Techniques & Methods
Sequence Generation
Sequence generation is the task of producing ordered sequences of tokens—text, code, audio frames, or actions—given an input context. Autoregressive language models generate sequences one token at a time, using all previously generated tokens as context for each next prediction.
Controlling sequence generation involves techniques like temperature sampling (controlling randomness), beam search (maximizing probability), and constrained decoding (enforcing output formats). These trade-offs between diversity, quality, and constraint satisfaction are central to production LLM systems.
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Related Terms
Techniques & Methods
Autoregression
Statistical modeling approach where future values are predicted from past observed values.
Techniques & Methods
Beam Search
Search algorithm that maintains multiple candidate sequences to find high-quality generated outputs.
Techniques & Methods
Decoding Rules
Guidelines and algorithms that control how language models translate internal representations into output tokens.
Techniques & Methods
Generation
Producing new text, code, or content based on learned patterns and a given input prompt.