Papers by Haixu Tang
LLMs as Lab Engineers: A Benchmark for Analytical Method Lifecycle Management (2026.findings-acl)
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Xiaoyi Chen, Mahsa Monshizadeh, Chaoqi Zhang, Jianjun Lang, Yang Wu, Genevieve Mortensen, Xiaozhong Liu, Haixu Tang
| Challenge: | General-purpose commercial models outperform domain-specialized ones, while RAG and reasoning significantly improve performance. |
| Approach: | They propose a benchmark to evaluate LLMs' capabilities in analytical chemistry scenarios. |
| Outcome: | The proposed framework outperforms existing benchmarks focused on factual knowledge and provides practical guidance for analytical chemistry challenges. |
Knowledge-Aware Co-Reasoning for Multidisciplinary Collaboration (2025.emnlp-main)
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| Challenge: | Existing multi-agent paradigms rely on prompt engineering and lack of knowledge integration. |
| Approach: | They propose a framework that integrates structured knowledge reasoning into multidisciplinary collaboration by using clinical knowledge graphs to guide dynamic discipline determination. |
| Outcome: | Extensive experiments on academic and real-world datasets demonstrate the effectiveness of the proposed framework. |
Hey, That’s My Data! Token-Only Dataset Inference in Large Language Models (2026.findings-acl)
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| Challenge: | Existing dataset inference methods require logit access, but many modern LLMs restrict such access. |
| Approach: | They propose a token-only dataset inference framework that allows models to overwrite prior knowledge when trained on new data. |
| Outcome: | The proposed framework overwrites prior knowledge when trained on new data. |
Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition (2026.findings-acl)
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| Challenge: | Cloud-hosted Large Language Models (LLMs) offer unmatched reasoning capabilities and dynamic knowledge, yet submitting raw queries to these services can expose sensitive user intent. |
| Approach: | They propose a framework that formulates the trade-off between knowledge utility and privacy as a strategic game. |
| Outcome: | The proposed framework reduces intent leakage while maintaining high-fidelity answer quality. |
STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion (2023.emnlp-main)
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Xurui Li, Yue Qin, Rui Zhu, Tianqianjin Lin, Yongming Fan, Yangyang Kang, Kaisong Song, Fubang Zhao, Changlong Sun, Haixu Tang, Xiaozhong Liu
| Challenge: | Commercial news provides rich semantics and timely information for automated financial risk detection. |
| Approach: | They propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph to endorse the risk detection enhancement. |
| Outcome: | The proposed model outperforms existing models in terms of generalization and semantics and annotation. |