What is Neural Language Models?
Neural Language Models (NLMs) use neural networks to carry out language modeling tasks, such as predicting future words and giving word sequences probabilities. Many applications involving Natural Language Processing (NLP) view this capacity as essential.
Compared to more conventional language models like n-grams, NLMs have a number of advantages. Unlike n-gram models whose number of parameters increases exponentially with the size of the context, they can accommodate much longer contexts. Neural network models have the ability to condition on any lengths of sequences as well as the complete history of predictions, in contrast to conventional sequence models that depend on the Markov assumption. NLMs predict words more accurately and generalize over comparable words more effectively. In order to facilitate generalization from the training to the test set, they accomplish this by projecting words onto a continuous space where words with comparable contexts have similar representations.
Several neural network architectures are used for language modelling:
- Early neural language models made use of feedforward neural networks, or FFNNs. These models produce a probability distribution over the potential following words after receiving as input a representation of a certain number of words (such as a k-gram). Based on the N-1 preceding words, they estimate the likelihood of the subsequent word.
- Recurrent neural networks (RNNs), especially those with gated architectures such as Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM), are highly effective at identifying statistical regularities in sequences and are built to handle sequential data. RNN-based language models can condition on the complete prediction history and relax the Markov assumption. It has been demonstrated that they offer far superior perplexities to conventional language models. One kind of RNN used to model tree structures is a recursive neural network.
- More recently, transformers have emerged as the most popular architecture for language modeling. To manage word associations across great distances without depending on recurrent connections, they employ strategies like self-attention and positional encodings.
- Convolutional Neural Networks (CNNs) are neural architectures that are specifically designed to handle language input. They may also be used for language modeling, often down to the character level.
Components of Neural Language Models (NLMs)
An embedding layer is a crucial part of neural networks for language, especially NLMs. In a lower-dimensional space, this layer converts discrete symbols, such as words, into continuous vectors. Generalization is made easier by this transformation, which treats words as mathematical objects where distance might reflect relationships. The language model can learn these word representations while it is being trained.
It is common practice to train neural networks, including NLMs, with optimization algorithms such as gradient descent. Backpropagation on a computing graph is used to calculate the gradient. Both a word predictor and learnt word embeddings are produced by training the parameters to minimize a loss function.
Text generation is an important use case for neural language models. Word by word, models are able to create text auto-regressively by forecasting a probability distribution over the subsequent word conditioned on the previous sequence. This feature, which is a component of the emerging field of artificial intelligence known as generative AI, is emphasized as having great practical significance.
Neural language models Pros and Cons
Neural language models do have several cons despite their pros. Working with very large vocabulary sizes and training corpora can become prohibitively expensive, and predicting a word’s likelihood in context can be far more costly than using a typical language model. In tasks like machine translation, where interpolating the probabilities from both types of models can produce better results, they do not always outperform traditional models, even though they use data more efficiently and can achieve competitive perplexities with smaller training sets. This implies that even while NLMs are superior at generalizing, there are situations where this generalization is undesirable and traditional models’ rigidity is favored.
Large language models are often pre-trained using more current methods, which are usually based on the Transformer architecture. Using the rich representations acquired during pretraining, these models are refined on particular downstream NLP tasks after being pretrained on language modeling tasks. Here’s an illustration of transfer learning. This pretrain-finetune methodology is also commonly employed with masked language modeling frameworks like BERT.