Benchmark Contamination in LLMs
Investigating data leakage in bug detection benchmarks
This study examines whether Large Language Models are genuinely solving bug detection tasks or simply memorizing benchmark datasets.
Key Findings:
- LLMs demonstrate concerning levels of data contamination in common bug benchmarks
- Even advanced models show evidence of memorizing specific bugs rather than truly understanding them
- The research reveals significant reliability issues in how we evaluate LLM performance for software engineering tasks
This research matters because it challenges the validity of current evaluation methods for LLMs in code-related tasks, highlighting the need for more robust benchmarking approaches that prevent data leakage.