# LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs

arXiv: https://arxiv.org/abs/2402.11804 · HTML: https://mlsanity.com/paper/2402.11804/

## Key facts

- Cited by 13 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 16 of its 62 references that are in the map.
- First posted on arXiv 19 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, 16 of 16)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](https://mlsanity.com/paper/1911.06136/) (2019)
- [Language is All a Graph Needs](https://mlsanity.com/paper/2308.07134/) (2023)
- [LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities](https://mlsanity.com/paper/2305.13168/) (2023)
- [Towards Foundation Models for Knowledge Graph Reasoning](https://mlsanity.com/paper/2310.04562/) (2023)
- [A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs](https://mlsanity.com/paper/2206.04798/) (2022)
- [A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes](https://mlsanity.com/paper/2307.06046/) (2023)
- [Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types](https://mlsanity.com/paper/2302.01313/) (2023)
- [Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery](https://mlsanity.com/paper/2105.10488/) (2021)
- [SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation](https://mlsanity.com/paper/2305.09958/) (2023)
- [Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning](https://mlsanity.com/paper/2005.10831/) (2020)
- [Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model](https://mlsanity.com/paper/2312.01837/) (2023)
- [River of No Return: Graph Percolation Embeddings for Efficient Knowledge Graph Reasoning](https://mlsanity.com/paper/2305.09974/) (2023)

## Built on by (most cited first, 13 of 13)

- [Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning](https://mlsanity.com/paper/2410.14211/) (2024)
- [SirLLM: Streaming Infinite Retentive LLM](https://mlsanity.com/paper/2405.12528/) (2024)
- [SEMMA: A Semantic Aware Knowledge Graph Foundation Model](https://mlsanity.com/paper/2505.20422/) (2025)
- [Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs](https://mlsanity.com/paper/2410.12609/) (2024)
- [Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications](https://mlsanity.com/paper/2510.21131/) (2025)
- [Large Language Models for Knowledge Graph Embedding: A Survey](https://mlsanity.com/paper/2501.07766/) (2025)
- [Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning](https://mlsanity.com/paper/2505.21926/) (2025)
- [Knowledge Reasoning Language Model: Unifying Knowledge and Language for Inductive Knowledge Graph Reasoning](https://mlsanity.com/paper/2510.13909/) (2025)
- [Bridging Graph Structure and Knowledge-Guided Editing for Interpretable Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2601.21978/) (2026)
- [Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey](https://mlsanity.com/paper/2607.09666/) (2026)
- [Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2607.26909/) (2026)
- [Structure-Internalized Rule Language Model for Faithful Knowledge Graph Reasoning](https://mlsanity.com/paper/2608.17443/) (2026)
- [Reification as a Transferable Vocabulary: Zero-Shot Link Prediction with Vanilla GNNs](https://mlsanity.com/paper/2609.11347/) (2026)

## Abstract (arXiv)

Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications. One critical challenge of KG inductive reasoning is handling low-resource scenarios with scarcity in both textual and structural aspects. In this paper, we attempt to address this challenge with Large Language Models (LLMs). Particularly, we utilize the state-of-the-art LLMs to generate a graph-structural prompt to enhance the pre-trained Graph Neural Networks (GNNs), which brings us new methodological insights into the KG inductive reasoning methods, as well as high generalizability in practice. On the methodological side, we introduce a novel pretraining and prompting framework ProLINK, designed for low-resource inductive reasoning across arbitrary KGs without requiring additional training. On the practical side, we experimentally evaluate our approach on 36 low-resource KG datasets and find that ProLINK outperforms previous methods in three-shot, one-shot, and zero-shot reasoning tasks, exhibiting average performance improvements by 20%, 45%, and 147%, respectively. Furthermore, ProLINK demonstrates strong robustness for various LLM promptings as well as full-shot scenarios.