Papers by Jindi Yu
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination (2023.findings-emnlp)
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| Challenge: | Existing explanation datasets for large language models are limited to the English language and general domain, leading to a scarcity of linguistic diversity and a lack of resources in specialized domains, such as medical. |
| Approach: | They propose to use a medical dataset to assess the interpretability of Large Language Models (LLMs) . they propose to analyze medical text and generate rationales for their decisions . |
| Outcome: | The proposed model passes the pharmacist examination with a 75.7% accuracy, while other models like ChatGPT fail. |
Medico: Towards Hallucination Detection and Correction with Multi-source Evidence Fusion (2024.emnlp-demo)
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| Challenge: | Existing studies show that LLMs can confidently state non-existent facts rather than answering "I don't know". |
| Approach: | They propose a multi-source evidence fusion enhanced hallucination detection and correction framework that fuses evidence from multiple sources and iteratively revises the hallucinous content. |
| Outcome: | The proposed framework detects whether the generated content contains factual errors, provides the rationale behind the judgment, and iteratively revises the hallucinated content. |