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.

翻译：在自由形式问答任务中，准确估计大型语言模型的语义偶然不确定性和认知不确定性尤为困难，因为获得稳定的估计通常需要大量昂贵的生成过程。我们提出一种多样性引导采样器，该采样器在解码过程中抑制语义冗余的输出，同时覆盖自回归和掩码扩散两种范式，并显著提升采样效率。其核心思想是使用一个在部分前缀或中间扩散状态上轻量微调的自然语言推理模型，将连续的语义相似性惩罚注入到模型的提议分布中。我们通过重要性重加权对下游不确定性估计进行去偏处理，并利用控制变量法缩减其方差。在四个问答基准测试中，我们的方法在相同样本数量下覆盖了更多语义簇，其性能达到或超越了基线方法。该框架具有模块化特性，且无需访问基础语言模型的梯度，有望作为风险敏感模型部署中不确定性估计的即插即用增强组件。