To evaluate coherence there are metrics like BLEU, and ROUGE. These metrics compare the generated text to reference texts for consistency and logical flow. It returns score between 0 and 1. Higher score indicates the better performance. Relevance can be assessed through semantic similarity measures like cosine similarity with BERT embeddings. It ensures the generated content is pertinent to the given context. For fluency there are perplexity and human evaluation metrics that judge the smoothness and grammatical correctness of the generated text. It basically assess the language model's output quality.
Thanks for such a detailed explanation
What techniques are available for measuring coherence, relevance, and fluency in the output refinement of the generator steps?
To evaluate coherence there are metrics like BLEU, and ROUGE. These metrics compare the generated text to reference texts for consistency and logical flow. It returns score between 0 and 1. Higher score indicates the better performance.
Relevance can be assessed through semantic similarity measures like cosine similarity with BERT embeddings. It ensures the generated content is pertinent to the given context.
For fluency there are perplexity and human evaluation metrics that judge the smoothness and grammatical correctness of the generated text. It basically assess the language model's output quality.