AWS Certified AI Practitioner Practice Exam

To evaluate the accuracy of a generative AI solution that translates manuals, which strategy should the company use?

1. Bilingual Evaluation Understudy (BLUE)

2. Root mean squared error (RMSE)

3. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)

4. F1 score

The correct answer is: Bilingual Evaluation Understudy (BLUE)

The Bilingual Evaluation Understudy (BLEU) is the most appropriate strategy for evaluating the accuracy of a generative AI solution that translates manuals. BLEU is specifically designed for assessing machine translation systems by comparing the generated translations against one or more reference translations. It quantifies how similar the generated output is to the expected output, focusing on the precision of n-grams (contiguous sequences of words) within the translations. Using BLEU enables a company to measure the adequacy and fluency of the translations quantitatively. A higher BLEU score indicates that the machine-generated translation is closer to human quality in terms of how well it matches reference translations. It effectively captures the nuances in language and helps evaluate the overall performance and quality of the translation output, which is crucial for applications like translating manuals where accuracy is vital. Other methods like Root Mean Squared Error (RMSE), which is typically used for regression analysis, and the F1 score, which is more relevant for classification tasks, do not provide the same level of insight for translation tasks. Similarly, ROUGE is primarily used for evaluating summarization models where the focus is on recall and coverage rather than the precision and fluency required for translation assessments. Hence, BLEU is the most suitable choice