Small language models show logical and faithfulness errors in chain-of-thought explanations during acute respiratory failure phenotyping, and biased prompts shift their answers.
Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network
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Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks
Small language models show logical and faithfulness errors in chain-of-thought explanations during acute respiratory failure phenotyping, and biased prompts shift their answers.