CoT-Core: Accelerating LLM Evaluation via CoT-Aware Coreset Selection

Abstract:Evaluating Large Language Models (LLMs) incurs prohibitive computational overhead during continuous development processes. While coreset selection accelerates evaluation, existing methods either suffer from a severe ``cold start'' bottleneck requiring massive historical logs (e.g., Item Response Theory) or exhibit a surface lexical bias that misses the underlying reasoning manifold of tasks. We propose CoT-Core, a novel training-free core question selection framework. Recognizing that lexically disparate questions can share equivalent underlying logic, CoT-Core prompts LLMs to unroll zero-shot Chain-of-Thought (CoT) reasoning trajectories. Projecting these paths into a latent space effectively clusters questions by intrinsic logical equivalence rather than superficial text similarity. Extensive experiments on GSM8K, MMLU, MMLU-Pro, and GPQA demonstrate that CoT-Core drastically reduces evaluation costs while maintaining high-fidelity score estimation, and delineate the boundary conditions of reasoning-aware pruning, revealing that its efficacy is intrinsically gated by task complexity.

23 pages, 3 figures, 10 tables. Includes appendix

Artificial Intelligence (cs.AI)

arXiv:2608.00014 [cs.AI]

(or arXiv:2608.00014v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.00014 Focus to learn more arXiv-issued DOI via DataCite

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