Is perplexity related to training data?

Perplexity is directly influenced by training data quality and relevance. It quantifies how well a language model predicts a sample text based on what it learned during training.

High perplexity often indicates mismatches between the training data and the evaluation data. Key factors include the training data's vocabulary coverage, domain relevance, linguistic patterns, and overall quality. Insufficient or noisy training data typically results in poorer model predictions and higher perplexity scores. Preprocessing choices applied to the training data also significantly impact perplexity outcomes.

Analyzing perplexity helps diagnose potential training data issues like domain mismatch, poor data quality, or insufficient coverage. By measuring perplexity on validation sets representative of the target domain, practitioners can assess data adequacy and guide improvements. This metric is crucial for evaluating language model performance and informing decisions on data collection, cleaning, and augmentation strategies during development. Lower perplexity generally correlates with better model performance on language tasks.