Large language models (LLMs) have demonstrated impressive reasoning abilities
in complex tasks. However, they lack up-to-date knowledge and experience
hallucinations during reasoning, which can lead to incorrect reasoning
processes and diminish their performance and trustworthiness. Knowledge graphs
(KGs), which capture vast amounts of facts in a structured format, offer a
reliable source of knowledge for reasoning. Nevertheless, existing KG-based LLM
reasoning methods only treat KGs as factual knowledge bases and overlook the
importance of their structural information for reasoning. In this paper, we
propose a novel method called reasoning on graphs (RoG) that synergizes LLMs
with KGs to enable faithful and interpretable reasoning. Specifically, we
present a planning-retrieval-reasoning framework, where RoG first generates
relation paths grounded by KGs as faithful plans. These plans are then used to
retrieve valid reasoning paths from the KGs for LLMs to conduct faithful
reasoning. Furthermore, RoG not only distills knowledge from KGs to improve the
reasoning ability of LLMs through training but also allows seamless integration
with any arbitrary LLMs during inference. Extensive experiments on two
benchmark KGQA datasets demonstrate that RoG achieves state-of-the-art
performance on KG reasoning tasks and generates faithful and interpretable
reasoning results.

本论文提出了一种理论上基于知识图谱的新方法 —— 图推理（RoG），该方法通过将大型语言模型（LLMs）与知识图谱（KGs）相结合，实现了忠实和可解释的推理，实验结果表明 RoG 在 KG 推理任务上取得了最先进的性能。

图上推理：忠实且可解释的大型语言模型推理

Reasoning on Graphs: Faithful and Interpretable Large Language Model  Reasoning

Large language models (LLMs) perform better when they produce step-by-step,
"Chain-of-Thought" (CoT) reasoning before answering a question, but it is
unclear if the stated reasoning is a faithful explanation of the model's actual
reasoning (i.e., its process for answering the question). We investigate
hypotheses for how CoT reasoning may be unfaithful, by examining how the model
predictions change when we intervene on the CoT (e.g., by adding mistakes or
paraphrasing it). Models show large variation across tasks in how strongly they
condition on the CoT when predicting their answer, sometimes relying heavily on
the CoT and other times primarily ignoring it. CoT's performance boost does not
seem to come from CoT's added test-time compute alone or from information
encoded via the particular phrasing of the CoT. As models become larger and
more capable, they produce less faithful reasoning on most tasks we study.
Overall, our results suggest that CoT can be faithful if the circumstances such
as the model size and task are carefully chosen.

在大型语言模型中，通过分析模型预测的变化，研究发现 Chain-of-Thought 推理在某些任务中对答案预测的条件具有较大影响，但随着模型规模的增大，模型对大多数任务的推理越来越不可信，因此建议在选择模型规模和任务时谨慎使用 Chain-of-Thought 推理。