The Illusion of LLM Benchmark Success
Revealing the failures of contamination mitigation strategies
This research exposes serious flaws in current strategies for addressing benchmark data contamination in LLM evaluation, finding that attempted fixes may be ineffective.
- Modified benchmarks remain vulnerable to contamination and can be solved by LLMs using similar reasoning as original questions
- Question regeneration strategies fail to create truly novel evaluations
- Testing methods retain semantic similarities to training data, undermining evaluation integrity
- Current mitigation approaches provide a false sense of security while not addressing root problems
For security professionals, this highlights critical vulnerabilities in how we evaluate AI systems, potentially leading to deployment of models with overstated capabilities and unknown risks.