Perplexity is a crucial metric for evaluating language models in NLP. Discover what perplexity measures and how it indicates a model’s performance on unseen data.
Table of Contents
Question
In the context of NLP, what does “perplexity” measure in language models?
A. The level of confusion among the model’s predictions
B. The model’s ability to generate complex language constructs
C. The computational complexity of the model’s architecture
D. The model’s overall ability to predict unseen data
Answer
D. The model’s overall ability to predict unseen data
Explanation
In the context of Natural Language Processing (NLP), perplexity is a metric used to evaluate the performance of language models. Specifically, perplexity measures a language model’s overall ability to predict unseen data (Choice D).
Perplexity quantifies how well a probabilistic model, such as a language model, predicts a sample. It is calculated by taking the exponential of the average negative log-likelihood of a sequence of words. A lower perplexity score indicates that the model is better at predicting the next word in a sequence, given the words that precede it.
In simpler terms, perplexity measures how “surprised” or uncertain the model is when encountering new, unseen data. A model with lower perplexity is less surprised and more confident in its predictions, indicating better performance and generalization to new data.
Perplexity does not directly measure the level of confusion among the model’s predictions (Choice A), the model’s ability to generate complex language constructs (Choice B), or the computational complexity of the model’s architecture (Choice C). These aspects may indirectly impact perplexity but are not what perplexity specifically quantifies.
In summary, perplexity is a key metric in NLP that measures a language model’s overall ability to predict unseen data, with lower perplexity scores indicating better performance and generalization.
Infosys Certified Applied Generative AI Professional certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Infosys Certified Applied Generative AI Professional exam and earn Infosys Certified Applied Generative AI Professional certification.