Why Your LLM Eval Harness Is Lying to You (And How to Fix It)
A new approach to evaluating Large Language Models (LLMs) has been proposed to address the issue of static evaluation harnesses failing to detect model regressions. This method involves refreshing evaluation datasets weekly with real production traces, stratified by intent cluster to ensure representative sampling. Additionally, a permanent adversarial set, curated from actual customer support tickets indicating model failures, is weighted heavily in the evaluation process to prioritize real-world performance. AI
IMPACT Improves LLM reliability by ensuring evaluation methods accurately reflect real-world performance and detect regressions.