Summary of Bmretriever: Tuning Large Language Models As Better Biomedical Text Retrievers, by Ran Xu et al.
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers
by Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May D. Wang, Joyce C. Ho, Chao Zhang, Carl Yang
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Quantitative Methods (q-bio.QM)
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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 BMRetriever is a series of dense retrievers designed for enhancing biomedical retrieval. The model uses unsupervised pre-training on large biomedical corpora, followed by instruction fine-tuning on labeled datasets and synthetic pairs. This approach enables BMRetriever to excel at various biomedical applications, such as 5 tasks across 11 datasets. Notably, the 410M variant outperforms larger models up to 11.7 times, while the 2B variant matches the performance of models with over 5B parameters. The model’s parameter efficiency and strong performance make it an attractive solution for biomedical information retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BMRetriever is a new way to help computers understand medical texts better. It uses a big collection of medical papers to learn, then gets even better by fine-tuning on specific tasks like identifying diseases or finding relevant research. The model does really well on many different biomedical applications, which are important for things like disease diagnosis and treatment. One cool thing is that BMRetriever can be more effective than bigger models, which means it could be used in situations where big computers aren’t available. |
Keywords
» Artificial intelligence » Fine tuning » Unsupervised
