TASK-SPECIFIC LLM EVALS THAT DO & DON'T WORK

Summary from the TLDR AI newsletter

Off-the-shelf evaluations often fail to effectively measure LLM performance for specific tasks. Useful metrics for classification include recall, precision, ROC-AUC, while summarization and translation can employ NLI-based consistency checks and chrF or BLEURT, respectively. Consider potential defects like copyright regurgitation and toxicity in models, using tests such as RealToxicityPrompts for comprehensive evaluation.