Summary of Collectivesft: Scaling Large Language Models For Chinese Medical Benchmark with Collective Instructions in Healthcare, by Jingwei Zhu et al.
CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare
by Jingwei Zhu, Minghuan Tan, Min Yang, Ruixue Li, Hamid Alinejad-Rokny
First submitted to arxiv on: 29 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 The study presents a comprehensive benchmark in Chinese, dubbed the Comprehensive Medical Benchmark (CMB), to evaluate the capabilities of Large Language Models (LLMs) in medical scenarios. The authors demonstrate that dataset diversity and distribution can significantly enhance LLM performance through supervised fine-tuning (SFT). They successfully train a smaller base model, achieving scores comparable to larger models, indicating that well-curated datasets can optimize performance regardless of model size. The study highlights the importance of dataset quality and diversity in fine-tuning processes, suggesting that even smaller models can reach high performance levels with carefully curated datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how Large Language Models (LLMs) perform on medical tasks in Chinese. They create a special test to see how well these models do on different types of medical problems. The researchers find that using a variety of medical information makes the LLMs better at solving different medical problems. This means that even smaller language models can do well if they’re trained with a lot of different medical information. |
Keywords
» Artificial intelligence » Fine tuning » Supervised
