Summary of Medhalu: Hallucinations in Responses to Healthcare Queries by Large Language Models, By Vibhor Agarwal et al.
MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Models
by Vibhor Agarwal, Yiqiao Jin, Mohit Chandra, Munmun De Choudhury, Srijan Kumar, Nishanth Sastry
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have impressive capabilities in understanding and generating human language, but they are not immune to hallucinations – generating plausible-sounding yet factually incorrect information. As chatbots empowered by LLMs become popular, users may ask health-related queries, risking exposure to these hallucinations, which can have societal and healthcare implications. This work investigates hallucinations in LLM-generated responses to real-world healthcare queries from patients. The authors propose MedHalu, a medical hallucination dataset with diverse health-related topics and corresponding hallucinated responses from LLMs. They also introduce the MedHaluDetect framework for evaluating LLM capabilities in detecting hallucinations. Evaluators – medical experts, LLMs, and laypeople – were used to study who is more vulnerable to these medical hallucinations. The results show that LLMs are worse at detecting hallucinations than experts, performing similarly or even worse than laypeople. To improve hallucination detection, the authors propose an expert-in-the-loop approach by infusing expert reasoning into LLMs. The approach leads to significant performance gains for all LLMs, with a 6.3 percentage point average macro-F1 improvement for GPT-4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can sometimes make up false information that sounds real. This is bad news because chatbots using these models might give you wrong answers when you ask health questions. Researchers looked into this problem and made a special dataset called MedHalu with fake medical responses from large language models. They also created a tool to test how well these models can spot their own mistakes. The results showed that the models are actually pretty bad at correcting themselves, just like regular people. To fix this, the researchers suggested letting doctors review what the models say before sharing it with others. |
Keywords
» Artificial intelligence » Gpt » Hallucination
