Entropy Sentinel: Continuous LLM Accuracy Monitoring from Decoding Entropy Traces in STEM
Deploying LLMs raises two coupled challenges: (1) monitoring - estimating where a model underperforms as traffic and domains drift - and (2) improvement - prioritizing data acquisition to close the largest performance gaps. We test whether an inference-time signal can estimate slice-level accuracy under domain shift. For each response, we compute an output-entropy profile from final-layer next-token probabilities (from top-k logprobs) and summarize it with eleven statistics. A lightweight classifier predicts instance correctness, and averaging predicted probabilities yields a domain-level accuracy estimate. We evaluate on ten STEM reasoning benchmarks with exhaustive train/test compositions (k in {1,2,3,4}; all "10 choose k" combinations), across nine LLMs from six families (3B-20B). Estimates often track held-out benchmark accuracy, and several models show near-monotonic ordering of domains. Output-entropy profiles are thus an accessible signal for scalable monitoring and for targeting data acquisition.
696e27193f1837bfb8970b8e
Pedro Memoli Buffa
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2026-01-19T14:55:14.674Z
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Pedro Memoli Buffa
PMemoli
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PMemoli
696e27193f1837bfb8970b8f
Luciano Del Corro
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2026-01-19T12:58:24.541Z
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Luciano Del Corro
lucianodelcorro
user
lucianodelcorro
Entropy Sentinel: Continuous LLM Accuracy Monitoring from Decoding Entropy Traces in STEM
Deploying LLMs raises two coupled challenges: (1) monitoring - estimating where a model underperforms as traffic and domains drift - and (2) improvement - prioritizing data acquisition to close the largest performance gaps. We test whether an inference-time signal can estimate slice-level accuracy under domain shift. For each response, we compute an output-entropy profile from final-layer next-token probabilities (from top-k logprobs) and summarize it with eleven statistics. A lightweight classifier predicts instance correctness, and averaging predicted probabilities yields a domain-level accuracy estimate. We evaluate on ten STEM reasoning benchmarks with exhaustive train/test compositions (k in {1,2,3,4}; all "10 choose k" combinations), across nine LLMs from six families (3B-20B). Estimates often track held-out benchmark accuracy, and several models show near-monotonic ordering of domains. Output-entropy profiles are thus an accessible signal for scalable monitoring and for targeting data acquisition.
Output-entropy profiles computed from final-layer next-token probabilities serve as a scalable signal for monitoring LLM performance and prioritizing data acquisition under domain shifts.
output-entropy profile next-token probabilities final-layer top-k logprobs instance correctness domain-level accuracy estimate STEM reasoning benchmarks domain shift data acquisition