CoT-Core: Accelerating LLM Evaluation via CoT-Aware Coreset Selection
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arXiv:2608.00014v1 Announce Type: new 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…
1Key Takeaways
- arXiv:2608.00014v1 Announce Type: new 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….
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv cs.AI reports that arXiv:2608.00014v1 Announce Type: new Abstract: Evaluating Large Language Models (LLMs) incurs prohibitive computational overhead during continuous development processes.
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