Causal language modeling (CLM), in the context of natural language processing (NLP) and artificial intelligence, refers to the generation of text or language in a way that it follows a causal or sequential order, typically from left to right.
This is in contrast to autoregressive language modeling, where the model generates text by predicting the next word or token based on the preceding context, which can be a mix of left and right context.
In a causal language model, the generation process is unidirectional (hence also known as Unidirectional Model), with each token generated only depending on the tokens to its left in the input sequence. This can be thought of as simulating the process of writing or speaking, where you start from the beginning of a sentence or sequence and continue to add words or tokens one after another, without looking ahead to the rightmost part of the sequence.
One popular architecture for causal language modeling is the Transformer architecture, which has been used in models like GPT (Generative Pre-trained Transformer). In these models, each token is generated based on the information encoded in the preceding tokens, and this generation process proceeds sequentially.
Causal language models have been widely used for various NLP tasks, including text generation, language translation, and text completion, among others. They are particularly useful for tasks where the order of the generated text is important and needs to follow a specific sequence, such as in storytelling, dialogue generation, and code generation. Predict the next token based on the previous sequence of tokens.
See Also: Autoregressive Models