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by chaxor
1114 days ago
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NER, Entity linking, and relationship extraction definitely seem to be 'low hanging fruit' due to LLM improvements, but one of the big problems is that they really need a completely different architecture to limit the decoder vocab if using a decoder transformer for producing the set of sequences in relation extraction with specific entity ids.
A Longformer with full attention to input sequence, and sliding window attention to a large dictionary could be a decent way to find tune a system like this, but there are few that try it.
Unfortunately there's a lot of stupidity going around right now in thinking the answer is just to 'pRoOoMpT tHe LLm RiGhT', but that will always be exceedingly wasteful such that processing terabytes of files will be prohibitively expensive, and there's no guarantee the system will always restrict to the specific vocab and structure desired. The images in radiology definitely make these types of things harder, and the sparsity is an enormous issue. However, working with some projects in this area, I don't think it's as impenetrable as a lot of radiologists in AI suggest. The main thing needed in the field is adoption of better techniques and architectures to deal with these problems. |
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> Unfortunately there's a lot of stupidity going around right now in thinking the answer is just to 'pRoOoMpT tHe LLm RiGhT'
I agree with that this is not the right approach despite all the media hype, my research has been (more or less) attempting what you've proposed.
> A Longformer with full attention to input sequence, and sliding window attention to a large dictionary could be a decent way to find tune a system like this, but there are few that try it.
Good idea, although I'm biased as we tried this ourselves! Problem is the dictionary (ontology) doesn't exist. RadLex and UMLS are far too inadequate in coverage. Actively working to address the gaps and hope to have something to open-source within the next couple of months.