constrained-generation

This paper introduces a tractable method to control autoregressive large language model generation to satisfy LR(k) context-free grammars in polynomial time, improving over exponential previous methods. It demonstrates that current LLMs often fail to generate sequences satisfying simple nested constraints, motivating the need for efficient constrained generation.

This paper proposes a constrained, verifiable agent framework for open-web data collection that shifts LLM output from free-form code to typed JSON collector configurations, achieving zero execution-stage LLM tokens and low latency on 80 tasks.

This paper evaluates how LLMs perform in the game Taboo under lexical constraints, showing trade-offs between compliance and communicative effectiveness, and finding that models are weaker guessers than humans.

Introduces Constrained Tabular Diffusion for Finance (CTDF), a method that integrates feasibility operations into diffusion sampling to enforce hard constraints for financial synthetic data generation.

This paper proposes a two-stage fine-tuning pipeline combining domain-adaptive fine-tuning and reinforcement learning to generate protein sequences that match a desired amino-acid composition profile while maintaining sequence quality.

LatticeBridge proposes a twisted sequential Monte Carlo decoder for structured sequence generation that improves constraint satisfaction by treating the problem as rare-event inference, outperforming greedy and beam baselines on CommonGen, E2E NLG, and WikiBio.

This paper presents a system for constrained humor generation that uses a generate-many select-best strategy with a preference model learned from human comparisons. It achieved top ranks in English and Chinese subtasks and second in Spanish at SemEval-2026 Task 1.