# An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaboration arXiv: https://arxiv.org/abs/2402.04978 ยท HTML: https://mlsanity.com/paper/2402.04978/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. - Builds on 33 of its 46 references that are in the map. - First posted on arXiv 7 Feb 2024. ## Placed in - LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/) ## Builds on (most cited first, 30 of 33) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022) - [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023) - [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022) - [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022) - [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021) - [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023) - [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023) - [KILT: a Benchmark for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2009.02252/) (2020) - [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019) - [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018) - [KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](https://mlsanity.com/paper/1911.06136/) (2019) - [StructGPT: A General Framework for Large Language Model to Reason over Structured Data](https://mlsanity.com/paper/2305.09645/) (2023) - [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://mlsanity.com/paper/2002.01808/) (2020) - [ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2107.02137/) (2021) - [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://mlsanity.com/paper/2201.05966/) (2022) - [A Survey of the State of Explainable AI for Natural Language Processing](https://mlsanity.com/paper/2010.00711/) (2020) - [PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text](https://mlsanity.com/paper/1904.09537/) (2019) - [Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources](https://mlsanity.com/paper/2305.13269/) (2023) - [Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals](https://mlsanity.com/paper/2101.03737/) (2021) - [UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark](https://mlsanity.com/paper/2103.13009/) (2021) - [CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge](https://mlsanity.com/paper/2109.01653/) (2021) - [DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases](https://mlsanity.com/paper/2210.00063/) (2022) - [Boosting Language Models Reasoning with Chain-of-Knowledge Prompting](https://mlsanity.com/paper/2306.06427/) (2023) - [Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering](https://mlsanity.com/paper/2210.01613/) (2022) - [Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer](https://mlsanity.com/paper/2212.02027/) (2022) - [Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements](https://mlsanity.com/paper/2305.03695/) (2023) ## Built on by (most cited first, 16 of 16) - [Graph Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2408.08921/) (2024) - [AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents](https://mlsanity.com/paper/2407.04363/) (2024) - [Large Language Model Meets Graph Neural Network in Knowledge Distillation](https://mlsanity.com/paper/2402.05894/) (2024) - [SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and Calibration](https://mlsanity.com/paper/2412.12151/) (2024) - [Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications](https://mlsanity.com/paper/2501.11632/) (2025) - [Bridging the Fairness Gap: Enhancing Pre-trained Models with LLM-Generated Sentences](https://mlsanity.com/paper/2501.06795/) (2025) - [Harnessing the Power of Semi-Structured Knowledge and LLMs with Triplet-Based Prefiltering for Question Answering](https://mlsanity.com/paper/2409.00861/) (2024) - [Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Question Answering](https://mlsanity.com/paper/2409.04181/) (2024) - [Pyramid-Driven Alignment: Pyramid Principle Guided Integration of Large Language Models and Knowledge Graphs](https://mlsanity.com/paper/2410.12298/) (2024) - [Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.24245/) (2025) - [Bonsai: Interpretable Tree-Adaptive Grounded Reasoning](https://mlsanity.com/paper/2504.03640/) (2025) - [From Graph Retrieval to Schema Realization: Counterfactual Validation for Text-to-SPARQL over Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/2508.01815/) (2025) - [GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning](https://mlsanity.com/paper/2411.14479/) (2024) - [SAGE: Structure Aware Graph Expansion for Retrieval of Heterogeneous Data](https://mlsanity.com/paper/2602.16964/) (2026) - [SchemaRAG: Dynamic Large Schema Reduction for LLM-driven Structured Information Extraction](https://mlsanity.com/paper/2607.00008/) (2026) - [AGE: Adaptive-masking for Graph Embedding in Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2607.00052/) (2026) ## Abstract (arXiv) While Large Language Models (LLMs) demonstrate exceptional performance in a multitude of Natural Language Processing (NLP) tasks, they encounter challenges in practical applications, including issues with hallucinations, inadequate knowledge updating, and limited transparency in the reasoning process. To overcome these limitations, this study innovatively proposes a collaborative training-free reasoning scheme involving tight cooperation between Knowledge Graph (KG) and LLMs. This scheme first involves using LLMs to iteratively explore KG, selectively retrieving a task-relevant knowledge subgraph to support reasoning. The LLMs are then guided to further combine inherent implicit knowledge to reason on the subgraph while explicitly elucidating the reasoning process. Through such a cooperative approach, our scheme achieves more reliable knowledge-based reasoning and facilitates the tracing of the reasoning results. Experimental results show that our scheme significantly progressed across multiple datasets, notably achieving over a 10% improvement on the QALD10 dataset compared to the best baseline and the fine-tuned state-of-the-art (SOTA) work. Building on this success, this study hopes to offer a valuable reference for future research in the fusion of KG and LLMs, thereby enhancing LLMs' proficiency in solving complex issues.