Pre-trained language models are sophisticated algorithms used by various natural language processing (NLP) applications that allow them to understand natural language. They are already trained using a vast corpus of text in the language of interest, such as English, from which they are able to discern language structure. This allows them to accurately recognize words, sentences, and phrases, as well as interpret the context of sentences.
The first efforts in creating pre-trained language models began in the 1980s and 1990s, with statistical language models such as n-grams and Markov models being the first models put into use in computer-based language processing.
The development of more intelligent systems, such as artificial neural networks, in the 21st century has enabled the creation of more sophisticated pre-trained language models. These models are capable of adapting to many different types of natural language inputs and can be trained quickly and accurately.
These advanced models are now used in applications such as machine translation, text summarization, and conversational agents. They are also used in search engine query refinement and web content classification, which are important components of modern web applications.
The most renowned pre-trained language models are the Google-made BERT, GPT-2 and, more recently, GPT-3. These models, named Transformer language models, are considered some of the most sophisticated and powerful pre-trained language models currently available.
The use of such pre-trained language models has tremendously increased the accessibility and usefulness of natural language processing applications throughout the world. It has enabled small organizations and individual developers to gain access to methods of language analysis previously only employed by large technology companies.
