Constrained Generation

Constrained generation focuses on controlling the output of generative models, such as large language models (LLMs) and diffusion models, to satisfy specific requirements or constraints, improving the quality, relevance, and safety of generated content. Current research emphasizes developing efficient and flexible methods for incorporating constraints, exploring techniques like prompt engineering, importance sampling, constraint satisfaction problem solvers, and novel decoding algorithms (e.g., A*-esque search). These advancements are crucial for addressing challenges like hallucinations in LLMs, improving data augmentation for low-resource scenarios, and enabling more reliable and controllable applications in diverse fields including natural language processing, computer-aided design, and procedural content generation.