Atomic fact-checking of LLM oncology recommendations increased clinician trust from 26.9% to 66.5% (Cohen's d=0.94) in a trial of 356 doctors.
Improving reliability and explainability of medical question answering through atomic fact checking in retrieval-augmented llms
2 Pith papers cite this work. Polarity classification is still indexing.
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A framework detects LLM anomalies including hallucinations, jailbreaks, and backdoors by forensic inspection of layer-wise hidden state patterns, reporting over 95% accuracy with minimal computational overhead.
citing papers explorer
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Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial
Atomic fact-checking of LLM oncology recommendations increased clinician trust from 26.9% to 66.5% (Cohen's d=0.94) in a trial of 356 doctors.
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Exposing the Ghost in the Transformer: Abnormal Detection for Large Language Models via Hidden State Forensics
A framework detects LLM anomalies including hallucinations, jailbreaks, and backdoors by forensic inspection of layer-wise hidden state patterns, reporting over 95% accuracy with minimal computational overhead.