  Advancements in natural language generation (NLG) and large language models
(LLMs) have led to proficient text generation in various tasks. However,
integrating intricate constraints into neural text generation, due to LLMs'
opacity, remains challenging. This study investigates constrained text
generation for LLMs, where predefined constraints are applied during LLM's
generation process. Our research mainly focuses on mainstream open-source LLMs,
categorizing constraints into lexical, structural, and relation-based types. We
also present various benchmarks to facilitate fair evaluation. The study
addresses some key research questions, including evaluating, understanding and
improving constrained text generation for LLMs. Results illuminate LLMs'
capacity and deficiency to incorporate constraints and provide insights for
future developments in constrained text generation. Codes and datasets will be
released upon acceptance.
