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 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%2C while CP manages structural constraints. This approach builds on GenCP%2C an improved version of On-the-fly Constraint Programming Search (OTFS) using LLM-generated domains. Compared to Beam Search (BS)%2C a standard NLP method%2C this combined approach (GenCP with LLM) is faster and produces better results%2C ensuring all constraints are satisfied. This fusion of CP and ML presents new possibilities for enhancing text generation under constraints.?quality=80&w=800)
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