Causal Language Model
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A "Causal Language Model" (Causal LM) is a type of language model that generates text by predicting the next word or token in a sequence based on the previous words or tokens. It is designed to capture the causal relationships and dependencies between the elements in a text sequence.
In a Causal LM, the model learns to predict the likelihood of a particular word occurring given the context of the preceding words. It assigns probabilities to different words based on their conditional probability, considering the entire sequence of words that came before. This causal relationship allows the model to generate coherent and contextually relevant text.
One of the key characteristics of Causal LMs is their autoregressive nature. This means that the model generates text sequentially, one word or token at a time, using the previously generated words as input to predict the next word. This autoregressive approach enables the model to capture long-range dependencies and produce coherent and grammatically correct text.
Causal LMs are often trained on large-scale text datasets using techniques like maximum likelihood estimation, where the model aims to maximize the likelihood of the training data. During training, the model learns to assign higher probabilities to the correct next words and lower probabilities to incorrect or unlikely choices.
These models have been widely used in various natural language processing tasks, such as language generation, machine translation, text completion, and dialogue systems. They are known for their ability to generate fluent and contextually appropriate text, making them valuable tools for a wide range of applications.