How can the performance of a chatGPT model be evaluated in a way that takes into account both the quality of its generated responses and its ability to maintain coherence in long-term conversations? One approach might be to use a metric like perplexity, which is a measure of how well a language model predicts the next word in a sequence given the words that have come before it. However, perplexity does not necessarily capture the coherence of the model's responses over multiple turns in a conversation. Another possibility might be to use a measure like the BLEU score, which compares the model's generated responses to a set of reference responses and assigns a score based on the overlap between the two. However, the BLEU score does not take into account the quality of the generated responses themselves, only their similarity to the reference responses. Is there a way to combine these two approaches, or to come up with a new metric that takes into account both the quality and coherence of the model's responses in a more holistic way?
toothie25 t1_j0jvmec wrote
Reply to [D] Simple Questions Thread by AutoModerator
How can the performance of a chatGPT model be evaluated in a way that takes into account both the quality of its generated responses and its ability to maintain coherence in long-term conversations? One approach might be to use a metric like perplexity, which is a measure of how well a language model predicts the next word in a sequence given the words that have come before it. However, perplexity does not necessarily capture the coherence of the model's responses over multiple turns in a conversation. Another possibility might be to use a measure like the BLEU score, which compares the model's generated responses to a set of reference responses and assigns a score based on the overlap between the two. However, the BLEU score does not take into account the quality of the generated responses themselves, only their similarity to the reference responses. Is there a way to combine these two approaches, or to come up with a new metric that takes into account both the quality and coherence of the model's responses in a more holistic way?