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+in+complex+question-answering+(QA)+scenarios+has+always+been+a+research+focal+point.+Recent+studies+have+attempted+to+enhance+LLMs%27+performance+by+combining+step-wise+planning+with+external+retrieval.+While+effective+for+advanced+models+like+GPT-3.5%252C+smaller+LLMs+face+challenges+in+decomposing+complex+questions%252C+necessitating+supervised+fine-tuning.+Previous+work+has+relied+on+manual+annotation+and+knowledge+distillation+from+teacher+LLMs%252C+which+are+time-consuming+and+not+accurate+enough.+In+this+paper%252C+we+introduce+a+novel+framework+for+enhancing+LLMs%27+planning+capabilities+by+using+planning+data+derived+from+knowledge+graphs+(KGs).+LLMs+fine-tuned+with+this+data+have+improved+planning+capabilities%252C+better+equipping+them+to+handle+complex+QA+tasks+that+involve+retrieval.+Evaluations+on+multiple+datasets%252C+including+our+newly+proposed+benchmark%252C+highlight+the+effectiveness+of+our+framework+and+the+benefits+of+KG-derived+planning+data.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.3f463c678f4b01d276421c5b7cdda0da%3frik%3dtLBsv6u14u70jw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Retrieval+Augmented+Generation+and+Large+Language+Models+Systems+Flow+Map&FORM=IRPRST&ck=C8BF9FD4AF71D28EED56A1F2DAA9122D&selectedIndex=23&itb=0)
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