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LLM benchmark contamination inflates scores but rarely reorders leaderboards

By PulseAugur Editorial · [1 sources] ·

A new paper from arXiv investigates benchmark contamination in large language models, distinguishing between score inflation and leaderboard reordering. The research found that while contamination does inflate absolute scores, it rarely alters the ranking of models on leaderboards. The study proposes a method to audit for contamination by comparing original test items with paraphrased versions, suggesting that leaderboards should report paraphrase-controlled rankings alongside confidence intervals. AI

IMPACT Provides a method to audit LLM benchmarks for contamination, improving the reliability of model evaluations.

RANK_REASON Research paper analyzing LLM benchmark contamination. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM benchmark contamination inflates scores but rarely reorders leaderboards

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Research paper analyzing LLM benchmark contamination. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Xingyao Xiao (Stanford University), Yihong Cheng (City University of Macau) ·

    Contamination Inflates Scores but Rarely Reorders Large Language Model Leaderboards

    arXiv:2609.02899v1 Announce Type: new Abstract: Benchmark contamination, the leakage of test items into training data, is widely described as a threat to the reliability of large language model (LLM) leaderboards. We argue that this concern conflates two distinct questions: wheth…

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