Don't Make Your LLM an Evaluation Benchmark Cheater (2311.01964v1)

Abstract: LLMs~(LLMs) have greatly advanced the frontiers of artificial intelligence, attaining remarkable improvement in model capacity. To assess the model performance, a typical approach is to construct evaluation benchmarks for measuring the ability level of LLMs in different aspects. Despite that a number of high-quality benchmarks have been released, the concerns about the appropriate use of these benchmarks and the fair comparison of different models are increasingly growing. Considering these concerns, in this paper, we discuss the potential risk and impact of inappropriately using evaluation benchmarks and misleadingly interpreting the evaluation results. Specially, we focus on a special issue that would lead to inappropriate evaluation, \ie \emph{benchmark leakage}, referring that the data related to evaluation sets is occasionally used for model training. This phenomenon now becomes more common since pre-training data is often prepared ahead of model test. We conduct extensive experiments to study the effect of benchmark leverage, and find that it can dramatically boost the evaluation results, which would finally lead to an unreliable assessment of model performance. To improve the use of existing evaluation benchmarks, we finally present several guidelines for both LLM developers and benchmark maintainers. We hope this work can draw attention to appropriate training and evaluation of LLMs.

Analysis of "Don't Make Your LLM an Evaluation Benchmark Cheater"

The paper "Don't Make Your LLM an Evaluation Benchmark Cheater" by Kun Zhou et al. addresses a critical issue in the evaluation of LLMs: benchmark leakage. As LLMs are increasingly employed across various domains, their evaluation against standardized benchmarks is essential to understand their performance and capabilities. However, the integrity of these evaluations can be compromised if benchmark datasets inadvertently overlap with training datasets, a phenomenon referred to as "benchmark leakage."

Conceptual Framework

According to the authors, benchmark leakage can artificially inflate the performance of LLMs on certain tasks, thereby distorting the comparative and intrinsic assessments of LLMs' capabilities. Typical evaluation benchmarks like MMLU, Big-Bench, and AGIEval, among others, are designed to test LLMs on aspects such as multitask language understanding, and human-level task handling. The paper argues that without stringent controls, LLMs may achieve unnaturally high scores by merely memorizing leaked benchmark data rather than demonstrating genuine capacity.

Experimental Methodology

The team conducted extensive experiments to explore the ramifications of benchmark leakage. They trained multiple popular LLM architectures, including GPT-Neo, phi-1.5, OpenLLaMA, and LLaMA-2, across various experimental conditions simulating varying degrees of data leakage, including full access to test prompts, training sets, and test sets.

Key Findings

The paper revealed significant and concerning insights:

1. Performance Inflation: Smaller models were shown to outperform significantly larger models when subject to benchmark leakage. For instance, a 1.3B parameter model trained with leaked test prompts outperformed a 65B model on certain tasks.
2. Adverse Effects on Model Generalization: The experimental results showed that training with leaked data could adversely affect a model's performance on tasks not related to the leaked data. This suggests a trade-off where specific task performance increases at the cost of generalization capabilities.
3. Data Contamination and Fair Evaluation: The paper underscores the impracticality of completely eliminating data contamination and stresses the importance of transparency in pre-training data compositions.

Implications in AI Development

The paper emphasizes the need for caution in the creation and maintenance of benchmark datasets. It proposes several guidelines:

• Diverse Benchmarking: Encouraging diverse benchmarks from varied sources to guard against performance inflation and to test a wide array of LLM abilities.
• Data Decontamination: Rigorous examination of overlap between pre-training corpora and benchmark datasets, recommending methods like $n$-gram overlap analysis.
• Reporting Transparency: Calls for open disclosure of potential contamination risks and providing contamination analysis reports alongside benchmark results.

Future Directions

The paper points to several avenues for future research:

• Advanced Contamination Detection: Developing more sophisticated techniques for detecting semantic and syntactical data leakage beyond simple $n$-gram overlap.
• Dynamic Benchmarks: Evaluating the feasibility of evolving benchmarks that can adapt to the growing body of public data used for training LLMs, ensuring they remain challenging and relevant.
• Holistic LLM Evaluation: Looking beyond task-specific benchmarks to assess LLMs on broader cognitive metrics, such as adaptability and contextual understanding, to mitigate the effects of potential leakage.

In conclusion, the paper provides a detailed and methodologically rigorous analysis of a prevalent issue in LLM evaluation. Ensuring the integrity of LLM performance assessments is not only crucial for fair comparisons but also for the incremental development of more capable and generalizable AI systems. The guidelines and insights presented here are poised to influence both future AI research and industry best practices.