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Conversational BestLLM Model
+have+demonstrated+great+potential+in+Conversational+Recommender+Systems+(CRS).+However%252C+the+application+of+LLMs+to+CRS+has+exposed+a+notable+discrepancy+in+behavior+between+LLM-based+CRS+and+human+recommenders%3a+LLMs+often+appear+inflexible+and+passive%252C+frequently+rushing+to+complete+the+recommendation+task+without+sufficient+inquiry.This+behavior+discrepancy+can+lead+to+decreased+accuracy+in+recommendations+and+lower+user+satisfaction.+Despite+its+importance%252C+existing+studies+in+CRS+lack+a+study+about+how+to+measure+such+behavior+discrepancy.+To+fill+this+gap%252C+we+propose+Behavior+Alignment%252C+a+new+evaluation+metric+to+measure+how+well+the+recommendation+strategies+made+by+a+LLM-based+CRS+are+consistent+with+human+recommenders%27.+Our+experiment+results+show+that+the+new+metric+is+better+aligned+with+human+preferences+and+can+better+differentiate+how+systems+perform+than+existing+evaluation+metrics.+As+Behavior+Alignment+requires+explicit+and+costly+human+annotations+on+the+recommendation+strategies%252C+we+also+propose+a+classification-based+method+to+implicitly+measure+the+Behavior+Alignment+based+on+the+responses.+The+evaluation+results+confirm+the+robustness+of+the+method.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.22d072400503d8b3fe83e56617f397c7%3frik%3de4EjsCjwZ%252bi03Q%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Conversational+Best+LLM+Model&FORM=IRPRST&ck=623E88D77AF44AD67A08CCD2000FD6B2&selectedIndex=29&itb=0)
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