  Constraint Programming (CP) and Machine Learning (ML) face challenges in text
generation due to CP's struggle with implementing "meaning'' and ML's
difficulty with structural constraints. This paper proposes a solution by
combining both approaches and embedding a Large Language Model (LLM) in CP. The
LLM handles word generation and meaning, while CP manages structural
constraints. This approach builds on GenCP, an improved version of On-the-fly
Constraint Programming Search (OTFS) using LLM-generated domains. Compared to
Beam Search (BS), a standard NLP method, this combined approach (GenCP with
LLM) is faster and produces better results, ensuring all constraints are
satisfied. This fusion of CP and ML presents new possibilities for enhancing
text generation under constraints.
