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Are Near-Tied LLM Rankings Robust to Family-DIF-Guided Benchmark Recomposition?

Zheng, Qiaoyuan et al.
arXiv (All) · Papers
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computation-and-languagemachine-learning
Small leaderboard gaps are often interpreted as evidence that one language model is better than another, but their sign may depend on which benchmark items are included. We test this using item-level responses from five benchmarks and a family-label-free spectral approximation to multidimensional it
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