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%20in%20complex%20question-answering%20(QA)%20scenarios%20has%20always%20been%20a%20research%20focal%20point.%20Recent%20studies%20have%20attempted%20to%20enhance%20LLMs'%20performance%20by%20combining%20step-wise%20planning%20with%20external%20retrieval.%20While%20effective%20for%20advanced%20models%20like%20GPT-3.5%2C%20smaller%20LLMs%20face%20challenges%20in%20decomposing%20complex%20questions%2C%20necessitating%20supervised%20fine-tuning.%20Previous%20work%20has%20relied%20on%20manual%20annotation%20and%20knowledge%20distillation%20from%20teacher%20LLMs%2C%20which%20are%20time-consuming%20and%20not%20accurate%20enough.%20In%20this%20paper%2C%20we%20introduce%20a%20novel%20framework%20for%20enhancing%20LLMs'%20planning%20capabilities%20by%20using%20planning%20data%20derived%20from%20knowledge%20graphs%20(KGs).%20LLMs%20fine-tuned%20with%20this%20data%20have%20improved%20planning%20capabilities%2C%20better%20equipping%20them%20to%20handle%20complex%20QA%20tasks%20that%20involve%20retrieval.%20Evaluations%20on%20multiple%20datasets%2C%20including%20our%20newly%20proposed%20benchmark%2C%20highlight%20the%20effectiveness%20of%20our%20framework%20and%20the%20benefits%20of%20KG-derived%20planning%20data.)
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![Large Language Models: What Is It & Its Applications [Updated]](https://cdn.labellerr.com/Foundation%20Models/Introduction%20to%20LLM/LLMs.webp)
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