Perplexity is a metric used to evaluate how well an LLM predicts a sequence of tokens. It quantifies the uncertainty of the model's predictions, with lower values indicating better performance. Mathematically, perplexity is the exponential of the average negative log probability assigned to the tokens in the dataset.
For example, if a model assigns high probabilities to the correct tokens in a test set, it will have low perplexity, reflecting its ability to generate text similar to the dataset. Conversely, high perplexity suggests that the model struggles to predict the next token accurately, indicating a need for further training or fine-tuning.
Perplexity is primarily used during model evaluation to compare different architectures or training configurations. While it is a useful measure for language modeling tasks, it does not always correlate with real-world performance, especially in complex applications like dialogue systems, where other factors such as coherence and relevance also matter.