What are common evaluation metrics used to assess the performance of LLMs?

Common evaluation metrics for assessing the performance of LLMs include BLEU (Bilingual Evaluation Understudy) for translation tasks, ROUGE (Recall-Oriented Understudy for Gisting Evaluation) for summarization, and accuracy or F1 scores for classification tasks. For generative tasks, metrics like perplexity are also used to quantify how well a probability model predicts a sample. Additionally, human evaluations are often utilized to assess fluency, relevance, and coherence, providing a qualitative measure of model performance.