Recent scrutiny of AI benchmarking practices reveals significant flaws undermining the trustworthiness of reported scores. A group of researchers from the European Commission's Joint Research Center reviewed 100 studies from the last decade and found issues like biases in dataset creation, insufficient documentation, and data contamination. These problems raise doubts about the integrity of benchmark results from prominent models like OpenAI's o3 and Google's Gemini 2.0 Pro. Additionally, the usual one-time testing fails to account for the complexity of multi-modal AI interactions, further questioning the benchmarks' applicability in real-world settings.
AI benchmark scores can be misleading, with systematic issues in their design, application, and evaluation processes undermining their reliability.
Numerous biases and flaws were found in AI benchmarking practices, including poor documentation and challenges in separating true performance from noise.
The research highlights that benchmarking within AI is akin to hardware makers pushing their performance in marketing, casting doubt on trustworthiness.
Current benchmarking approaches fail to consider the multi-modal nature of AI models, which limits their relevance and applicability in real-world scenarios.
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