Metrics for GenAI Text

Here's a breakdown of some common metrics used to evaluate generative AI models, including BLEU, ROUGE, METEOR, and GLEU: Metrics based on N-gram Overlap:

BLEU (Bilingual Evaluation Understudy): BLEU scores measure how similar the generated text is to a set of human-written reference texts. It considers matching n-grams (sequences of n words) between the generated text and the references. Higher BLEU scores indicate better performance, but BLEU can be criticized for not considering word order or semantics.

BLEU-n: BLEU-n is a variant of BLEU that specifically focuses on n-gram matches of length n. BLEU-4, for example, considers 4-word sequence matches.

GLEU (Gymnastics Error Rate): Similar to BLEU, GLEU scores assess n-gram overlap. However, GLEU penalizes the model more severely for unmatched words compared to BLEU.

GLEU-n: Similar to BLEU-n, GLEU-n is a variant of GLEU that focuses on n-gram matches of specific length n.

Metrics Beyond N-gram Overlap:

ROUGE (Recall-Oriented Understudy for Gisting Evaluation): ROUGE scores look beyond just n-gram overlap to consider how well the generated text captures the gist or important information from the reference text. ROUGE offers several variants, including ROUGE-L (Longest Common Subsequence) and ROUGE-N (n-gram), each measuring different aspects of similarity.

METEOR (Metric for Evaluation of Translation with Ordering): METEOR scores take into account not just n-gram overlap but also synonyms and paraphrases. It aims to provide a more semantic evaluation of how well the generated text aligns with the reference text.