Self-Reflective Planning with Knowledge Graphs: Enhancing LLM Reasoning Reliability for Question Answering

• URL: http://arxiv.org/abs/2505.19410v1
• Date: Mon, 26 May 2025 01:59:00 GMT
• Title: Self-Reflective Planning with Knowledge Graphs: Enhancing LLM Reasoning Reliability for Question Answering
• Authors: Jiajun Zhu, Ye Liu, Meikai Bao, Kai Zhang, Yanghai Zhang, Qi Liu,
• Abstract summary: We propose Self-Reflective Planning (SRP), a framework that synergizes large language models with knowledge graphs.<n>In the planning process, SRP first searches for references to guide planning and reflection.<n>After retrieving knowledge from KGs through a reasoning path, it implements iterative reflection by judging the retrieval result and editing the reasoning path until the answer is correctly retrieved.
• Score: 9.601307470705732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, yet they remain prone to hallucinations when reasoning with insufficient internal knowledge. While integrating LLMs with knowledge graphs (KGs) provides access to structured, verifiable information, existing approaches often generate incomplete or factually inconsistent reasoning paths. To this end, we propose Self-Reflective Planning (SRP), a framework that synergizes LLMs with KGs through iterative, reference-guided reasoning. Specifically, given a question and topic entities, SRP first searches for references to guide planning and reflection. In the planning process, it checks initial relations and generates a reasoning path. After retrieving knowledge from KGs through a reasoning path, it implements iterative reflection by judging the retrieval result and editing the reasoning path until the answer is correctly retrieved. Extensive experiments on three public datasets demonstrate that SRP surpasses various strong baselines and further underscore its reliable reasoning ability.

Related papers

• Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities [43.88784275673178]
  Retrieval-Augmented Generation (RAG) shows impressive performance by supplementing and substituting parametric knowledge in Large Language Models (LLMs)<n>We show how triplets located in the gold reasoning path and their variants are used to construct partially relevant knowledge by removing the path that contains the answer.<n>Our awakening-based approach demonstrates greater efficacy in practical applications, outperforms traditional methods that rely on embedding-based similarity.
  arXiv  Detail & Related papers  (2025-08-02T09:54:46Z)
• Reliable Reasoning Path: Distilling Effective Guidance for LLM Reasoning with Knowledge Graphs [14.60537408321632]
  Large language models (LLMs) often struggle with knowledge-intensive tasks due to a lack of background knowledge.<n>We propose the RRP framework to mine the knowledge graph.<n>We also introduce a rethinking module that evaluates and refines reasoning paths according to their significance.
  arXiv  Detail & Related papers  (2025-06-12T09:10:32Z)
• KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing [64.38243807002878]
  We present KnowTrace, an elegant RAG framework to mitigate the context overload in large language models.<n>KnowTrace autonomously traces out desired knowledge triplets to organize a specific knowledge graph relevant to the input question.<n>It consistently surpasses existing methods across three multi-hop question answering benchmarks.
  arXiv  Detail & Related papers  (2025-05-26T17:22:20Z)
• RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement [85.08223786819532]
  Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks.<n>We propose textbfRAG-Star, a novel RAG approach that integrates retrieved information to guide the tree-based deliberative reasoning process.<n>Our experiments involving Llama-3.1-8B-Instruct and GPT-4o demonstrate that RAG-Star significantly outperforms previous RAG and reasoning methods.
  arXiv  Detail & Related papers  (2024-12-17T13:05:36Z)
• FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering [46.41364317172677]
  Large Language Models (LLMs) are often challenged by generating erroneous or hallucinated responses.<n>We propose a unified framework, FiDeLiS, designed to improve the factuality of LLM responses by anchoring answers to verifiable reasoning steps retrieved from Knowledge Graphs.<n>Our method, as a training-free framework, not only improve the performance but also enhance the factuality and interpretability across different benchmarks.
  arXiv  Detail & Related papers  (2024-05-22T17:56:53Z)
• Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs [52.42505579545893]
  Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought explanations alongside answers. We propose a novel discriminative and generative CoT evaluation paradigm to assess LLMs' knowledge of reasoning and the accuracy of the generated CoT.
  arXiv  Detail & Related papers  (2024-02-17T05:22:56Z)
• keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM [27.76205400533089]
  Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, especially for question answering. We present a novel framework to assist LLMs, such as ChatGPT, to retrieve question-related structured information on the knowledge graph. The experimental results on KBQA datasets show that Keqing can achieve competitive performance and illustrate the logic of answering each question.
  arXiv  Detail & Related papers  (2023-12-31T08:39:04Z)
• Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning [104.92384929827776]
  Large language models (LLMs) have demonstrated impressive reasoning abilities in complex tasks. They lack up-to-date knowledge and experience hallucinations during reasoning. Knowledge graphs (KGs) offer a reliable source of knowledge for reasoning.
  arXiv  Detail & Related papers  (2023-10-02T10:14:43Z)
• Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph [29.447300472617826]
  Think-on-Graph (ToG) is a new approach for external knowledge graphs (KG) in large language models (LLMs) ToG iteratively executes beam search on KG, discovers the most promising reasoning paths, and returns the most likely reasoning results. ToG achieves overall SOTA in 6 out of 9 datasets where most previous SOTAs rely on additional training.
  arXiv  Detail & Related papers  (2023-07-15T03:31:38Z)
• Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks [121.74957524305283]
  This paper proposes a novel framework named textbfSearch-in-the-Chain (SearChain) for the interaction between Information Retrieval (IR) and Large Language Model (LLM) Experiments show that SearChain outperforms state-of-the-art baselines on complex knowledge-intensive tasks.
  arXiv  Detail & Related papers  (2023-04-28T10:15:25Z)
• Rethinking with Retrieval: Faithful Large Language Model Inference [91.66406351103484]
  We propose a novel post-processing approach, rethinking with retrieval (RR) RR retrieves relevant external knowledge based on the reasoning steps obtained from the chain-of-thought prompting. We evaluate the effectiveness of RR through extensive experiments with GPT-3 on three complex reasoning tasks.
  arXiv  Detail & Related papers  (2022-12-31T22:35:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.