N-Gram Language Models: Training and Text Generation

What is an N-Gram Language Model

An N-gram language model predicts the probability of a sequence of words based on historical data. For instance, in a bigram (N=2) model, the probability of word B following word A is calculated from the frequency of occurrences of 'A B' pairs in a corpus.

The model is trained by analyzing a large text dataset to count how often different sequences of words appear together.

How N-Gram Models Generate Text

Text generation using an N-gram model involves selecting the next word based on the probabilities calculated from the trained model. For a bigram model, this means choosing the most likely word to follow the current one.

The process is repeated iteratively, with each new word influencing the selection of subsequent words until a complete sentence or paragraph is generated.

Perplexity: Measuring Model Quality

Perplexity is a measure of how well a probability distribution or model predicts a sample. In N-gram models, it quantifies the average number of choices required to predict the next word in a sequence.

A lower perplexity indicates better prediction accuracy; thus, a model with low perplexity is considered more effective at generating coherent text.

Smoothing Techniques and Sampling Temperature

Add-k smoothing techniques are used to avoid zero probabilities for unseen word pairs by adding a small constant k to the count of each pair. This prevents overfitting and ensures that all words have some probability.

Sampling temperature controls how random or deterministic the generation process is; higher temperatures lead to more varied and creative outputs, while lower temperatures result in more predictable text.

Frequently asked questions

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Everything above runs in your browser — open N-Gram Language Model: Live Perplexity & Text Generation and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.