This week, on our Techno Thursdays segment we will explore what ‘Perplexity’ in language models mean & how it measures prediction accuracy. Also, how it helps choose the best AI for tasks like translation & speech recognition.
Perplexity: a term that might sound more suited to a detective novel than the world of artificial intelligence. But in the realm of Natural Language Processing (NLP), perplexity plays a crucial role in evaluating how well language models understand and predict human language.
So, how exactly does perplexity work? Imagine you’re reading a sentence. A good language model shouldn’t be surprised by the next word. Perplexity quantifies this surprise. Lower perplexity indicates the model confidently predicts the next word in a sequence, reflecting higher accuracy. Conversely, high perplexity suggests the model is unsure about what comes next, hinting at lower accuracy.
Here’s the technical side: Perplexity is calculated from the probability distribution assigned by the model to each word. The lower the average probability of the predicted words, the higher the perplexity. It’s essentially a measure of how many possible choices the model has to consider for the next word, with a lower number indicating a more precise prediction.
Perplexity in Action: Evaluating Language Models
Perplexity shines when evaluating various language models for tasks like machine translation, speech recognition, and text generation. Let’s take machine translation. A model with lower perplexity on the target language can translate sentences more fluently and accurately, as it’s better at predicting the natural flow of words. Similarly, in speech recognition, a lower perplexity score suggests the model is making fewer errors in deciphering spoken language.
The Perplexity Race: Comparing Language Models
Since perplexity reflects a model’s ability to predict the next word accurately, it’s a valuable tool for comparing different language models. By comparing perplexity scores, developers can identify models that perform better at understanding and generating human language. This is crucial for selecting the best model for a specific NLP task.
It’s Not All Perplexity and Sunshine
While perplexity is a helpful metric, it has limitations. It doesn’t directly measure factors like fluency, grammar, or factual correctness. A model with low perplexity might generate grammatically correct but nonsensical text. Additionally, perplexity scores can vary depending on the dataset used for evaluation.
Therefore, perplexity is best used as a starting point for evaluating language models. It should be combined with other metrics that assess different aspects of language understanding and generation.
Who’s Winning the Perplexity Race?
Unfortunately, there’s no single answer. Perplexity scores can vary depending on the model architecture, training data, and the specific task being evaluated. However, some of the leading language models like GPT-3 and Jurassic-1 Jumbo demonstrate impressive perplexity scores, indicating their strong ability to predict word sequences.
The Future of Perplexity
As language models continue to evolve, so too will perplexity as a metric. Researchers are exploring ways to refine perplexity to account for factors like fluency and factual correctness. By combining perplexity with other evaluation methods, we can gain a more comprehensive understanding of language model performance and push the boundaries of NLP capabilities.
Perplexity AI: Putting Perplexity into Action
Interestingly, there’s a company independent of any major tech corporation named Perplexity AI that utilizes the concept of perplexity in its search engine. This search engine leverages advanced language models to understand user queries and provide informative answers with cited sources. Perplexity AI goes beyond simply providing search results; it aims to guide users through the information-gathering process using perplexity to assess the confidence and relevance of the information presented.