Stratified sampling for LLM eval sets: why your aggregate pass rate hides the regressions that…
This article discusses the limitations of using a single aggregate pass rate to evaluate Large Language Models (LLMs). It argues that this metric can obscure significant performance regressions within specific data slices. The author advocates for stratified sampling to create more nuanced evaluation sets, ensuring that all segments of the data are adequately represented and tested. AI
IMPACT Highlights the need for more sophisticated evaluation methods to accurately assess LLM performance and identify critical failure points.