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KG-o1: Enhancing Multi-hop Question Answering in Large Language Models via Knowledge Graph Integration
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KG-o1 is a novel four-stage framework that integrates Knowledge Graphs (KGs) to enhance Large Language Models' (LLMs) multi-hop reasoning abilities. It involves constructing KG-derived datasets (KG-MHQA SFT and DPO) and using them to fine-tune LLMs (via Supervised Fine-Tuning and a "Self-improved Adaptive DPO" strategy), aiming to improve the LLM's intrinsic knowledge expression and reasoning capabilities during a training stage by internalizing logical paths.
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