arXiv:2603.03305v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to generate executable outputs, JSON objects, and API calls, where a single syntax error can make the output unusable. Constrained decoding enforces validity token-by-token via masking and renormalization, but it can distort generation when the model assigns low probability mass to valid continuations, pushing decoding toward locally valid yet semantically incorrect trajectories. We propose \emph{Draft-Conditioned Constrained Decoding (DCCD)}, a simple two-step, training-free inference
The increasing reliance on LLMs for executable code and structured data necessitates efficient and reliable constrained decoding methods to ensure output validity.
This development addresses a critical flaw in LLM output generation, making them more reliable for real-world applications requiring precise, valid, and semantically correct structured data, thereby accelerating their integration into automated workflows.
LLMs can now generate structured outputs like JSON and API calls with higher accuracy and semantic correctness, reducing the need for extensive post-processing or error handling.
- · AI developers
- · Software engineers
- · API-driven platforms
- · Automated systems integrators
- · Manual data validation services
- · LLM error correction tools
Improved reliability and usability of LLMs for generating structured data and executable code.
Accelerated adoption of LLMs in business process automation and software development.
Enhanced trust in AI-generated outputs leading to broader societal reliance on autonomous systems for critical functions.
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Read at arXiv cs.LG