong Huang, Baohang Li, Kui Jiang, Yang Xiang, Zhirui Zhang, Yunfei Lu, Duyu Tang, Dandan Tu, Bing Qin
| Challenge: | Large Vision-Language Models (LVLMs) have impressive multimodal abilities but remain prone to multilingual object hallucination. |
| Approach: | They propose a cross-lingual attention intervention method to mitigate multilingual object hallucination in LVLMs by aligning attention patterns. |
| Outcome: | The proposed method improves 13.56% (up to 30%) on the POPE and 21.75% on the hallucination subsets across languages. |
Aligning Translation-Specific Understanding to General Understanding in Large Language Models (2024.emnlp-main)
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| Challenge: | Large Language models (LLMs) have remarkable abilities in understanding complex texts . however, understanding misalignment leads to LLMs mistakenly translating complex concepts . |
| Approach: | They propose a translation process that aligns the translation-specific understanding with the general understanding to improve translation quality and reduce translation literalness. |
| Outcome: | The proposed translation process improves translation quality and reduces translation literalness by -25% -51%. |
EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents (2025.acl-long)
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Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, Heng Ji
| Challenge: | Existing language model agents excel in planning and reasoning, but lack creativity in unfamiliar environments. |
| Approach: | They propose a benchmark suite of room escape game environments to challenge agents with creative reasoning, unconventional tool use and iterative problem-solving to uncover implicit goals. |
| Outcome: | The proposed framework can perform with 40% fewer steps and hints and performs robustly across difficulty levels. |
Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models (2024.acl-long)
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Weihong Zhong, Xiaocheng Feng, Liang Zhao, Qiming Li, Lei Huang, Yuxuan Gu, Weitao Ma, Yuan Xu, Bing Qin
| Challenge: | Large Vision-Language Models (LVLMs) suffer from multimodal hallucinations . however, the generated hallucines could influence the models’ subsequent generation . |
| Approach: | They propose a framework to evaluate LVLMs' behaviors when encountering generated hallucinations and a method to revise the output distribution of LVLs with the one derived from the residual visual input. |
| Outcome: | The proposed framework reduces the performance of open-source LVLMs by 31%, indicating that they are prone to accept the generated hallucinations and make false claims that they would not have supported without distractions. |
SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment (2025.coling-main)
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Yuchun Fan, Yongyu Mu, YiLin Wang, Lei Huang, Junhao Ruan, Bei Li, Tong Xiao, Shujian Huang, Xiaocheng Feng, Jingbo Zhu
| Challenge: | Large language models (LLMs) have demonstrated significant improvements in reasoning abilities, but these improvements are primarily focused on English, leading to inferior performance in non-English scenarios. |
| Approach: | They propose a multilingual reasoning alignment approach that fine-tunes the layers responsible for multilingual comprehension in one stage. |
| Outcome: | The proposed method fine-tunes 6 of the 9 layers responsible for multilingual comprehension, while reducing training time by 4.1-11.9 compared to the two-stage method. |
Breaking Language Preference in Multilingual RAG via Language-Controllable Retrieval and Language-Agnostic Reasoning (2026.findings-acl)
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| Challenge: | Existing approaches to improve retrieval accuracy and generation quality of large language models suffer from language preference. |
| Approach: | They propose a framework that explicitly disentangles multilingual RAG into language-controllable retrieval and language-agnostic reasoning. |
| Outcome: | Experimental results show that the proposed approach outperforms baselines across multilingual benchmarks. |
One for All: Update Parameterized Knowledge Across Multiple Models with Once Edit (2025.acl-long)
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Weitao Ma, Xiyuan Du, Xiaocheng Feng, Lei Huang, Yichong Huang, Huiyi Zhang, Xiaoliang Yang, Baohang Li, Xiachong Feng, Ting Liu, Bing Qin
| Challenge: | Existing methods for modifying large language models focus on individual models, resulting in errors and hallucinations. |
| Approach: | They propose an ensemble-based approach that employs a plug-in model as the editing module and a dynamic weight mechanism to enhance its effectiveness. |
| Outcome: | The proposed approach outperforms existing methods while achieving superior editing efficiency. |
Towards Higher Pareto Frontier in Multilingual Machine Translation (2023.acl-long)
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| Challenge: | Existing Pareto optimization approaches are limited by the long-tailed distribution of multilingual corpora. |
| Approach: | They propose a Pareto mutual distillation framework that pushes the Paret frontier outwards rather than making trade-offs. |
| Outcome: | The proposed framework pushes the Pareto frontier outwards rather than making trade-offs, the authors show. |
Language-Specific Layer Matters: Efficient Multilingual Enhancement for Large Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Large vision-language models exhibit an imbalance in multilingual capabilities . |
| Approach: | They propose a training recipe that achieves efficient multilingual enhancement for LVLMs by Precise Language Specific layers fine-tuning. |
| Outcome: | The proposed training recipe achieves efficient multilingual enhancement for LVLMs by fine-tuning language specific layers. |
Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation (2026.acl-long)
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| Challenge: | Existing studies on large language models focus on literal-level translation quality, such as adequacy and fluency. |
| Approach: | They propose a Culture-Aware Novel-Driven Parallel Dataset for Machine Translation and a multi-dimensional evaluation framework for assessing cultural translation quality. |
| Outcome: | The proposed model improves evaluation reliability in LLM-as-a-judge scenarios under culture-aware constraints. |
DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction (2023.findings-emnlp)
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| Challenge: | Current Event Extraction methods focus on high-resource scenarios, which requires large amount of annotated data. |
| Approach: | They propose a demonstration-based learning paradigm for EE to fully use annotated data . they propose EE as a natural language generation task guided by schema-based prompts . |
| Outcome: | The proposed model outperforms current methods in low-resource scenarios. |
Learning Which Features Matter: RoBERTa Acquires a Preference for Linguistic Generalizations (Eventually) (2020.emnlp-main)
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| Challenge: | Pretraining on self-supervised linguistic tasks is effective for learning features helpful for language understanding, but it requires more data to learn to prefer linguistic generalizations over surface ones. |
| Approach: | They propose a set of 20 ambiguous binary classification tasks to test whether a pretrained model prefers linguistic or surface generalizations. |
| Outcome: | The proposed model can learn to represent linguistic features with little pretraining data, but requires far more data to learn to prefer linguistic generalizations over surface ones. |
Two-Stage Parameter Alignment for Multi-LoRA Merging in Large Language Models (2026.findings-acl)
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| Challenge: | Current general model merging methods are prone to parameter interference problems . a novel two-stage parameter alignment framework is proposed to address this problem . |
| Approach: | They propose a two-stage parameter alignment framework that integrates low-rank LoRAs . they propose to reduce the computational complexity of existing methods by preserving fine-grained functions . |
| Outcome: | The proposed framework exhibits greater robustness than other methods in high-rank and high-interference scenarios while preserving fine-grained functions. |
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. |
When Do You Need Billions of Words of Pretraining Data? (2021.acl-long)
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| Challenge: | Pretrained language models (LMs) are dominated by models that can encode billions of words. |
| Approach: | They use classifier probing, information-theoretic probing and unsupervised relative acceptability judgments to evaluate model ability. |
| Outcome: | The proposed models require only about 10M to 100M words to learn to encode most syntactic and semantic features. |
FroM: Frobenius Norm-Based Data-Free Adaptive Model Merging (2025.findings-emnlp)
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| Challenge: | a new adaptive merging method is proposed to improve fine-tuning performance . traditional methods often encounter task interference when merging full fine-uning models . |
| Approach: | They propose an adaptive merging method that directly measures model parameters using the Frobenius norm . |
| Outcome: | The proposed method outperforms baseline methods in various fine-tuning scenarios. |
MPR-GUI: Benchmarking and Enhancing Multilingual Perception and Reasoning in GUI Agents (2026.acl-long)
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Ruihan Chen, Qiming Li, Xiaocheng Feng, Weihong Zhong, Xiaoliang Yang, Yuxuan Gu, Zekun Zhou, Yunfei Lu, Haoyu Ren, Kun Chen, Dandan Tu, Bing Qin
| Challenge: | Existing GUI benchmarks lack fine-grained diagnostics to identify which capabilities lead to task failures. |
| Approach: | They propose a multilingual P R GUI Benchmark to assess LVLMs' language capabilities . they propose XLI to align non-English hidden states with English ones during inference . |
| Outcome: | The proposed benchmark reveals consistent gaps between English and non-English settings . it reduces the cross-lingual gaps with an average gain of 6.5% in non- English settings compared to static benchmarks . |