Say you are trying to decide which artificial intelligence (AI) tool would be best for translating a research paper from Spanish to English. Browsing the web, you discover one analysis gives the best grade to Google’s Gemini large language model, but another ranks Translated’s Lara 3 at the top—whereas a third gives honors to an open model from Nous Research.
Welcome to the chaotic and confusing world of AI benchmarks—tests designed to measure everything from a model’s speed and accuracy to its ability to bypass safeguards. Now, however, researchers are working to bring greater transparency and reproducibility to benchmarking, launching several initiatives aimed at making it easier to understand what the scores actually mean and whether they can be trusted.
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