Perplexity Cannot Always Tell Right from Wrong (ianbarber.blog)

A new study by Veličković et al. challenges the reliability of perplexity as a metric for assessing the performance of transformer-based language models in the AI/ML community. While perplexity offers a way to quantify a model's prediction uncertainty—where a lower value indicates higher confidence—it reveals a significant flaw: a confidently wrong prediction can yield a misleadingly low perplexity. This means that models might seem to perform well on long input sequences but could be making errors that are obscured by their self-assuredness. The researchers prove that for certain input lengths, a highly confident model might actually be wrong, yet still report low perplexity scores, suggesting that the model is inaccurately convinced of its correctness. This phenomenon becomes more pronounced with longer contexts, as the model may miscalculate the significance of certain tokens. The findings have important implications for model evaluation, indicating that relying solely on perplexity could lead to a false sense of accuracy and reinforce the need for more robust assessment methods in language modeling.