Causal Language Model
A Causal Language Model is an autoregressive model trained to predict the next token in a sequence given only the preceding tokens. It uses attention masking to prevent the model from looking at future tokens during training.
Frequently Asked Questions
What is the difference between causal and masked language models?▼
Causal models predict the next token based only on left context (e.g. GPT). Masked models predict hidden tokens based on both left and right context (e.g. BERT).
Why are causal language models preferred for chatbots?▼
Because they are optimized for step-by-step generative decoding, mimicking the way humans construct sentences word by word.
Quick Facts
- CategoryNatural Language Processing
- Key ApplicationConversational agents, creative text generation, and code autocomplete systems.
Coverage Trend12 Weeks
Related AI Terms
Causal Language Model Media Coverage & Intelligence
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