In the context of large language models (LLMs), a corpus refers to a collection of text documents used for training or evaluation purposes. A corpus can be thought of as a representative sample of a language or a domain, and it is typically selected to be large and diverse enough to capture the nuances and variability of natural language.
Corpora can be created for a specific purpose, such as sentiment analysis or machine translation, or they can be general-purpose, covering a broad range of topics and genres. Corpora can be obtained from various sources, including web pages, books, news articles, social media posts, and scientific publications.
Once a corpus has been collected, it is typically preprocessed to remove noise and irrelevant information, such as HTML tags, punctuation, and stop words. The resulting text is then tokenized into individual words or subwords, which serve as the basic input units for the LLM. The corpus is then used to train the LLM using supervised or unsupervised learning algorithms, depending on the task and the availability of labeled data.
