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Language models' confidence signals disagree, impacting reliability assessment

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the distinction between local and global confidence signals in autoregressive language models. The study found that these two measures, derived from the probability of the greedy-selected answer token versus the frequency of answers from repeated sampling, are weakly correlated and differ in their association with correctness. Global confidence showed a moderate link to accuracy, while local confidence had little correlation. The research also indicated that disagreements between these confidence signals can signal sampling instability in models, particularly on benchmarks like the ARC challenge. AI

IMPACT Highlights the need for careful interpretation of model confidence metrics, impacting how AI systems are evaluated and controlled.

RANK_REASON Research paper published on arXiv detailing findings about confidence signals in language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Language models' confidence signals disagree, impacting reliability assessment

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Research paper published on arXiv detailing findings about confidence signals in language models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Julio C. Amador Diaz Lopez ·

    When Confidence Signals Disagree: Local and Global Confidence in Autoregressive Language Models

    arXiv:2609.16933v1 Announce Type: cross Abstract: Modern predictive systems expose multiple quantities that are commonly interpreted as measures of confidence. However, these quantities can summarize different aspects of the predictive process. This distinction matters when confi…

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