Data Contamination Through the Lens of Time
Manley Roberts Himanshu Thakur Christine Herlihy Colin White Samuel Dooley
Recent claims about the impressive abilities of large language models (LLMs)
are often supported by evaluating publicly available benchmarks. Since LLMs
train on wide swaths of the internet, this practice raises concerns of data
contamination, i.e., evaluating on examples that are explicitly or implicitly
included in the training data. Data contamination remains notoriously
challenging to measure and mitigate, even with partial attempts like controlled
experimentation of training data, canary strings, or embedding similarities. In
this work, we conduct the first thorough longitudinal analysis of data
contamination in LLMs by using the natural experiment of training cutoffs in
GPT models to look at benchmarks released over time. Specifically, we consider
two code/mathematical problem-solving datasets, Codeforces and Project Euler,
and find statistically significant trends among LLM pass rate vs. GitHub
popularity and release date that provide strong evidence of contamination. By
open-sourcing our dataset, raw results, and evaluation framework, our work
paves the way for rigorous analyses of data contamination in modern models. We
conclude with a discussion of best practices and future steps for publicly
releasing benchmarks in the age of LLMs that train on webscale data.