提出高效方法估算大模型生成中的不确定性动态，降低计算成本。
Forking Fast: Efficiently Estimating Uncertainty Dynamics in Text Generation
- 通过统计建模平滑低样本数据，减少重复采样次数
- 发现不确定性随采样增多趋于稳定，噪声多为采样误差
- 适合研究模型推理过程与可靠性评估的学者
大模型推理具有随机性，理解其行为需分析其可能生成的推理链分布，即不确定性。现有基于重采样的分析虽能揭示推理关键步骤，但每轮采样所有标记或句子代价高昂。本文表明，大量重采样后，不确定性动态趋于稳定，噪声主要源于采样而非模型对单个标记的敏感性。我们构建统计模型，对低样本生成序列进行平滑处理，更准确逼近高样本结果，显著降低采样开销。
原文摘要 · Abstract (English)
LLM reasoning is stochastic, and so understanding a model requires grappling with the distribution of reasoning chains that it might produce for a given question, i.e., its uncertainty. Resampling-based analyses characterize this distribution, revealing which steps of a rollout determine how the model arrives at its answer. However, a major limitation of these approaches is that resampling text sequences at every token or sentence in a reasoning chain is very costly. Our work strives to make resampling analysis more computationally efficient, while also shedding light on an important scientific question: what is the right statistical model for explaining uncertainty dynamics in text generation? We show that when resampling many reasoning chains, uncertainty dynamics converge to stable patterns, and noise is largely an artifact of sampling rather than an LLM's sensitivity to each individual token or reasoning step. We develop a statistical model for smoothing noisy low-sample rollout data to better approximate high-sample data, allowing us to significantly cut sampling costs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。