arXiv:2507.00460v3 Announce Type: replace Abstract: Open Large Language Model (LLM) benchmarks, such as HELM and BIG-Bench, provide standardized and transparent evaluation protocols that support comparative analysis, reproducibility, and systematic progress tracking in Language Model (LM) research. Yet, this openness also creates substantial risks of data leakage during LM testing--deliberate or inadvertent, thereby undermining the fairness and reliability of leaderboard rankings and leaving them vulnerable to manipulation by unscrupulous actors. We illustrate the severity of this issue by int
The proliferation and increasing reliance on large language models (LLMs) and their benchmark evaluations highlight the critical need for robust and fair assessment methods.
This report exposes a fundamental flaw in the evaluation of AI models, which could mislead research and investment, and undermine progress in various AI applications.
The conventional understanding of language model performance, as derived from current open benchmarks, is now questionable due to potential data leakage and manipulation risks.
- · Ethical AI research institutions
- · Independent AI safety auditors
- · Developers of more secure evaluation methodologies
- · AI models that eschew reliance on public benchmarks
- · LLM leaderboard platforms
- · Companies whose valuation relies heavily on benchmark scores
- · Researchers relying solely on open benchmarks
- · Unscrupulous actors attempting to game benchmark systems
Benchmark scores for Language Models become less credible, forcing a re-evaluation of model capabilities.
Increased investment and research into novel, leakage-proof evaluation techniques for AI will accelerate.
The development and deployment of genuinely superior LLMs might be slowed as researchers struggle with accurate assessment, potentially affecting AI adoption across various sectors.
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