arXiv Computation and Language
Sep 4

Contamination Inflates Scores but Rarely Reorders Large Language Model Leaderboards

The paper investigates how benchmark contamination—leakage of test items into training data—affects large language model (LLM) leaderboards. By comparing original test items with semantically equivalent paraphrases, the authors measure contamination as a violation of anchor-item invariance and find that it inflates absolute scores but rarely changes model rankings. Across 47 public models and 74 finetuned models on four benchmarks, the rank correlation between standard and paraphrase-controlled leaderboards is 0.997, with only a handful of cases showing differential contamination that could alter rankings.

By Xingyao Xiao (Stanford University), Yihong Cheng (City University of Macau)
arXiv Machine Learning
Sep 10

The BatchNorm Illusion: Diagnosing Normalization Artifacts in Machine Unlearning Evaluation

The paper uncovers a hidden flaw in evaluating machine unlearning on BatchNorm-based models: a single forward pass over retained data can alter the model’s normalization state without changing weights, misleadingly restoring performance. This effect, formalized as a weight‑preserving fixed‑point operator, shows that apparent forgetting can be entirely due to BatchNorm running statistics, not to the unlearning method itself. Experiments demonstrate that the artifact can inflate forget accuracy by up to 78 percentage points across nine methods, and that switching to GroupNorm eliminates the problem.

By Aaryaman Kalani, Murari Mandal, Dhruv Kumar, Mohan Kankanhalli, Yash Sinha