Summary of Closing the Gap Between Open-source and Commercial Large Language Models For Medical Evidence Summarization, by Gongbo Zhang et al.
Closing the gap between open-source and commercial large language models for medical evidence summarization
by Gongbo Zhang, Qiao Jin, Yiliang Zhou, Song Wang, Betina R. Idnay, Yiming Luo, Elizabeth Park, Jordan G. Nestor, Matthew E. Spotnitz, Ali Soroush, Thomas Campion, Zhiyong Lu, Chunhua Weng, Yifan Peng
First submitted to arxiv on: 25 Jul 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 This study focuses on fine-tuning open-source large language models (LLMs) to improve their performance in summarizing medical evidence. The authors use a benchmark dataset, MedReview, consisting of 8,161 pairs of systematic reviews and summaries. They fine-tune three open-sourced LLMs, PRIMERA, LongT5, and Llama-2, and evaluate their performance using ROUGE-L, METEOR, and CHRF scores. The results show an increase in performance for the fine-tuned models, with LongT5 achieving a score comparable to GPT-3.5 with zero-shot settings. Smaller fine-tuned models sometimes outperform larger zero-shot models. The study’s findings can guide model selection for tasks requiring specific domain knowledge, such as medical evidence summarization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research tries to make language models better at summarizing medical articles. Most previous studies used special language models that are not open-source. These special models work well but have some drawbacks, like being hard to customize and lacking transparency. The authors of this study wanted to see if they could improve the performance of open-source language models by fine-tuning them for medical evidence summarization. They tested three open-source models on a large dataset of medical articles and summaries. The results show that these fine-tuned models can summarize medical articles better than before, with one model performing almost as well as a top-performing zero-shot model. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Llama » Rouge » Summarization » Zero shot
