Summary of Healthq: Unveiling Questioning Capabilities Of Llm Chains in Healthcare Conversations, by Ziyu Wang et al.
HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations
by Ziyu Wang, Hao Li, Di Huang, Hye-Sung Kim, Chae-Won Shin, Amir M. Rahmani
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The paper introduces HealthQ, a novel framework for evaluating the questioning capabilities of large language models (LLMs) in digital healthcare. It proposes advanced LLM chains, including Retrieval-Augmented Generation (RAG), Chain of Thought (CoT), and reflective chains, to elicit comprehensive and relevant patient information. The framework integrates an LLM judge to evaluate generated questions across metrics such as specificity, relevance, and usefulness, aligned with traditional Natural Language Processing (NLP) metrics like ROUGE and Named Entity Recognition (NER)-based set comparisons. The authors validate HealthQ using custom datasets constructed from public medical datasets, ChatDoctor and MTS-Dialog, and demonstrate its robustness across multiple LLM judge models, including GPT-3.5, GPT-4, and Claude. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to evaluate how well large language models (LLMs) can ask questions to help doctors take better care of patients. It’s like training a model to be a good doctor by teaching it to ask the right questions. The authors created a special framework called HealthQ that helps figure out if an LLM is asking good or bad questions. They tested this framework with two sets of medical data and found that it works well with different types of models. |
Keywords
» Artificial intelligence » Claude » Gpt » Named entity recognition » Natural language processing » Ner » Nlp » Rag » Retrieval augmented generation » Rouge
