Efficient semantic uncertainty quantification in language models via diversity-steered sampling

• URL: http://arxiv.org/abs/2510.21310v1
• Date: Fri, 24 Oct 2025 10:06:21 GMT
• Title: Efficient semantic uncertainty quantification in language models via diversity-steered sampling
• Authors: Ji Won Park, Kyunghyun Cho,
• Abstract summary: We introduce a diversity-steered sampler that discourages semantically redundant outputs during decoding.<n>Key idea is to inject a continuous semantic-similarity penalty into the model's proposal distribution.<n>Being modular and requiring no gradient access to the base LLM, the framework promises to serve as a drop-in enhancement for uncertainty estimation.
• Score: 46.23327887393273
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurately estimating semantic aleatoric and epistemic uncertainties in large language models (LLMs) is particularly challenging in free-form question answering (QA), where obtaining stable estimates often requires many expensive generations. We introduce a diversity-steered sampler that discourages semantically redundant outputs during decoding, covers both autoregressive and masked diffusion paradigms, and yields substantial sample-efficiency gains. The key idea is to inject a continuous semantic-similarity penalty into the model's proposal distribution using a natural language inference (NLI) model lightly finetuned on partial prefixes or intermediate diffusion states. We debias downstream uncertainty estimates with importance reweighting and shrink their variance with control variates. Across four QA benchmarks, our method matches or surpasses baselines while covering more semantic clusters with the same number of samples. Being modular and requiring no gradient access to the base LLM, the framework promises to serve as a drop-in enhancement for uncertainty estimation in risk-sensitive model deployments.

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