The article also touches on the evaluation of copyright regurgitation and toxicity, suggesting methods to measure exact regurgitation and the proportion of toxic generations. It emphasizes the importance of human evaluation and balancing potential benefits and risks. The author provides practical examples and insights into how these evaluations can be applied in real-world scenarios, such as intent classification and opinion summarization, to improve the reliability and effectiveness of language models in production environments.
Key takeaways:
- Off-the-shelf evaluation metrics often fail to correlate with application-specific performance, necessitating custom evaluations for tasks like classification, summarization, and translation.
- Classification evaluations should focus on metrics like recall, precision, ROC-AUC, and PR-AUC, while also examining the separation of predicted probability distributions.
- Summarization evaluations can be simplified to classification tasks, focusing on factual consistency and relevance, with NLI models being effective for detecting inconsistencies.
- Machine translation evaluations benefit from metrics like chrF, BLEURT, and COMET, with chrF being language-independent and BLEURT offering a nuanced assessment through BERT-based finetuning.