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+and+Machine+Learning+(ML)+face+challenges+in+text+generation+due+to+CP%27s+struggle+with+implementing+meaning%27%27+and+ML%27s+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%252C+while+CP+manages+structural+constraints.+This+approach+builds+on+GenCP%252C+an+improved+version+of+On-the-fly+Constraint+Programming+Search+(OTFS)+using+LLM-generated+domains.+Compared+to+Beam+Search+(BS)%252C+a+standard+NLP+method%252C+this+combined+approach+(GenCP+with+LLM)+is+faster+and+produces+better+results%252C+ensuring+all+constraints+are+satisfied.+This+fusion+of+CP+and+ML+presents+new+possibilities+for+enhancing+text+generation+under+constraints.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.4279a87e8c22ec726ac695ad3fe90c8c%3frik%3dN5%252fR%252fZEBI2Oegg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Adding+Artificial+Constraint+to+a+Linear+Programming+Problem&FORM=IRPRST&ck=A0B9268CDC5341EA6C60F29BFE32B403&selectedIndex=31&itb=0)
