Causal language models are designed to predict the next token in a sequence by considering all previous tokens. They use autoregression, where the model generates text one word at a time, having visibility only of the past words but not future ones. This sequential generation is akin to how humans write. Compared to bidirectional models, which consider both past and future context, causal models focus on next-word prediction, making them powerful for tasks like text generation or dialogue systems.