Perplexity
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Definition: Perplexity is a measure of how well a language model (LM) can predict the next word in a sequence. It is calculated as the exponential of the cross-entropy of the model’s predictions.
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Formula: Perplexity (PP) = exp(Cross-entropy) Cross-entropy = – Σ (p(w_i) * log(q(w_i)))
Where: – p(w_i) is the true probability of the word w_i – q(w_i) is the predicted probability of the word w_i
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Interpretation: A lower perplexity indicates that the language model is better at predicting the next word in a sequence. A perplexity of 1 means that the model is perfectly predicting the next word, while a perplexity of 2 means that the model is only half as good at predicting the next word.
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Factors Affecting Perplexity: The perplexity of a language model can be affected by several factors, including:
- Size of the training data: Models trained on larger datasets tend to have lower perplexity.
- Quality of the training data: Models trained on clean and well-annotated data tend to have lower perplexity.
- Architecture of the model: Different language models have different architectures, which can affect their perplexity.
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Applications of Perplexity: Perplexity is commonly used to evaluate the performance of language models. It can also be used to compare different language models or to track the progress of a model during training.
Rising Trend on Google Trends NG
Perplexity has recently become a trending topic on Google Trends NG. This could be due to the increasing interest in natural language processing (NLP) and machine learning in Nigeria. As more researchers and practitioners in Nigeria explore language models, they are becoming more interested in measuring and improving the performance of these models using metrics such as perplexity.
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Please search for “perplexity” which is rapidly rising on Google Trends NG and explain in detail. Answers should be in English.
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