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Zipbench: Low-Cost Framework for Compressing Comprehensive Benchmarks of Large Language Models

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

Comprehensive benchmark suites are essential for improving large language models (LLMs), but many widely used benchmarks are redundant, making evaluation unnecessarily expensive. Although recent benchmark compression methods (BCMs) can mitigate this cost, many strong BCMs rely on large collections of per-sample evaluation results from numerous LLMs to identify representative samples. Building such collections is also expensive unless they are already public, making these methods difficult to extend to newly released benchmarks. To address this challenge, we present ZipBench, a simple and low-c

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