# Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering

arXiv: https://arxiv.org/abs/2404.14741 · HTML: https://mlsanity.com/paper/2404.14741/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://mlsanity.com/paper/2210.02406/) (2022)
- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Complexity-Based Prompting for Multi-Step Reasoning](https://mlsanity.com/paper/2210.00720/) (2022)
- [StructGPT: A General Framework for Large Language Model to Reason over Structured Data](https://mlsanity.com/paper/2305.09645/) (2023)
- [Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning](https://mlsanity.com/paper/2310.01061/) (2023)
- [Generate rather than Retrieve: Large Language Models are Strong Context Generators](https://mlsanity.com/paper/2209.10063/) (2022)
- [Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph](https://mlsanity.com/paper/2307.07697/) (2023)
- [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)
- [Few-shot In-context Learning for Knowledge Base Question Answering](https://mlsanity.com/paper/2305.01750/) (2023)
- [SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Bases](https://mlsanity.com/paper/2003.13956/) (2020)
- [Graph Reasoning for Question Answering with Triplet Retrieval](https://mlsanity.com/paper/2305.18742/) (2023)
- [Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments](https://mlsanity.com/paper/2403.08593/) (2024)
- [keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM](https://mlsanity.com/paper/2401.00426/) (2023)
- [Large Language Models Meet Knowledge Graphs to Answer Factoid Questions](https://mlsanity.com/paper/2310.02166/) (2023)

## Built on by (most cited first, 30 of 60)

- [AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents](https://mlsanity.com/paper/2407.04363/) (2024)
- [Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving](https://mlsanity.com/paper/2507.06229/) (2025)
- [FlowBench: Revisiting and Benchmarking Workflow-Guided Planning for LLM-based Agents](https://mlsanity.com/paper/2406.14884/) (2024)
- [Rethinking Memory in LLM based Agents: Representations, Operations, and Emerging Topics](https://mlsanity.com/paper/2505.00675/) (2025)
- [Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective](https://mlsanity.com/paper/2411.14258/) (2024)
- [GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments](https://mlsanity.com/paper/2504.00711/) (2025)
- [Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning](https://mlsanity.com/paper/2501.01124/) (2025)
- [Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking](https://mlsanity.com/paper/2505.23495/) (2025)
- [Knowledge Graph Enhanced Language Agents for Recommendation](https://mlsanity.com/paper/2410.19627/) (2024)
- [Advancing Language Multi-Agent Learning with Credit Re-Assignment for Interactive Environment Generalization](https://mlsanity.com/paper/2502.14496/) (2025)
- [Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning](https://mlsanity.com/paper/2503.05193/) (2025)
- [What Breaks Knowledge Graph based RAG? Benchmarking and Empirical Insights into Reasoning under Incomplete Knowledge](https://mlsanity.com/paper/2508.08344/) (2025)
- [PrivGemo: Privacy-Preserving Dual-Tower Graph Retrieval for Empowering LLM Reasoning with Memory Augmentation](https://mlsanity.com/paper/2601.08739/) (2026)
- [EarthSE: A Benchmark for Evaluating Earth Scientific Exploration Capability of LLMs](https://mlsanity.com/paper/2505.17139/) (2025)
- [Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph](https://mlsanity.com/paper/2411.19064/) (2024)
- [Relink: Constructing Query-Driven Evidence Graph On-the-Fly for GraphRAG](https://mlsanity.com/paper/2601.07192/) (2026)
- [RebuttalAgent: Strategic Persuasion in Academic Rebuttal via Theory of Mind](https://mlsanity.com/paper/2601.15715/) (2026)
- [A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data](https://mlsanity.com/paper/2601.17717/) (2026)
- [GraphWalker: Agentic Knowledge Graph Question Answering via Synthetic Trajectory Curriculum](https://mlsanity.com/paper/2603.28533/) (2026)
- [LLM-based Discriminative Reasoning for Knowledge Graph Question Answering](https://mlsanity.com/paper/2412.12643/) (2024)
- [Search-on-Graph: Iterative Informed Navigation for Large Language Model Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2510.08825/) (2025)
- [RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation](https://mlsanity.com/paper/2605.09907/) (2026)
- [HealthGenie: Empowering Users with Healthy Dietary Guidance through Knowledge Graph and Large Language Models](https://mlsanity.com/paper/2504.14594/) (2025)
- [Knowledge-Level Consistency Reinforcement Learning: Dual-Fact Alignment for Long-Form Factuality](https://mlsanity.com/paper/2509.23765/) (2025)
- [KnowPath: Knowledge-enhanced Reasoning via LLM-generated Inference Paths over Knowledge Graphs](https://mlsanity.com/paper/2502.12029/) (2025)
- [Incomplete Graph Learning: A Comprehensive Survey](https://mlsanity.com/paper/2502.12412/) (2025)
- [Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering](https://mlsanity.com/paper/2505.14099/) (2025)
- [Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning](https://mlsanity.com/paper/2505.17086/) (2025)
- [LLM Inference Enhanced by External Knowledge: A Survey](https://mlsanity.com/paper/2505.24377/) (2025)
- [Debate over Mixed-knowledge: A Robust Multi-Agent Reasoning Framework for Incomplete Knowledge Graph Question Answering](https://mlsanity.com/paper/2511.12208/) (2025)

## Abstract (arXiv)

To address the issues of insufficient knowledge and hallucination in Large Language Models (LLMs), numerous studies have explored integrating LLMs with Knowledge Graphs (KGs). However, these methods are typically evaluated on conventional Knowledge Graph Question Answering (KGQA) with complete KGs, where all factual triples required for each question are entirely covered by the given KG. In such cases, LLMs primarily act as an agent to find answer entities within the KG, rather than effectively integrating the internal knowledge of LLMs and external knowledge sources such as KGs. In fact, KGs are often incomplete to cover all the knowledge required to answer questions. To simulate these real-world scenarios and evaluate the ability of LLMs to integrate internal and external knowledge, we propose leveraging LLMs for QA under Incomplete Knowledge Graph (IKGQA), where the provided KG lacks some of the factual triples for each question, and construct corresponding datasets. To handle IKGQA, we propose a training-free method called Generate-on-Graph (GoG), which can generate new factual triples while exploring KGs. Specifically, GoG performs reasoning through a Thinking-Searching-Generating framework, which treats LLM as both Agent and KG in IKGQA. Experimental results on two datasets demonstrate that our GoG outperforms all previous methods.