What are the most common neural networks used in language modeling?

1 Feedforward neural networks

Feedforward neural networks (FNNs) are the simplest type of neural networks, consisting of layers of neurons that are connected by weights, but do not have any feedback loops or memory units. FNNs can be used for language modeling by taking a fixed number of previous words as input, and outputting a probability distribution over the vocabulary for the next word. However, FNNs have some drawbacks, such as the inability to capture long-term dependencies, the need to predefine the input length, and the high computational cost of processing large vocabularies.

2 Recurrent neural networks

Recurrent neural networks (RNNs) are a more advanced type of neural networks, which have feedback loops that allow them to store and update information over time. RNNs can be used for language modeling by taking one word at a time as input, and outputting a probability distribution over the vocabulary for the next word, while also updating their hidden state based on the previous inputs. RNNs can capture long-term dependencies, handle variable input lengths, and reduce the computational cost by sharing weights across time steps. However, RNNs also have some challenges, such as the vanishing or exploding gradient problem, the difficulty of parallelization, and the tendency to forget or overwrite information.

3 Long short-term memory networks

Long short-term memory networks (LSTMs) are a special kind of RNNs, which have memory units that can store and manipulate information over long periods of time. LSTMs can be used for language modeling by taking one word at a time as input, and outputting a probability distribution over the vocabulary for the next word, while also updating their memory units based on the previous inputs and outputs. LSTMs can overcome some of the problems of RNNs, such as the vanishing or exploding gradient problem, by using gates that control the flow of information in and out of the memory units. However, LSTMs still have some limitations, such as the high number of parameters, the complexity of training, and the difficulty of interpreting the memory units.

4 Transformer networks

Transformer networks are a newer type of neural networks, which do not use any recurrent or convolutional layers, but rely on attention mechanisms to learn the relationships between words in a text. Transformer networks can be used for language modeling by taking a sequence of words as input, and outputting a probability distribution over the vocabulary for each word, while also attending to the relevant parts of the input and output sequences. Transformer networks can outperform some of the previous neural networks, such as RNNs and LSTMs, by being able to capture long-range dependencies, handle variable input lengths, and parallelize the computation. However, Transformer networks also have some drawbacks, such as the need for large amounts of data, the high memory consumption, and the lack of sequential information.

5 Here’s what else to consider

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