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LLM extraction sensitivity to prompts, models, and schemas studied

By PulseAugur Editorial · [2 sources] ·

A new study published on arXiv investigates how sensitive large language models are to prompt, model, and schema choices when extracting structured data from clinical notes. Researchers found that while prompt variations had a moderate impact on categorization, the choice of model size significantly influenced the reassignment of primary admission tags. The study also revealed that collapsing the extraction schema to a binary format resolved most disagreements, highlighting that the distinction between absence and silence was a key factor in the model's interpretation. AI

IMPACT Highlights the need for careful configuration and validation of LLMs in sensitive domains like healthcare to ensure reliable data extraction.

RANK_REASON Academic paper detailing methodology and findings on LLM performance.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM extraction sensitivity to prompts, models, and schemas studied

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Martin Murin ·

    Measuring the sensitivity of LLM-based structured extraction to prompt, model, and schema choices in clinical discharge summaries

    arXiv:2606.05970v1 Announce Type: new Abstract: Large language models are increasingly used for structured extraction from clinical free-text notes, but the sensitivity of their output to upstream configuration choices is less understood than their accuracy on fixed benchmarks. T…

  2. arXiv cs.CL TIER_1 English(EN) · Martin Murin ·

    Measuring the sensitivity of LLM-based structured extraction to prompt, model, and schema choices in clinical discharge summaries

    Large language models are increasingly used for structured extraction from clinical free-text notes, but the sensitivity of their output to upstream configuration choices is less understood than their accuracy on fixed benchmarks. This work measures that sensitivity without human…

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