Chen et al. 2026 argue that keyword- and rule-based clinical text extraction breaks down on negation, temporal reasoning, and cross-paragraph dependencies, and that LLMs paired with retrieval augmentation and structured output constraints enable a "generation-as-structured-output" paradigm—though they hedge that RAG only "may improve" factual errors and inconsistencies.
E5 states keyword/rule-based approaches struggle with negation, temporal reasoning, and cross-paragraph dependencies and that LLMs with retrieval augmentation and structured output constraints enable the 'generation-as-structured-output' paradigm, and E6 hedges that RAG and structural output constraints 'may improve factual errors and inconsistencies.'
Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 1 Oct, 04:39
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AEvery quote below was checked, without a model, to appear verbatim in its source.
- 01
“they struggle with negation and uncertainty, temporal reasoning, and cross-paragraph dependencies, as well as variation in writing conventions across institutions and clinical services”
Full-text passage · no page number · evidence E5
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.
- 02
“retrieval-augmented generation (RAG) and structural output constraints (function calling/JSON schema), which may improve factual errors and inconsistencies in generated outputs”
Full-text passage · no page number · evidence E6
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.