Summary of K-qa: a Real-world Medical Q&a Benchmark, by Itay Manes et al.
K-QA: A Real-World Medical Q&A Benchmark
by Itay Manes, Naama Ronn, David Cohen, Ran Ilan Ber, Zehavi Horowitz-Kugler, Gabriel Stanovsky
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Human-Computer Interaction (cs.HC); 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 abstract presents a dataset called K-QA containing 1,212 patient questions from real-world conversations on an AI-driven clinical platform, K Health. The goal is to ensure the accuracy of responses from large language models (LLMs) in clinical settings. To address this challenge, the authors employ a panel of physicians to answer and decompose a subset of K-QA into self-contained statements. They also formulate two evaluation metrics: comprehensiveness, measuring essential clinical information, and hallucination rate, measuring contradictions between LLM answers and physician-curated responses. The authors evaluate state-of-the-art models using K-QA and find that in-context learning improves model comprehensiveness, while medically-oriented augmented retrieval reduces hallucinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about making sure AI language models give accurate answers in medical settings where mistakes can harm patients. To do this, the researchers created a dataset of real patient questions from a healthcare platform and had doctors answer some of those questions to create a benchmark. They also developed two ways to measure how well the models are doing: one checks if they’re giving enough important medical information, and the other sees how often their answers contradict what the doctors said. The results show that making the models learn in context helps them give more accurate answers, and using more medical information while searching for answers reduces mistakes. |
Keywords
* Artificial intelligence * Hallucination
