benchmarking

A Google Research and Rochester Institute of Technology study reveals that standard AI benchmarking practices using three to five human evaluators per test example systematically underestimate human disagreement and produce unreliable model comparisons. The researchers found that at least ten raters per example are needed for statistically reliable results, and that budget allocation between test examples and raters matters as much as total budget size.

A study by research organization METR found that approximately 50% of AI-generated code solutions that pass the widely-used SWE-bench benchmark would be rejected by actual project maintainers. The finding exposes a significant gap between industry-standard code generation benchmarks and real-world code review standards.

Google DeepMind is pushing for moral behavior in large language models to be evaluated with the same technical rigor applied to coding and math benchmarks. As LLMs take on roles like companions, therapists, and medical advisors, the research group argues current evaluation standards are insufficient.