Model Perplexity

Model perplexity, a measure of how well a language model predicts a sequence of words, is a key metric for evaluating and improving language model performance. Current research focuses on understanding and mitigating the impact of noisy data on perplexity, exploring its relationship to training data density, and developing methods to improve model quality by leveraging perplexity-based techniques like contrastive learning and minimum Bayes risk decoding. These advancements are significant because they directly address challenges in generating high-quality, reliable text, impacting applications ranging from hate speech detection to controlled text generation and machine translation.