Papers by Chenwei Wu
LLM Sensitivity Evaluation Framework for Clinical Diagnosis (2025.coling-main)
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| Challenge: | Existing studies on the sensitivity of Large Language Models (LLMs) to irrelevant contexts neglect the importance of key information. |
| Approach: | They investigate the sensitivity of large language models to key medical information by introducing different perturbation strategies to investigate their sensitivity. |
| Outcome: | The proposed models are based on three LLMs, namely GPT-3.5, GPT-4, Gemini, Claude3 and LLaMA2-7b, and demonstrate their reliability and sensitivity to medical information. |
MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation (2025.acl-industry)
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Hsin-Ling Hsu, Cong-Tinh Dao, Luning Wang, Zitao Shuai, Thao Nguyen Minh Phan, Jun-En Ding, Chun-Chieh Liao, Pengfei Hu, Xiaoxue Han, Chih-Ho Hsu, Dongsheng Luo, Wen-Chih Peng, Feng Liu, Fang-Ming Hung, Chenwei Wu
| Challenge: | Existing systems focus primarily on assessment rather than treatment planning. |
| Approach: | They propose a framework that structures LLM reasoning to align with real-life workflows. |
| Outcome: | The proposed framework outperforms baseline approaches in assessment accuracy and treatment plan quality. |
Embedding and Gradient Say Wrong: A White-Box Method for Hallucination Detection (2024.emnlp-main)
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| Challenge: | Existing methods for hallucination detection have attracted more attention from the community. |
| Approach: | They propose to model the distributional distance between the regular conditional output and the unconditional output, which is generated without a given input text. |
| Outcome: | The proposed model achieves state-of-the-art on the hallucination benchmarks HADES and other datasets. |
Multi-grained Named Entity Recognition (P19-1)
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| Challenge: | Existing approaches treat Named Entity Recognition (NER) as a sequence labeling task. |
| Approach: | They propose a framework for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. |
| Outcome: | The proposed framework outperforms current state-of-the-art frameworks by 4.4% in terms of the F1 score among nested/non-overlapping NER tasks. |