Summary of Domain-specific Pretraining Of Language Models: a Comparative Study in the Medical Field, by Tobias Kerner
Domain-Specific Pretraining of Language Models: A Comparative Study in the Medical Field
by Tobias Kerner
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 paper explores the use of language models (LLMs) for specific tasks within a single domain, requiring less general knowledge but more domain-specific expertise. The authors highlight the limitations of using large, state-of-the-art LLMs like GPT-4 or Claude-3-opus due to their size and proprietary nature. This can be a challenge when working with sensitive data. To address this issue, the paper focuses on domain-specific and mixed-domain pretraining as more efficient methods for developing specialized language models. The authors review related work in the medical field and compare benchmark results between specialized and general-purpose LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computers to help us do specific jobs better. Right now, we have big computers that are really good at many things, but they can’t be used locally because they’re so large and belong to someone else. This can be a problem when we need to work with private information. The authors want to find ways to make smaller computers that are just as good at specific tasks, using a technique called pretraining. They look at what other people have done in this area, specifically in medicine, and compare how well these small computers do compared to the big ones. |
Keywords
* Artificial intelligence * Claude * Gpt * Pretraining
