COM-BOM: Bayesian Exemplar Search for Efficiently Exploring the Accuracy-Calibration Pareto Frontier

• URL: http://arxiv.org/abs/2510.01178v1
• Date: Wed, 01 Oct 2025 17:57:49 GMT
• Title: COM-BOM: Bayesian Exemplar Search for Efficiently Exploring the Accuracy-Calibration Pareto Frontier
• Authors: Gaoxiang Luo, Aryan Deshwal,
• Abstract summary: Prior search methods narrowly optimize for predictive accuracy and model calibration.<n>In this paper, we formulate a multi-objective optimization problem targeting both the determinant of predictive accuracy and the minimization of expected calibration error.<n>We find that COM-BOM beats or matches the baselines at jointly optimizing the two objectives.
• Score: 12.261526989434282
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Selecting an optimal set of exemplars is critical for good performance of in-context learning. However, prior exemplar search methods narrowly optimize for predictive accuracy, critically neglecting model calibration--a key determinant of trustworthiness and safe deployment. In this paper, we formulate exemplar selection as a multi-objective optimization problem, explicitly targeting both the maximization of predictive accuracy and the minimization of expected calibration error. We solve this problem with a sample-efficient Combinatorial Bayesian Optimization algorithm (COM-BOM) to find the Pareto front that optimally trades off the two objectives of accuracy and calibration. We evaluate COM-BOM on multiple tasks from unsaturated MMLU-Pro benchmark and find that COM-BOM beats or matches the baselines at jointly optimizing the two objectives, while requiring a minimal number of LLM API calls.

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