AutoPyVerifier learns compact sets of executable Python verifiers from labeled LLM outputs via LLM synthesis and DAG search, improving objective prediction by up to 55 F1 points and downstream LLM accuracy by up to 17 points.
Trustjudge: Inconsistencies of llm-as-a-judge and how to alleviate them
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Both humans and LLMs trust content more when labeled human-authored than AI-generated, with LLMs showing denser attention to labels and higher uncertainty under AI labels, mirroring human heuristic patterns.
LLM ranking reliability for prioritization tasks can be assessed via coefficient of consistency ζ (intra-run circular triads) and Kendall's τ (inter-run distance), with three leading models showing distinct consistency profiles on homelessness allocation and ED triage.
MedFabric dataset and EtHER detector achieve over 15% better word-level fabrication detection in medical LLMs than prior methods by generating stylistically faithful errors and using decomposition-based checking.
A survey on LLM-as-a-Judge that reviews reliability strategies, proposes evaluation methods, and introduces a novel benchmark for assessing such systems.
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AutoPyVerifier: Learning Compact Executable Verifiers for Large Language Model Outputs
AutoPyVerifier learns compact sets of executable Python verifiers from labeled LLM outputs via LLM synthesis and DAG search, improving objective prediction by up to 55 F1 points and downstream LLM accuracy by up to 17 points.
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Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge
Both humans and LLMs trust content more when labeled human-authored than AI-generated, with LLMs showing denser attention to labels and higher uncertainty under AI labels, mirroring human heuristic patterns.
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Can LLMs Rank? A Tale of Triads and Triage
LLM ranking reliability for prioritization tasks can be assessed via coefficient of consistency ζ (intra-run circular triads) and Kendall's τ (inter-run distance), with three leading models showing distinct consistency profiles on homelessness allocation and ED triage.
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MedFabric and EtHER: A Data-Centric Framework for Word-Level Fabrication Generation and Detection in Medical LLMs
MedFabric dataset and EtHER detector achieve over 15% better word-level fabrication detection in medical LLMs than prior methods by generating stylistically faithful errors and using decomposition-based checking.
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A Survey on LLM-as-a-Judge
A survey on LLM-as-a-Judge that reviews reliability strategies, proposes evaluation methods, and introduces a novel benchmark for assessing such systems.