Papers by Xinzhe Li
Can Pretrained Language Models Derive Correct Semantics from Corrupt Subwords under Noise? (2023.starsem-1)
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| Challenge: | Existing studies have shown that Pretrained Language Models (PLMs) perform poorly under noise due to subword segmentation. |
| Approach: | They propose a framework for subword segmentation that provides a systematic categorization of segmentation corruption under noise and evaluation protocols by generating contrastive datasets with canonical-noisy word pairs. |
| Outcome: | The proposed framework provides a systematic categorization of segmentation corruption under noise and evaluation protocols by generating contrastive datasets with canonical-noisy word pairs. |
Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System (2025.acl-long)
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Haoyang Su, Renqi Chen, Shixiang Tang, Zhenfei Yin, Xinzhe Zheng, Jinzhe Li, Biqing Qi, Qi Wu, Hui Li, Wanli Ouyang, Philip Torr, Bowen Zhou, Nanqing Dong
| Challenge: | Recent AI methods have shown promise in tasks such as hypothesis generation and experimental design, but they fail to replicate the collaborative nature of real-world scientific practices. |
| Approach: | They propose a virtual scientific system that mimics the collaborative nature of scientific research by organizing a team of agents to generate, evaluate, and refine research ideas. |
| Outcome: | The proposed system outperforms the state-of-the-art method in producing new scientific ideas and offers valuable insights to guide future research. |
A Review of Prominent Paradigms for LLM-Based Agents: Tool Use, Planning (Including RAG), and Feedback Learning (2025.coling-main)
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| Challenge: | Large Language Models have been used for planning, tool use, and feedback learning . inconsistent taxonomy and complexity of workflows create challenges . |
| Approach: | They propose a unified taxonomy to review and discuss the three paradigms . they define environments/tasks, common LLM-profiled roles and universally applicable workflows based on prior work . |
| Outcome: | The proposed taxonomy compares LMPR implementations and workflow usage across paradigms . large language models have human-like reasoning capabilities, the authors say . |
LiTS: A Modular Framework for LLM Tree Search (2026.acl-demo)
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| Challenge: | Existing tree search methods are task-specific and require substantial reimplementation effort when adapting to new domains. |
| Approach: | They propose a Python framework for LLM reasoning via tree search that decomposes tree search into three reusable components that plug into algorithms like MCTS and BFS. |
| Outcome: | The proposed framework decomposes tree search into three reusable components that plug into algorithms like MCTS and BFS. |
Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search (2026.findings-acl)
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| Challenge: | Large language models excel at tasks such as mathematical and commonsense reasoning, but their performance improves further when additional test-time compute is allocated. |
| Approach: | They propose a plug-in framework that decides when to branch during search instead of expanding at every step. |
| Outcome: | The proposed framework reduces token generation, model calls, and runtime by 75-85% on GSM8K and Math500, with negligible or no accuracy loss. |