Forking Fast: Efficiently Estimating Uncertainty Dynamics in Text Generation
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 gener...
| Authors | Eric Bigelow, Amir Zur, Satchel Grant et al. |
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| Primary category | cs.CL |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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