用多样性引导采样，高效估算大模型问答不确定性
Efficient semantic uncertainty quantification in language models via diversity-steered sampling
- 通过语义相似度惩罚减少重复输出，提升生成多样性
- 相同样本数下覆盖更多语义簇，误差更小且稳定
- 无需修改原模型，适合高风险场景的不确定性增强
在自由格式问答任务中，准确估计大语言模型的语义随机与认知不确定性极具挑战，因稳定估计常需大量昂贵生成。本文提出一种多样性引导采样器，在自回归与掩码扩散框架下均适用，显著提升样本效率。核心思想是利用轻量微调的自然语言推理模型，在解码过程中对部分前缀或中间扩散状态注入连续语义相似度惩罚。通过重要性重加权消除下游不确定性估计偏差，并使用控制变量法降低方差。在四个问答基准上，该方法以相同样本数覆盖更多语义簇，性能优于或持平基线。该框架模块化强，无需访问原始模型梯度，可作为即插即用组件，用于高风险模型部署中的不确定性评估。
原文摘要 · Abstract (English)
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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