Summary of Retrieval Augmented Thought Process For Private Data Handling in Healthcare, by Thomas Pouplin et al.
Retrieval Augmented Thought Process for Private Data Handling in Healthcare
by Thomas Pouplin, Hao Sun, Samuel Holt, Mihaela van der Schaar
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 Retrieval-Augmented Thought Process (RATP), a novel approach that enhances Large Language Models’ (LLMs) capabilities by formulating thought generation as a multiple-step decision process. RATP leverages Monte-Carlo Tree Search and learns a proxy reward function to optimize the thought process, achieving 35% additional accuracy on private electronic medical records dataset compared to in-context retrieval-augmented generation for question-answering tasks. By addressing concerns about data privacy and information retrieval robustness, RATP has the potential to significantly improve LLMs’ applications in healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can help doctors and patients understand a lot of medical information. But there’s a problem – people might be worried that their personal health information will get shared without permission. Another issue is that even when these models are connected to information retrieval, they’re not very good at finding the most up-to-date information. The researchers in this paper came up with a new way called Retrieval-Augmented Thought Process (RATP) to help fix these problems. It works by breaking down the process of thinking into smaller steps and using algorithms to optimize those steps. When tested on real medical records, RATP did much better than current methods at answering medical questions. |
Keywords
* Artificial intelligence * Question answering * Retrieval augmented generation
