Search Results: AI_Evaluation

Public AI benchmarks often mislead product decisions by compressing multidimensional performance into deceptive single-number scores. Engineering leaders reveal how to escape the benchmark trap by creating minimum viable internal evaluations aligned with actual business needs.

New research reveals large language models frequently disengage from conversations when given an exit option, with bail rates reaching 32% in some configurations. The study introduces BailBench to systematically evaluate this behavior and examines its complex relationship with refusal mechanisms.