Papers by Xiaochuan Liu
Select, Read, and Write: A Multi-Agent Framework of Full-Text-based Related Work Generation (2025.findings-acl)
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| Challenge: | Existing methods for related work generation (RWG) suffer from shallow comprehension due to taking the limited portions of references as input and isolated explanation for each reference due to ineffective capturing the relationships among them. |
| Approach: | They propose a multi-agent framework that takes the limited portions of references papers as input and isolates the relationships between them. |
| Outcome: | The proposed framework outperforms other selectors and improves reading order with constrains of the graph structure. |
DataSage: Multi-agent Collaboration for Insight Discovery with External Knowledge Retrieval, Multi-role Debating, and Multi-path Reasoning (2026.findings-acl)
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| Challenge: | Existing data insight agents fail to deliver satisfactory results due to insufficient utilization of domain knowledge, shallow analytical depth, and error-prone code generation. |
| Approach: | They propose a novel multi-agent framework that incorporates external knowledge retrieval to enrich the analytical context, a multi-role debating mechanism to simulate diverse analytical perspectives and deepen analytical depth, and multi-path reasoning to improve the accuracy of the generated code and insights. |
| Outcome: | Extensive experiments on InsightBench show that DataSage outperforms existing data insight agents across all difficulty levels, improving by 7.5% and 13.9% respectively in insight-level and summary-level metrics. |
Evaluating Robustness of Generative Search Engine on Adversarial Factoid Questions (2024.findings-acl)
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Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip Yu, Zhijiang Guo
| Challenge: | Existing large language models (LLMs)-backed generative search engines may not always be accurate. |
| Approach: | They propose to evaluate the robustness of retrieval-augmented generation in a realistic and high-risk setting where adversaries have only black-box system access. |
| Outcome: | The proposed model exhibits higher susceptibility to factual errors compared to LLMs without retrieval. |