In the world of natural language processing (NLP), perplexity is a commonly used metric for measuring a language model’s performance. With emerging state-of-the-art AI language models like Microsoft’s Megatron and OpenAI’s GPT-3, it is essential to know how to evaluate their performance.
This article discusses perplexity in NLP and its pros and cons.
What Is Perplexity?
Perplexity is a statistical measure of how confidently a language model predicts a text sample. In other words, it quantifies how “surprised” the model is when it sees new data. The lower the perplexity, the better the model predicts the text.
The perplexity metric can be used to compare different language models, identify problems in a chatbot dataset, or fine-tune the parameters of a single model, among other uses.
Perplexity has its advantages and disadvantages as a metric. As such, it is important to understand both its strengths and weaknesses before using it to evaluate language models.
What Are the Pros of Perplexity?
Here are some of the advantages of perplexity in NLP.
Fast to Calculate
The perplexity metric is fast to calculate because it’s based on the average log-likelihood of the dataset, which can be approximated using a single pass through the data. That makes it especially useful for large datasets tuning hyperparameters in NLP models. This performance metric helps researchers weed out language models that are likely to perform poorly in the real world.
Useful in Estimating a Language Model’s Uncertainty
Perplexity is also a useful metric for estimating a language model’s uncertainty. That is, perplexity can help identify when a model is overfitting or underfitting data. For example, if perplexity decreases as the training set size increases, this is an indication that the model is overfitting the training data and will likely not generalize well to new data. However, it is important to remember that low perplexity is not always accurate.
The perplexity metric is also statistically robust. It is not easily influenced by outliers in the dataset. For example, if there is a single outlier sentence in the dataset, perplexity will not be greatly affected.
What Are the Cons of Perplexity?
Below are some of the weaknesses of perplexity in NLP.
Not Accurate for Final Evaluation
Perplexity is not suitable for final evaluation because it doesn’t measure accuracy. It’s possible for a model to have low perplexity but a high error rate. In other words, just because a model is confident in its predictions doesn’t mean that those predictions are correct. For this reason, perplexity should only be used as a preliminary measure. Once you’ve narrowed down your models using perplexity, you should evaluate them using other metrics.
Hard to Make Comparisons across Datasets
The main disadvantage of perplexity is that it can be hard to make comparisons across datasets because each dataset has its own distribution of words, and each model has its own parameters. That makes it difficult to directly compare the performances of models trained on different datasets.
Perplexity Might Favor Models Trained on Outdated Datasets
Another potential drawback of perplexity is that it might favor models trained on outdated datasets. For example, if a newer dataset contains words not present in the training data, the perplexity of the models trained on that data will be artificially inflated. That could lead to models that are not necessarily better at generalizing new data to be selected as the best-performing models.
Perplexity is a commonly used metric in NLP for evaluating language models, and it has its pros and cons. Despite its shortcomings, perplexity is still a useful metric for preliminary model selection. Once you’ve narrowed down your models using perplexity, you can evaluate them using other metrics to get a more accurate picture of their performance.