Essential Guide to LLMOps by Ryan Doan

Author:Ryan Doan
Language: eng
Format: epub
Publisher: Packt Publishing Limited
Published: 2024-07-17T00:00:00+00:00

Evaluating perplexity, BLUE, and ROUGE

Perplexity measures how well a model predicts a sample, offering insights into the model’s confidence in its predictions. A lower perplexity signifies higher prediction confidence, making it an essential metric for tasks such as speech recognition or next-word prediction in natural language processing (NLP).

The BLEU score is primarily used in machine translation to evaluate the quality of machine-generated text against a set of high-quality reference translations. By comparing the machine’s output with the reference translations, the BLEU score assesses the correspondence of phrases and the overall translation quality on a scale from 0 to 1, where a score closer to 1 indicates a translation that closely matches the quality of the human reference.

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