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New taxonomy classifies AI benchmark contamination by defeated mitigations

By PulseAugur Editorial · [1 sources] ·

A new taxonomy for benchmark contamination in AI has been proposed, organizing contamination types by the mitigation methods they defeat. This taxonomy categorizes contamination into direct, derivative, temporal, distributional, and acquired types, addressing both training-time and evaluation-time leakage. The research also introduces a four-field disclosure protocol for reporting contamination status, acknowledging that 'unknown' is a valid entry. An analysis of 41 documents revealed low inter-coder reliability for some variables, with significant disagreement on when a variable applies rather than on its stated content. AI

IMPACT This research aims to improve the reliability and transparency of AI benchmark reporting, potentially leading to more accurate assessments of model capabilities.

RANK_REASON The cluster contains an academic paper detailing a new taxonomy for AI benchmark contamination. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New taxonomy classifies AI benchmark contamination by defeated mitigations

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The cluster contains an academic paper detailing a new taxonomy for AI benchmark contamination. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Johanna Angulo, V\'ictor Yeste, Hector Espinos-Morato ·

    Benchmark Contamination: A Taxonomy Organized by Defeated Mitigation

    arXiv:2608.29463v1 Announce Type: cross Abstract: A benchmark score is a joint property of the model, the evaluation harness, the elicitation budget, the sampled population, and contamination status. Leaderboards publish the model and the score, so capability and leakage stay obs…

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