Summary of Curategpt: a Flexible Language-model Assisted Biocuration Tool, by Harry Caufield et al.
CurateGPT: A flexible language-model assisted biocuration tool
by Harry Caufield, Carlo Kroll, Shawn T O’Neil, Justin T Reese, Marcin P Joachimiak, Harshad Hegde, Nomi L Harris, Madan Krishnamurthy, James A McLaughlin, Damian Smedley, Melissa A Haendel, Peter N Robinson, Christopher J Mungall
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); 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 This paper addresses the pressing need for efficient and accurate biomedical discovery by developing a novel approach to data curation using generative AI. Current expert curators face significant time and resource constraints, as the rapid pace of new information being published daily exceeds their capacity for curation. The proposed design philosophy combines the emerging abilities of instruction-tuned large language models (LLMs) with more precise methods, enabling agents to perform reasoning, searching ontologies, and integrating knowledge across external sources. This streamlines the curation process, enhancing collaboration and efficiency in common workflows. The authors introduce CurateGPT, an LLM-driven annotation tool that melds the power of generative AI with trusted knowledge bases and literature sources. Compared to direct interaction with an LLM, CurateGPT’s agents enable access to information beyond that in the LLM’s training data and provide direct links to the data supporting each claim. This helps curators, researchers, and engineers scale up curation efforts to keep pace with the ever-increasing volume of scientific data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to help scientists organize and make sense of large amounts of biomedical research data. Right now, experts are spending a lot of time finding, sorting, and combining this information into something useful. But they’re getting overwhelmed by how fast new data is being published. The authors propose a new way to use AI to help with this process, making it faster and more accurate. They developed an AI tool called CurateGPT that uses large language models to search for relevant information and link it back to the original studies. |
