What are the most important generative models for natural language processing?

1 Autoregressive models

Autoregressive models are generative models that generate texts one word at a time, based on the previous words. They use a recurrent neural network (RNN) or a transformer network to capture the sequential dependencies and the long-term context of the texts. Some examples of autoregressive models are GPT-3, BERT, and XLNet. These models can generate fluent and diverse texts, but they have some limitations, such as slow inference speed, exposure bias, and repetition.

2 Variational autoencoders

Variational autoencoders (VAEs) are generative models that learn to encode the input data into a latent space and then decode it back into the output data. They use a probabilistic framework to handle the uncertainty and variability of the data. VAEs can generate texts that are more informative and concise than autoregressive models, but they also suffer from some issues, such as posterior collapse, mode collapse, and high variance.

3 Generative adversarial networks

Generative adversarial networks (GANs) are generative models that consist of two competing networks: a generator and a discriminator. The generator tries to fool the discriminator by producing fake texts, while the discriminator tries to distinguish between real and fake texts. The goal is to train the generator to produce texts that are indistinguishable from the real ones. GANs can generate texts that are more realistic and creative than other models, but they also face some challenges, such as mode collapse, instability, and evaluation.

4 Neural machine translation

Neural machine translation (NMT) is a specific application of generative models for NLP that aims to translate texts from one language to another. NMT models use an encoder-decoder architecture, where the encoder encodes the source text into a vector representation and the decoder generates the target text from the vector. NMT models can use different types of networks, such as RNNs, transformers, or attention mechanisms, to improve the quality and efficiency of the translation. NMT models can generate texts that are more accurate and natural than traditional rule-based or statistical methods, but they also require large amounts of data and computational resources.

5 Text summarization

Text summarization is another application of generative models for NLP that aims to produce a shorter version of a longer text, while preserving the main information and meaning. Text summarization models can be classified into two types: extractive and abstractive. Extractive models select the most important sentences or phrases from the original text, while abstractive models generate new sentences that paraphrase the original text. Text summarization models can use different techniques, such as attention, reinforcement learning, or pre-training, to improve the coherence and relevance of the summary. Text summarization models can generate texts that are more concise and informative than human-written summaries, but they also struggle with some problems, such as redundancy, factual errors, and lack of diversity.

6 Text-to-image synthesis

Text-to-image synthesis is a novel and challenging application of generative models for NLP that aims to generate realistic images from textual descriptions. Text-to-image synthesis models use a combination of natural language understanding and computer vision to bridge the gap between words and pixels. Text-to-image synthesis models can use different architectures, such as conditional GANs, transformers, or style transfer, to enhance the quality and diversity of the images. Text-to-image synthesis models can generate images that are more vivid and expressive than existing methods, but they also face some difficulties, such as semantic alignment, fine-grained details, and evaluation.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?