Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs

By Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, Ondrej Dusek. Conference of the European Chapter of the Association for Computational Linguistics, 2024

339 citations recorded by the source corpus.

Natural Language Processing (NLP) research is increasingly focusing on the use of Large Language Models (LLMs), with some of the most popular ones being either fully or partially closed-source. The lack of access to model details, especially regarding training data, has repeatedly raised concerns about data contamination among researchers. Several attempts have been made to address this issue, but they are limited to anecdotal evidence and trial and error. Additionally, they overlook the problem of indirect data leaking, where modelsare iteratively improved by using data coming from users. In this work, we conduct the first systematic analysis of work using OpenAI’s GPT-3.5 and GPT-4, the most prominently used LLMs today, in the context of data contamination. By analysing 255 papers and considering OpenAI’s data usage policy, we extensively document the amount of data leaked to these models during the first year after the model’s release. We report that these models have been globally exposed to ∼4.7M samples from 263 benchmarks. At the same time, we document a number of evaluation malpractices emerging in the reviewed papers, such as unfair or missing baseline comparisons and reproducibility issues. We release our results as a collaborative project on https://leak-llm.github.io/, where other researchers can contribute to our efforts.

Subfields: Information Systems

View Paper: https://www.semanticscholar.org/paper/798feda076ad710df65d509a7884bd15937c8056

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