Yes, in language modeling, lower perplexity is better. Perplexity is a measurement used to evaluate language models. It reflects how well a model predicts a sample. Lower perplexity means that the model's predictions are similar to the actual distribution, thereby making the model a better one. Ideally, a perfect model would have a perplexity of 1, meaning it perfectly predicts the sample every time. So, in summary, the lower the perplexity, the better the language model.

Yes, in the context of language modeling, a lower perplexity is indeed better. To provide a more detailed explanation, perplexity is a common metric used in language modeling to measure how well a probability model predicts a sample. In simple terms, it is a measure of how confused the model is when making predictions. The lower the perplexity, the less 'perplexed' or 'confused' the model is—so yes, lower is better. A language model with a high perplexity score indicates that it often assigns a low probability to the occurrence of the next word in a sentence, which means that it struggles to accurately predict the next word(s). On the other hand, a language model with low perplexity assigns a higher probability to the next word(s), meaning it "knows" better what comes next. In practice, a model with lower perplexity will be more useful for tasks such as speech recognition, machine translation, and autocorrect where the ability to accurately predict what comes next is crucial. However, note that while a lower perplexity is typically associated with a better model, perplexity alone should not be the only gauge for model success. It's also important to use additional ways of evaluation that fit your specific application, such as BLEU score for machine translation, or Word Error Rate (WER) for speech recognition. Therefore, while it's normally accurate to say a lower perplexity is better, it's always important to think of this measure in context of other evaluation metrics, and ultimately, the specific task your model was trained for.