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 in complex question-answering (QA) scenarios has always been a research focal point. Recent studies have attempted to enhance LLMs' performance by combining step-wise planning with external retrieval. While effective for advanced models like GPT-3.5%2C smaller LLMs face challenges in decomposing complex questions%2C necessitating supervised fine-tuning. Previous work has relied on manual annotation and knowledge distillation from teacher LLMs%2C which are time-consuming and not accurate enough. In this paper%2C we introduce a novel framework for enhancing LLMs' planning capabilities by using planning data derived from knowledge graphs (KGs). LLMs fine-tuned with this data have improved planning capabilities%2C better equipping them to handle complex QA tasks that involve retrieval. Evaluations on multiple datasets%2C including our newly proposed benchmark%2C highlight the effectiveness of our framework and the benefits of KG-derived planning data.?quality=80&w=800)
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