The Reliability Gap in Benchmark Auditing: Distribution Shift and Scale as Failure Modes of Contamination Detection
arXiv:2606.03305v1 Announce Type: new Abstract: Benchmark contamination, where evaluation examples appear in a model's training data, threatens the validity of LLM assessment. Statistical tools for detecting training-data membership exist, but have been validated almost exclusively in controlled academic regimes: large, homogeneous pre-training corpora and transparent, single-stage training pipelines. Whether these methods remain reliable in realistic auditing scenarios remains unclear. We identify two under-studied failure modes: distribution shift, which arises when suspect and validation se
The rapid deployment and increasing reliance on large language models necessitate robust evaluation methods, with benchmark contamination becoming a critical and newly highlighted validity threat.
The reliability of AI benchmarks directly impacts model development, deployment, and regulatory efforts, and contamination undermines the very foundation of trusting AI performance claims.
The understanding that current contamination detection methods may be insufficient for real-world LLM auditing scenarios suggests a need for more sophisticated, adaptable, and distribution-aware detection techniques.
- · AI ethics researchers
- · Organizations developing robust AI evaluation tools
- · Regulatory bodies focused on AI accountability
- · LLM developers relying on potentially contaminated benchmarks
- · Organizations with opaque AI training pipelines
- · Academic researchers using simplistic contamination detection
Increased scrutiny on past and current LLM benchmark results will emerge.
New research and development efforts will focus on advanced, robust, and scalable contamination detection methodologies.
The perceived trustworthiness of LLM performance metrics may decrease, potentially slowing adoption in highly sensitive applications until more reliable auditing practices are established.
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Read at arXiv cs.AI