Benchmarking Benchmark Leakage in Large Language Models (2404.18824v1)

Abstract: Amid the expanding use of pre-training data, the phenomenon of benchmark dataset leakage has become increasingly prominent, exacerbated by opaque training processes and the often undisclosed inclusion of supervised data in contemporary LLMs. This issue skews benchmark effectiveness and fosters potentially unfair comparisons, impeding the field's healthy development. To address this, we introduce a detection pipeline utilizing Perplexity and N-gram accuracy, two simple and scalable metrics that gauge a model's prediction precision on benchmark, to identify potential data leakages. By analyzing 31 LLMs under the context of mathematical reasoning, we reveal substantial instances of training even test set misuse, resulting in potentially unfair comparisons. These findings prompt us to offer several recommendations regarding model documentation, benchmark setup, and future evaluations. Notably, we propose the "Benchmark Transparency Card" to encourage clear documentation of benchmark utilization, promoting transparency and healthy developments of LLMs. we have made our leaderboard, pipeline implementation, and model predictions publicly available, fostering future research.

Understanding Data Leakage in LLMs

Introduction to the Issue of Data Leakage

In the field of AI and machine learning, particularly with the rampant growth of LLMs, the integrity of model evaluations is crucial. However, a sneaky issue often undermining this integrity is data leakage—where the data intended only for testing subtly makes its way into the training set. This issue is exacerbated by not always knowing which data the model has seen during its training, especially with the lack of transparency in the training processes of modern LLMs.

How Data Leakage Occurs

Data leakage can manifest in several forms:

• Training with unseen test data: Ideally, the data used to test the model's performance should be entirely new to the model. However, if test data leaks into the training set, the model's performance can appear misleadingly good, not because the model generalizes well, but because it has already seen the test examples.
• Training with seen training data: This scenario is more common and generally acceptable, where the model is trained on the designated training set. Issues arise when this data is assumed to be representative of general, unseen data.

Identifying Data Leakage

With opaque data and training procedures, spotting when and where leakage happens is no trivial task. The researchers focused on this by using two main metrics:

• Perplexity (PPL): This metric helps determine how well a model predicts a sample; lower perplexity suggests the model is more familiar with the data, possibly indicating leakage.
• N-gram Accuracy: This evaluates whether the model can predict fixed-size sequences of words accurately, which would suggest the model may have memorized parts of the dataset.

Explorative Methodologies

The paper introduces a detection pipeline that incorporates these metrics to analyze discrepancies in model performance on the original test data versus specially synthesized reference data. This approach allows them to gauge the potential of data leakage effectively.

Insights from Model Evaluations

When testing 31 different LLMs, the researchers found substantial discrepancies that hint at potential data leakage. Some models showed high familiarity with the benchmark datasets used for their evaluation, suggesting that portions (if not all) of these datasets were part of their training data.

Proposed Solutions and Future Directions

To combat issues of data leakage and improve the reliability of model evaluations, the paper proposes:

• Benchmark Transparency Card: Similar to a nutrition label, this documentation would detail the datasets used during the model's training and testing phases. This transparency can help reduce the misuse of datasets and promote fair comparisons between models.
• Robust Evaluation Setups: The researchers suggest regular updates and expansions to evaluation datasets and consider the use of dynamic benchmarks that evolve to continually provide novel challenges.

Conclusion

The paper sheds light on the critical issue of data leakage in LLMs, providing robust tools and methodologies for detecting such issues. It underscores a pressing need for transparency and rigor in handling the data used for training and testing AI models. Despite some limitations, such as the challenge of detecting leaks in altered data formats, these contributions are pivotal in guiding more ethical AI development practices.

The ongoing conversation about data leakage in AI is not just about improving model evaluations but also about fostering trust and reliability in AI applications used in real-world scenarios. As the field moves forward, both transparency and innovative detection methods will be key in ensuring AI technologies perform as intended without unseen biases or oversights.