Summary of Rulealign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment, by Xiaohan Wang et al.
RuleAlign: Making Large Language Models Better Physicians with Diagnostic Rule Alignment
by Xiaohan Wang, Xiaoyan Yang, Yuqi Zhu, Yue Shen, Jian Wang, Peng Wei, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Large Language Models (LLMs) like GPT-4, MedPaLM-2, and Med-Gemini have achieved competitive performance with human experts across various medical benchmarks. However, they still struggle to make professional diagnoses akin to physicians, particularly in gathering patient information and reasoning the final diagnosis efficiently. To address this challenge, we introduce the RuleAlign framework, designed to align LLMs with specific diagnostic rules. We develop a medical dialogue dataset comprising rule-based communications between patients and physicians and design an alignment learning approach through preference learning. Experimental results demonstrate the effectiveness of our proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about improving computers that can talk like doctors. Right now, these “AI doctors” are good at answering questions, but they’re not as good at making diagnoses. The problem is that AI doctors need to understand what patients are saying and then figure out the correct diagnosis. To fix this, we created a special way for AI doctors to learn how to follow rules used by real doctors. We made a big database of conversations between patients and doctors and taught our AI doctor to understand these conversations and make good diagnoses. Our results show that our approach works! |
Keywords
» Artificial intelligence » Alignment » Gemini » Gpt
