Evaluating the performance of language models encompasses a diverse array of metrics and methodologies tailored to specific language tasks and applications. Common evaluation metrics include perplexity, a measure of the model’s predictive uncertainty; BLEU score, used for assessing machine translation quality; ROUGE score, employed in text summarization tasks; and human evaluation, which solicits subjective judgments from human annotators to assess the quality, coherence, and fluency of generated text.
