Average token log-probability provides a zero-shot confidence signal for small LLMs that matches supervised baselines in-distribution and outperforms them out-of-distribution, with a new retrieval-conditional variant improving further at lower latency.
Log probabilities are a reliable estimate of semantic plausibility in base and instruction-tuned language models
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A configurable pipeline turns text corpora into quantitative semantic signals via embeddings, logprobs, and UMAP-based noise reduction for document positioning and corpus profiling.
Direct Reasoning Optimization applies token-level Reasoning Reflection Reward (R3) focused on high-variance tokens and rubric-gating constraints to improve sample-efficient RL training of LLMs on unverifiable tasks.
citing papers explorer
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Zero-Shot Confidence Estimation for Small LLMs: When Supervised Baselines Aren't Worth Training
Average token log-probability provides a zero-shot confidence signal for small LLMs that matches supervised baselines in-distribution and outperforms them out-of-distribution, with a new retrieval-conditional variant improving further at lower latency.
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Text-as-Signal: Quantitative Semantic Scoring with Embeddings, Logprobs, and Noise Reduction
A configurable pipeline turns text corpora into quantitative semantic signals via embeddings, logprobs, and UMAP-based noise reduction for document positioning and corpus profiling.
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Direct Reasoning Optimization: Token-Level Reasoning Reflectivity Meets Rubric Gates for Unverifiable Tasks
Direct Reasoning Optimization applies token-level Reasoning Reflection Reward (R3) focused on high-variance tokens and rubric-gating constraints to improve sample-efficient RL training of LLMs on unverifiable tasks.