How Hard is it to Rig a Benchmark? A Social Choice Analysis of Leaderboard Robustness

• URL: http://arxiv.org/abs/2605.23628v1
• Date: Fri, 22 May 2026 13:40:00 GMT
• Title: How Hard is it to Rig a Benchmark? A Social Choice Analysis of Leaderboard Robustness
• Abstract summary: We consider benchmark-specific training as a form of election manipulation.<n>For any ordinal benchmark, the problem of choosing datasets to train on so that a target model becomes top-ranked corresponds to shift bribery.<n>We derive expressions for it under arithmetic mean, median, mean win rate and pairwise majority.
• Score: 8.615817304274529
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-task benchmarks have become a central pillar of machine learning research, yet their growing influence has incentivised benchmark gaming -- strategic actions taken to improve the leaderboard rank of a specific model. Treating datasets as voters and models as candidates, we consider benchmark-specific training -- the inclusion of benchmark data in training -- as a form of election manipulation. For any ordinal benchmark, the problem of choosing datasets to train on so that a target model becomes top-ranked corresponds to shift bribery, a class of manipulation problems from computational social choice. Leveraging this identification, we show that the benchmark-specific training problem is NP-hard under Borda count and mean win rate. Complementing this worst-case perspective, we introduce the instance-level robustness, the minimum number of datasets a model developer must include in training to top a given leaderboard, and derive expressions for it under arithmetic mean, median, mean win rate and pairwise majority. We evaluate these expressions on MMLU under HELM and on BIG-Bench Hard (BBH) under the Open LLM Leaderboard. Across both suites, mean win rate is hardest to manipulate: this gap is clear on BBH (24 tasks, 4507 models), where its median robustness is 22 tasks (92%), compared with 13 (54%) under arithmetic mean and 12 (50%) under median and pairwise majority.

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