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%20and%20Machine%20Learning%20(ML)%20face%20challenges%20in%20text%20generation%20due%20to%20CP's%20struggle%20with%20implementing%20meaning''%20and%20ML's%20difficulty%20with%20structural%20constraints.%20This%20paper%20proposes%20a%20solution%20by%20combining%20both%20approaches%20and%20embedding%20a%20Large%20Language%20Model%20(LLM)%20in%20CP.%20The%20LLM%20handles%20word%20generation%20and%20meaning%2C%20while%20CP%20manages%20structural%20constraints.%20This%20approach%20builds%20on%20GenCP%2C%20an%20improved%20version%20of%20On-the-fly%20Constraint%20Programming%20Search%20(OTFS)%20using%20LLM-generated%20domains.%20Compared%20to%20Beam%20Search%20(BS)%2C%20a%20standard%20NLP%20method%2C%20this%20combined%20approach%20(GenCP%20with%20LLM)%20is%20faster%20and%20produces%20better%20results%2C%20ensuring%20all%20constraints%20are%20satisfied.%20This%20fusion%20of%20CP%20and%20ML%20presents%20new%20possibilities%20for%20enhancing%20text%20generation%20under%20constraints.)
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