# Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text arXiv: https://arxiv.org/abs/2402.13415 ยท HTML: https://mlsanity.com/paper/2402.13415/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. - Builds on 19 of its 35 references that are in the map. - First posted on arXiv 20 Feb 2024. ## Placed in - LLM reasoning / [Prompting and chain-of-thought reasoning](https://mlsanity.com/lr/prompting-and-chain-of-thought-reasoning/) ## Builds on (most cited first, 19 of 19) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018) - [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023) - [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022) - [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022) - [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022) - [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023) - [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://mlsanity.com/paper/2210.01240/) (2022) - [Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning](https://mlsanity.com/paper/2205.09712/) (2022) - [ART: Automatic multi-step reasoning and tool-use for large language models](https://mlsanity.com/paper/2303.09014/) (2023) - [CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text](https://mlsanity.com/paper/1908.06177/) (2019) - [Explaining Answers with Entailment Trees](https://mlsanity.com/paper/2104.08661/) (2021) - [Multi-Hop Knowledge Graph Reasoning with Reward Shaping](https://mlsanity.com/paper/1808.10568/) (2018) - [Compositional Semantic Parsing with Large Language Models](https://mlsanity.com/paper/2209.15003/) (2022) - [MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models](https://mlsanity.com/paper/2308.09729/) (2023) - [LAMBADA: Backward Chaining for Automated Reasoning in Natural Language](https://mlsanity.com/paper/2212.13894/) (2022) - [Recursion of Thought: A Divide-and-Conquer Approach to Multi-Context Reasoning with Language Models](https://mlsanity.com/paper/2306.06891/) (2023) ## Built on by (most cited first, 14 of 14) - [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024) - [A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models](https://mlsanity.com/paper/2501.13958/) (2025) - [Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning](https://mlsanity.com/paper/2501.07845/) (2025) - [When Reasoning Meets Information Aggregation: A Case Study with Sports Narratives](https://mlsanity.com/paper/2406.12084/) (2024) - [What Makes a Good Natural Language Prompt?](https://mlsanity.com/paper/2506.06950/) (2025) - [From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context](https://mlsanity.com/paper/2508.07117/) (2025) - [Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance](https://mlsanity.com/paper/2501.11849/) (2025) - [Flowchart2Mermaid: A Vision-Language Model Powered System for Converting Flowcharts into Editable Diagram Code](https://mlsanity.com/paper/2512.02170/) (2025) - [A Structure-Aware Framework for Learning Device Placements on Computation Graphs](https://mlsanity.com/paper/2405.14185/) (2024) - [SeeingEye: Agentic Information Flow Unlocks Multimodal Reasoning In Text-only LLMs](https://mlsanity.com/paper/2510.25092/) (2025) - [PromptPrism: A Linguistically-Inspired Taxonomy for Prompts](https://mlsanity.com/paper/2505.12592/) (2025) - [AURA: Affordance-Understanding and Risk-aware Alignment Technique for Large Language Models](https://mlsanity.com/paper/2508.06124/) (2025) - [T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning](https://mlsanity.com/paper/2603.03790/) (2026) - [EvoRAG: Making Knowledge Graph-based RAG Automatically Evolve through Feedback-driven Backpropagation](https://mlsanity.com/paper/2604.15676/) (2026) ## Abstract (arXiv) Although Large Language Models (LLMs) excel at addressing straightforward reasoning tasks, they frequently struggle with difficulties when confronted by more complex multi-step reasoning due to a range of factors. Firstly, natural language often encompasses complex relationships among entities, making it challenging to maintain a clear reasoning chain over longer spans. Secondly, the abundance of linguistic diversity means that the same entities and relationships can be expressed using different terminologies and structures, complicating the task of identifying and establishing connections between multiple pieces of information. Graphs provide an effective solution to represent data rich in relational information and capture long-term dependencies among entities. To harness the potential of graphs, our paper introduces Structure Guided Prompt, an innovative three-stage task-agnostic prompting framework designed to improve the multi-step reasoning capabilities of LLMs in a zero-shot setting. This framework explicitly converts unstructured text into a graph via LLMs and instructs them to navigate this graph using task-specific strategies to formulate responses. By effectively organizing information and guiding navigation, it enables LLMs to provide more accurate and context-aware responses. Our experiments show that this framework significantly enhances the reasoning capabilities of LLMs, enabling them to excel in a broader spectrum of natural language scenarios.