Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

• URL: http://arxiv.org/abs/2309.16595v4
• Date: Sat, 15 Jun 2024 09:26:21 GMT
• Title: Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
• Authors: Jin Huang, Xingjian Zhang, Qiaozhu Mei, Jiaqi Ma,
• Abstract summary: Large language models (LLMs) are gaining increasing attention for their capability to process graphs with rich text attributes. We aim to understand why the incorporation of structural information inherent in graph data can improve the prediction performance of LLMs.
• Score: 18.328637750057037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are gaining increasing attention for their capability to process graphs with rich text attributes, especially in a zero-shot fashion. Recent studies demonstrate that LLMs obtain decent text classification performance on common text-rich graph benchmarks, and the performance can be improved by appending encoded structural information as natural languages into prompts. We aim to understand why the incorporation of structural information inherent in graph data can improve the prediction performance of LLMs. First, we rule out the concern of data leakage by curating a novel leakage-free dataset and conducting a comparative analysis alongside a previously widely-used dataset. Second, as past work usually encodes the ego-graph by describing the graph structure in natural language, we ask the question: do LLMs understand the graph structure in accordance with the intent of the prompt designers? Third, we investigate why LLMs can improve their performance after incorporating structural information. Our exploration of these questions reveals that (i) there is no substantial evidence that the performance of LLMs is significantly attributed to data leakage; (ii) instead of understanding prompts as graph structures as intended by the prompt designers, LLMs tend to process prompts more as contextual paragraphs and (iii) the most efficient elements of the local neighborhood included in the prompt are phrases that are pertinent to the node label, rather than the graph structure.

Related papers

• GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design [13.365623514253926]
  Graph In-context Learning (GraphICL) Benchmark is a comprehensive benchmark comprising novel prompt templates to capture graph structure and handle limited label knowledge. Our systematic evaluation shows that general-purpose LLMs equipped with our GraphICL outperform state-of-the-art specialized graph LLMs and graph neural network models.
  arXiv  Detail & Related papers  (2025-01-27T03:50:30Z)
• Each Graph is a New Language: Graph Learning with LLMs [9.22463167477865]
  We present textbfGraph-textbfDefined textbfLanguage for textbfLarge textbfLanguage textbfModel (GDL4LLM) to transfer powerful language understanding capabilities to graph-structured data. GDL4LLM translates graphs into a graph language corpus instead of graph descriptions and pre-trains LLMs on this corpus to adequately understand graph structures.
  arXiv  Detail & Related papers  (2025-01-20T13:20:41Z)
• Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion [20.973071287301067]
  Large Language Models (LLMs) present massive inherent knowledge and superior semantic comprehension capability. Empirical evidence suggests that LLMs consistently perform worse than conventional knowledge graph completion approaches. We propose a novel instruction-tuning-based method, namely FtG, to address these challenges.
  arXiv  Detail & Related papers  (2024-12-12T09:22:04Z)
• How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension [53.6373473053431]
  This work introduces a benchmark to assess large language models' capabilities in graph pattern tasks. We have developed a benchmark that evaluates whether LLMs can understand graph patterns based on either terminological or topological descriptions. Our benchmark encompasses both synthetic and real datasets, and a variety of models, with a total of 11 tasks and 7 models.
  arXiv  Detail & Related papers  (2024-10-04T04:48:33Z)
• GUNDAM: Aligning Large Language Models with Graph Understanding [10.080136100700692]
  We introduce the textbfGraph textbfUnderstanding for textbfNatural Language textbfDriven textbfAnalytical textbfModel (model) This model adapts LLMs to better understand and engage with the structure of graph data, enabling them to perform complex reasoning tasks by leveraging the graph's structure itself.
  arXiv  Detail & Related papers  (2024-09-30T07:59:10Z)
• Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Path [53.71787069694794]
  We focus on the graph reasoning ability of Large Language Models (LLMs) We revisit the ability of LLMs on three fundamental graph tasks: graph description translation, graph connectivity, and the shortest-path problem. Our findings suggest that LLMs can fail to understand graph structures through text descriptions and exhibit varying performance for all these fundamental tasks.
  arXiv  Detail & Related papers  (2024-08-18T16:26:39Z)
• Struct-X: Enhancing Large Language Models Reasoning with Structured Data [38.558614152006975]
  Struct-X operates through five key phases: read-model-fill-reflect-reason'' It encodes structured data into a topological space using graph embeddings. It fills in missing entity information with knowledge retrieval modules. The final phase involves constructing a topological network with selected tokens.
  arXiv  Detail & Related papers  (2024-07-17T13:06:25Z)
• Large Language Models on Graphs: A Comprehensive Survey [77.16803297418201]
  We provide a systematic review of scenarios and techniques related to large language models on graphs. We first summarize potential scenarios of adopting LLMs on graphs into three categories, namely pure graphs, text-attributed graphs, and text-paired graphs. We discuss the real-world applications of such methods and summarize open-source codes and benchmark datasets.
  arXiv  Detail & Related papers  (2023-12-05T14:14:27Z)
• Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs [57.052160123387104]
  We present the Disentangled Graph-Text Learner (DGTL) model, which is able to enhance the reasoning and predicting capabilities of LLMs for TAGs. Our proposed DGTL model incorporates graph structure information through tailored disentangled graph neural network (GNN) layers. Experimental evaluations demonstrate the effectiveness of the proposed DGTL model on achieving superior or comparable performance over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-10-27T14:00:04Z)
• Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs [59.74814230246034]
  Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities. We investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors.
  arXiv  Detail & Related papers  (2023-07-07T05:31:31Z)
• Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [51.90524745663737]
  A key innovation is our use of explanations as features, which can be used to boost GNN performance on downstream tasks. Our method achieves state-of-the-art results on well-established TAG datasets. Our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-05-31T03:18:03Z)

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.