Contamination Inflates Scores but Rarely Reorders Large Language Model Leaderboards
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: whether contamination inflates absolute scores, and whether it reorders the ranking of models. We recast contamination as a violation of anchor-item invariance and measure it through the differential functioning of original versus semantically equivalent paraphrased items, a within-item contrast that holds the measured skill fixed and isolates memorization from capability. Using per-instance responses from 47 publicly released models and 74 models finetuned with a k...
| Authors | Xingyao Xiao, Yihong Cheng |
|---|---|
| Primary category | cs.CL |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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