Which metric is commonly used for evaluating text generated by Generative AI?

Which metric is commonly used for evaluating text generated by Generative AI?

• A. Inception score
• B. BLEU score
• C. F1 score
• D. ROC-AUC score

Answer: (B)

The BLEU score is a widely recognized metric used for evaluating text generated by generative AI systems, particularly in the context of natural language processing and machine translation. This score measures the quality of generated text against one or more reference texts by comparing n-grams (continuous sequences of n items) between them. The BLEU score provides a quantitative measure of how closely the generated text matches human-written reference texts, allowing for an assessment of fluency and relevance in generation tasks. It is particularly effective in capturing the precision of correct n-grams in the generated output, which helps identify how well the model adheres to expected language patterns and syntax. A higher BLEU score indicates better alignment with the reference texts, making it a crucial tool in evaluating the performance of generative models in producing human-like text. In contrast, other metrics such as the Inception score, F1 score, and ROC-AUC score are more associated with evaluating image generation, classification tasks, and binary classification models, rather than being tailored for assessing the quality of generated textual content.

The BLEU score is a widely recognized metric used for evaluating text generated by generative AI systems, particularly in the context of natural language processing and machine translation. This score measures the quality of generated text against one or more reference texts by comparing n-grams (continuous sequences of n items) between them. The BLEU score provides a quantitative measure of how closely the generated text matches human-written reference texts, allowing for an assessment of fluency and relevance in generation tasks.

It is particularly effective in capturing the precision of correct n-grams in the generated output, which helps identify how well the model adheres to expected language patterns and syntax. A higher BLEU score indicates better alignment with the reference texts, making it a crucial tool in evaluating the performance of generative models in producing human-like text.

In contrast, other metrics such as the Inception score, F1 score, and ROC-AUC score are more associated with evaluating image generation, classification tasks, and binary classification models, rather than being tailored for assessing the quality of generated textual content.