Paper Summary: Method to Measure Benchmark Contamination
This is a summary and explanation for a paper that measured dataset contamination using kernel diverge scores: https://arxiv.org/pdf/2502.00678 . Benchmarks are a flawed estimation of a model’s real-world capabilities for a variety of reasons. One reason in benchmark dataset contamination - i.e. if part of benchmark’s dataset overlaps with training data, the model will have been specifically trained to do well on those tests. Therefore, the benchmark fails to accurately measure how well the model generalizes to examples it hasn’t seen. This is an interesting paper that looks at Kernel Divergence to estimate to dataset contamination. Aim Given a dataset D and model M, the paper aims to create a dataset contamination score S(D,M). The contamination ratio is the proportion of the benchmark dataset that has been seen in training data. This score should be monotonic (datasets with higher contamination should have higher scores) and consistent (datasets with similar contamination should...