Think-on-Graph 20: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation
<|reference_start|>Think-on-Graph 20: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation: Retrieval-augmented generation (RAG) has enhanced large language models (LLMs) by using knowledge retrieval to address knowledge gaps. However, existing RAG approaches often fail to ensure the depth and completeness of the information retrieved, which is essential for complex reasoning tasks. In this work, we present Think-on-Graph 2.0 (ToG-2), a hybrid RAG framework that iteratively retrieves information from both unstructured and structured knowledge sources in a tightly integrated manner. Specifically, ToG-2 leverages knowledge graphs (KGs) to connect documents via entities, facilitating deep and knowledge-guided context retrieval. Simultaneously, it uses documents as entity contexts to enable precise and efficient graph retrieval.   ToG-2 alternates between graph retrieval and context retrieval to search for in-depth clues relevant to the question, enabling LLMs to generate accurate answers. We conduct a series of experiments to demonstrate the following advantages of ToG-2: (1) ToG-2 tightly integrates context retrieval and graph retrieval, enhancing context retrieval through the KG while enabling reliable graph retrieval based on contexts; (2) it achieves deep and faithful reasoning in LLMs through an iterative knowledge retrieval process that integrates contexts and the KG; and (3) ToG-2 is training-free and compatible with various LLMs as a plug-and-play solution. Extensive experiments show that ToG-2 achieves state-of-the-art (SOTA) performance on 6 out of 7 knowledge-intensive datasets with GPT-3.5, and can elevate the performance of smaller models (e.g., LLAMA-2-13B) to the level of GPT-3.5's direct reasoning.<|reference_end|>
@article{ma2024think-on-graph,
    title={Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning
  with Knowledge-guided Retrieval Augmented Generation},
    author={Shengjie Ma, Chengjin Xu, Xuhui Jiang, Muzhi Li, Huaren Qu, Cehao
  Yang, Jiaxin Mao, Jian Guo},
    journal={arXiv preprint arXiv:2407.10805},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10805},
    primaryClass={cs.CL cs.AI}
}