Task-Specific LLM Evals that Do & Don't Work
Eugene Yan
The article discusses evaluation metrics and methods for assessing large language model performance on specific tasks including classification, extraction, summarization, and translation. Key concrete metrics mentioned are ROC-AUC and PR-AUC for classification (ranging from 0.0 to 1.0), natural language inference models for measuring factual consistency in summaries, and specialized tools like chrF and COMET for translation quality. The author recommends moving beyond generic off-the-shelf evaluations toward task-specific metrics that better correlate with actual application performance and can reliably measure production-ready systems.
Why it matters
Evals for classification, summarization, translation, copyright regurgitation, and toxicity.
