Knowing When to Stop: Bayesian Optimal Stopping for LLM Evaluations
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TL;DR - Optstop is a Bayesian adaptive-stopping framework that allocates LLM evaluation trials according to uncertainty rather than fixed repetition counts. It can substantially reduce evaluation compute while preserving overall conclusions.
- Uses hierarchical Bayesian inference for binary, ordinal, and continuous outcomes.
- Stops sampling items once estimates are sufficiently precise or stable.
- Keeps all benchmark items eligible and requires no calibrated item bank.
- Removed 57%–97% of planned trials in an illustrative 200-item evaluation across nine validation settings.
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Knowing When to Stop: Bayesian Optimal Stopping for LLM Evaluations
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TL;DR - Optstop is a Bayesian adaptive-stopping framework that allocates LLM evaluation trials according to uncertainty rather than fixed repetition counts. It can substantially reduce evaluation compute while preserving overall conclusions.
- Uses hierarchical Bayesian inference for binary, ordinal, and continuous outcomes.
- Stops sampling items once estimates are sufficiently precise or stable.
- Keeps all benchmark items eligible and requires no calibrated item bank.
- Removed 57%–97% of planned trials in an illustrative 200-item evaluation across nine validation settings.