Summary of Fine Tuning Large Language Models For Medicine: the Role and Importance Of Direct Preference Optimization, by Thomas Savage et al.
Fine Tuning Large Language Models for Medicine: The Role and Importance of Direct Preference Optimization
by Thomas Savage, Stephen Ma, Abdessalem Boukil, Vishwesh Patel, Ekanath Rangan, Ivan Lopez, Jonathan H Chen
First submitted to arxiv on: 19 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 In this investigation, researchers compared the performance of two popular Large Language Model (LLM) fine-tuning methods – Supervised Fine Tuning (SFT) and Direct Preference Optimization (DPO) – for five natural language tasks in medicine. The tasks included classification with text or numeric data, clinical reasoning, summarization, and clinical triage. Results showed that SFT alone was sufficient for classification tasks, but DPO improved performance on more complex tasks like clinical reasoning, summarization, and clinical triage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a study comparing two LLM fine-tuning methods, researchers found that Supervised Fine Tuning (SFT) worked well for simple tasks, while Direct Preference Optimization (DPO) helped with more complex ones. They looked at five tasks: classifying text or numbers, making clinical decisions, summarizing texts, and prioritizing patients. The study showed which method to use for each task. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Large language model » Optimization » Summarization » Supervised
