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This paper addresses the computational inefficiency of resampling analyses used to estimate uncertainty dynamics in text generation by large language models (LLMs). The authors introduce a statistical model that smooths noisy low-sample rollout data, demonstrating that uncertainty dynamics converge to stable patterns when resampling multiple reasoning chains. Their findings reveal that much of the noise observed in uncertainty estimates is due to sampling artifacts rather than the model's sensitivity to individual tokens, leading to significant reductions in sampling costs.
Uncertainty in LLM reasoning is largely a sampling artifact, allowing for more efficient analyses that cut costs without sacrificing accuracy.
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.
