Publication

Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model

2003
Reports

Abstract

Previous work on statistical language modeling has shown that it is possible to train a feed-forward neural network to approximate probabilities over sequences of words, resulting in significant error reduction when compared to standard baseline models. However, in order to train the model on the maximum likelihood criterion, one has to make, for each example, as many network passes as there are words in the vocabulary. We introduce adaptive importance sampling as a way to accelerate training of the model. We show that a very significant speed-up can be obtained on standard problems.

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2003
Reports

Abstract

Official source

Related concepts (32)

Language model

A language model is a probabilistic model of a natural language that can generate probabilities of a series of words, based on text corpora in one or multiple languages it was trained on. Large language models, as their most advanced form, are a combination of feedforward neural networks and transformers. They have superseded recurrent neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model.

Large language model

A large language model (LLM) is a language model characterized by its large size. Their size is enabled by AI accelerators, which are able to process vast amounts of text data, mostly scraped from the Internet. The artificial neural networks which are built can contain from tens of millions and up to billions of weights and are (pre-)trained using self-supervised learning and semi-supervised learning. Transformer architecture contributed to faster training.

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

Related publications (31)

Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model

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