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Using LLM outputs to enhance CL terms and structure #2081
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I think these LLM descriptions read great, but definitely need review by trained curators to avoid possible hallucinations and verify facts and references. Clearly, they provide excellent starting material for in depth descriptions of cell types as well as suggesting additional subclasses, as David points out. But human review is key, because published cell type descriptions will be the feedstock of future LLMs and we don't want to create a cycle of misinformation that becomes difficult to correct as it gets embedded in other resources and search engine indices. |
This issue has not seen any activity in the past 6 months; it will be closed automatically in one year from now if no action is taken. |
@Caroline-99 - had forgotten about this ticket. Has some relevant detail. Let's keep open for now. Working through this example, even with the limited tech stack we have, could be a useful exercise. We will also need a new epic with an outline roadmap, but I think that needs more discussion. |
New plan:
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Cell Guide has draft cell type descriptions from ChatGPT
e.g. Acinar Cell Of Salivary Gland
CL def: An acinar cell of salivary gland.
CL parent class def:
ChatGPT:
This clearly needs curating for accuracy and the addition of references and some details may be too human-specific for CL or may better belong in an extended knowledge graph, but reading it also immediately suggests some obvious improvements we can make.
One way we could home in on some references is via another LLM-based resource, perplexity:
https://www.perplexity.ai/search/f5e5f8a6-cf6e-4b67-b28b-320173bc0725?s=c
The references may not be ideal primary refs - but typically have references to good primary refs. This description also provides more detail on secretory mechanisms (which might suggest more GO links) as well as useful context and material for the structure and function of related cell types - the myoepithelial cells and duct cells of the salivary glands.
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