Papers by Xuelong Li
Dual Prompt Tuning based Contrastive Learning for Hierarchical Text Classification (2024.findings-acl)
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| Challenge: | Existing methods focus on hierarchy-aware text feature by exploiting explicit parent-child relationships, resulting in label confusion within each layer. |
| Approach: | They propose a dual-prompt tuning method which emphasizes discrimination among peer labels by performing contrastive learning on each hierarchical layer. |
| Outcome: | The proposed method outperforms existing methods on benchmark datasets and is available on github. |
LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have a high vulnerability to jailbreak attacks that leverage crafted prompts to generate malicious outputs. |
| Approach: | They propose to use large language models to test their security against jailbreak attacks that leverage crafted prompts to generate malicious outputs. |
| Outcome: | The proposed model is based on 320 manually crafted malicious code generation requirements, covering 11 jailbreak methods and 29 code functionality categories. |
Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization (2026.findings-acl)
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Mingkuan Zhao, Wentao Hu, Tianchen Huang, Yuheng Min, Suquan Chen, Yide Gao, Yanbo Zhai, Shuangyong Song, Xuelong Li
| Challenge: | Hallucinations in Large Language Models persist in critical domains where generated content diverges from contextual facts or logical constraints. |
| Approach: | They propose to generate hallucinations as orthogonal noise relative to the semantic manifold of the residual stream. |
| Outcome: | The proposed method achieves superior contextual faithfulness compared to state-of-the-art methods. |
The Mark Fades: Adaptive Evolutionary Paraphrase-based Attack against LLM Watermarks (2026.findings-acl)
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| Challenge: | Existing paraphrase-based watermark removal methods struggle to balance efficacy with text quality. |
| Approach: | They propose a training-free evolutionary framework that models watermark removal as a constrained multi-objective optimization problem by using a Pseudo-Log-Likelihood-guided mutation to precisely target and modify watermark-carrying tokens. |
| Outcome: | The proposed method outperforms baseline methods on a Qwen3 series watermark scheme while maintaining high semantic fidelity. |
Awakening Dormant Experts:Counterfactual Routing to Mitigate MoE Hallucinations (2026.acl-long)
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Wentao Hu, Yanbo Zhai, Xiaohui Hu, Mingkuan Zhao, Shanhong yu, Xue Liu, Kaidong Yu, Shuangyong Song, Xuelong Li
| Challenge: | Sparse Mixture-of-Experts models are vulnerable to hallucinations, authors say . static Top-k routing leaves "specialist experts" under-prioritized for specific tokens . |
| Approach: | They propose a training-free inference framework to awaken dormant experts . they propose 'counterfactual routing' to shift computational resources from syntax-dominant to knowledge-intensive layers . |
| Outcome: | Experiments show that CoR improves factual accuracy by 3.1% without increasing the inference budget. |
Visual Attention Reasoning via Hierarchical Search and Self-Verification (2026.acl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) often hallucinate due to fragile, linear reasoning and weak visual grounding. |
| Approach: | They propose a framework that reformulates reasoning as a hierarchical search with self-verification and replaces linear Chain-of-Thought with a tree-search policy capable of backtracking to correct logical errors. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on hallucination and safety benchmarks. |
Table-R1: Region-based Reinforcement Learning for Table Understanding (2026.findings-acl)
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Zhenhe Wu, Jian Yang, Zhongjiang He, Changzai Pan, Jiaheng Liu, Xianjie Wu, Yu Zhao, Shuangyong Song, Yongxiang Li, Zhoujun Li, Xuelong Li
| Challenge: | Tables are a widely used data format that poses unique challenges for language models due to their structured row-column interactions. |
| Approach: | They propose a region-based reinforcement learning approach that integrates region evidence into reasoning steps. |
| Outcome: | The proposed method outperforms baseline models on three benchmark datasets and significantly reduces the reasoning token consumption by 67.5%. |
D-QRELO: Training- and Data-Free Delta Compression for Large Language Models via Quantization and Residual Low-Rank Approximation (2026.findings-acl)
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Junlin Li, Shuangyong Song, Guodong DU, Ngai Wong, Xuebo Liu, Yongxiang Li, Min Zhang, Jing Li, Xuelong Li
| Challenge: | Existing methods for fine-tuned large language models fail on fine-scale datasets . large data scale amplifies delta parameter magnitude, singular values, and entropy, causing compression errors. |
| Approach: | They propose a training- and data-free delta compression method that captures dominant delta structure and compensates residual low-rank approximation to recover fine-grained details from smaller residual error. |
| Outcome: | The proposed method outperforms existing methods on large-scale datasets on dense and MoE architectures. |
WebUIBench: A Comprehensive Benchmark for Evaluating Multimodal Large Language Models in WebUI-to-Code (2025.findings-acl)
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| Challenge: | Existing benchmarks for large language models focus on webpage generation outcomes. |
| Approach: | They propose a multi-view evaluation framework to evaluate MLLMs in four key areas: WebUI Perception, HTML Programming, WebUI-HTML Understanding, and WebUI to code. |
| Outcome: | The proposed framework evaluates MLLMs in four key areas: WebUI Perception, HTML Programming, WebUI-HTML Understanding, and WebUI to code. |
Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration (2025.findings-acl)
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| Challenge: | Existing methods for grounding large language models suffer from inefficient querying . Existing approaches that rely on physical verification or self-reflection suffer from excessive querying. |
| Approach: | They propose a framework that introduces Reinforced Advantage feedback for efficient self-refinement of plans. |
| Outcome: | The proposed framework surpasses baselines in success rate and significantly decreases interaction steps of agents and query rounds of LLMs. |
CEMT:Controllable Element-Oriented Machine Translation via Structured Linguistic Reasoning (2026.findings-acl)
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| Challenge: | Large Language Models suffer from paraphrasing errors, omissions, or hallucinations when input contains translation-specific elements that require strict preservation or controlled transformation. |
| Approach: | They propose a Controllable Element-Oriented Machine Translation framework that decomposes the translation process into a linguistically grounded analysis, strategy formulation, and final generation. |
| Outcome: | The proposed framework improves on the WMT23/24 Chinese–English benchmarks while significantly reducing element-level constraint violations. |
Logic-Regularized Verifier Elicits Reasoning from LLMs (2025.acl-long)
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| Challenge: | Typical verifiers require resource-intensive supervised dataset construction, which is costly and faces limitations in data diversity. |
| Approach: | They propose an unsupervised verifier regularized by logical rules that uses internal activations and logical constraints on multiple reasoning paths. |
| Outcome: | Experiments on 10 datasets show that the proposed verifier outperforms baselines and is comparable to the supervised verifier. |
Search to Pass Messages for Temporal Knowledge Graph Completion (2022.findings-emnlp)
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| Challenge: | Recent studies on missing facts in temporal knowledge graphs are based on hand-designed architectures and fail to explore the diverse topological and temporal properties of TKGs. |
| Approach: | They propose to use neural architecture search to design a data-specific message passing architecture for TKG completion. |
| Outcome: | The proposed architectures achieve the state-of-the-art performance on three benchmark datasets. |
T2R-BENCH: A Benchmark for Real World Table-to-Report Task (2025.emnlp-main)
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Jie Zhang, Changzai Pan, Sishi Xiong, Kaiwen Wei, Yu Zhao, Xiangyu Li, Jiaxin Peng, Xiaoyan Gu, Jian Yang, Wenhan Chang, Zhenhe Wu, Jiang Zhong, Shuangyong Song, Xuelong Li
| Challenge: | Existing table benchmarks lack the capacity to adequately assess the practical application of table reasoning in industrial applications. |
| Approach: | They propose a bilingual table-to-report task and a table-based benchmark to assess the quality of table reasoning. |
| Outcome: | The proposed task is based on a bilingual benchmark with 457 industrial tables and evaluation criteria to measure the quality of report generation. |
INT: Establishing Information Transfer for Multilingual Intent Detection and Slot Filling (2025.findings-acl)
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Di Wu, Liting Jiang, Bohui Mao, Hongyan Xie, Haoxiang Su, Zhongjiang He, Ruiyu Fang, Shuangyong Song, Hao Huang, Xuelong Li
| Challenge: | Existing studies struggle to achieve performance comparable to that on high-resource languages due to inherent linguistic diversity of multilingual SLU tasks. |
| Approach: | They propose a multilingual information transfer network to solve these challenges . they propose to reformulate SF as a span prediction problem and introduce a slot-matching attention mechanism to achieve slot alignment across languages. |
| Outcome: | The proposed model outperforms baseline models on the MASSIVE and MASSIV-UG datasets in overall accuracy across all languages. |
Improve LLM-as-a-Judge Ability as a General Ability (2025.emnlp-main)
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| Challenge: | Recent studies focus on generative judges, but only on their judge ability. |
| Approach: | They propose a method that leverages the generative and reasoning capabilities of large language models to evaluate LLM responses across diverse scenarios, providing accurate preference signals. |
| Outcome: | The proposed model performs on RewardBench with only 2% to 40% of the data required by other training frameworks. |