Constrained Decoding

Constrained decoding is a technique used to guide the output of large language models (LLMs) by enforcing specific constraints during text generation, improving accuracy and reducing undesirable outputs like hallucinations or syntax errors. Current research focuses on integrating constrained decoding with various LLMs and applying it to diverse tasks, including code generation, API call generation, and multi-hop question answering, often employing methods like finite-state machines or trie data structures to efficiently manage constraints. This approach is significant because it enhances the reliability and controllability of LLMs, leading to more accurate and trustworthy outputs across numerous applications while minimizing the need for extensive model retraining.