arXiv:2606.01926v1 Announce Type: new Abstract: Generations from large language models often fail to conform to desired constraints such as JSON schema. Existing locally constrained decoding (LCD) approaches enforce constraints by myopically masking out next tokens, resulting in biased sampling and degradation in performance. Recent work uses sequential Monte Carlo (SMC) methods to mitigate such biases, but designing effective proposal distributions or potential functions remains a key challenge. In this work, we propose a generic approach to construct proposals and potentials for SMC sampling
The increasing deployment of large language models in structured environments necessitates reliable constraint enforcement to prevent output failures and ensure practical utility.
Improving the reliability and performance of constrained language model generation is critical for their adoption in automated workflows and agentic systems, expanding their practical applications.
Techniques for mitigating bias in constrained decoding will lead to more robust and accurate AI outputs, directly impacting the feasibility of true AI agents and structured data generation.
- · AI developers
- · Enterprises adopting AI agents
- · SaaS platforms leveraging LLMs
- · Systems reliant on uncontrolled LLM outputs
- · Traditional manual data structuring
More reliable constraint-following LLMs will enable their use in critical applications requiring strict output formats.
The improved predictability of LLM outputs could accelerate the development and trust in AI agent orchestration.
As AI agents become more reliable, they could begin to autonomously manage more complex, interconnected systems, potentially collapsing various workflow layers.
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Read at arXiv cs.CL