Technical Metrics for GenAI for Text
Evaluating Text Generation: A Look Beyond BLEU METRICWhile evaluating the quality of machine-generated text (MT) is crucial, there's no single perfect metric. This article explores various metrics used for Generative AI (GenAI) for text, going beyond the popular BLEU score:BLEU (BiLingual Evaluation Understudy): BLEU assesses n-gram (sequence of n words) overlap between generated text and a set of reference translations. It works well for comparing machine translation (MT) systems, but suffers from shortcomings: GLEUGLEU (Gym Levenshtein Edit Distance): GLEU addresses some BLEU limitations by incorporating Levenshtein edit distance, a measure of similarity between strings. It considers word insertions, deletions, and substitutions. METEORMETEOR (Metric for Evaluation of Translation with Ordering): METEOR builds on BLEU, incorporating synonyms, stemming (reducing words to their root form), and punctuation information for a more nuanced evaluation. PERPLEXITYPerplexity: Perplexity measures how well a language model predicts the next word in a sequence. Lower perplexity indicates better prediction capability. However, perplexity doesn't directly assess semantic coherence or fluency. ROUGEROUGE (Recall-Oriented Understudy for Gisting Evaluation): ROUGE family of metrics (ROUGE-L, ROUGE-S, ROUGE-W) focuses on recall, considering the ratio of n-grams in the generated text that are also present in the reference text. Other Metrics:CIDEr (Consensus-based Image Description Evaluation): CIDEr incorporates BLEU along with word embedding similarity to evaluate the factual correctness and semantic similarity of generated descriptions. MoverScore: MoverScore assesses semantic similarity between generated text and reference text using a pre-trained sentence embedding model. It focuses on capturing the overall meaning rather than exact word overlap. BERTScore: BERTScore leverages pre-trained BERT models to calculate both precision and recall for n-gram matches, along with a measure of grammatical correctness. Choosing the Right Metric: It depends on use case and task. Here are some considerations: Machine Translation (MT): BLEU, GLEU, METEOR, ROUGE are commonly used. Text Summarization: ROUGE metrics are often preferred. Text Generation for Creativity: Metrics like CIDEr or MoverScore might be more suitable, as they assess semantic similarity and factual correctness. |