GraphLLM: Boosting Graph Reasoning Ability of Large Language Model

• URL: http://arxiv.org/abs/2310.05845v1
• Date: Mon, 9 Oct 2023 16:42:00 GMT
• Title: GraphLLM: Boosting Graph Reasoning Ability of Large Language Model
• Authors: Ziwei Chai, Tianjie Zhang, Liang Wu, Kaiqiao Han, Xiaohai Hu, Xuanwen Huang, Yang Yang
• Abstract summary: GraphLLM is a pioneering end-to-end approach that integrates graph learning models with Large Language Models. Our empirical evaluations across four fundamental graph reasoning tasks validate the effectiveness of GraphLLM. The results exhibit a substantial average accuracy enhancement of 54.44%, alongside a noteworthy context reduction of 96.45%.
• Score: 7.218768686958888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advancement of Large Language Models (LLMs) has remarkably pushed the boundaries towards artificial general intelligence (AGI), with their exceptional ability on understanding diverse types of information, including but not limited to images and audio. Despite this progress, a critical gap remains in empowering LLMs to proficiently understand and reason on graph data. Recent studies underscore LLMs' underwhelming performance on fundamental graph reasoning tasks. In this paper, we endeavor to unearth the obstacles that impede LLMs in graph reasoning, pinpointing the common practice of converting graphs into natural language descriptions (Graph2Text) as a fundamental bottleneck. To overcome this impediment, we introduce GraphLLM, a pioneering end-to-end approach that synergistically integrates graph learning models with LLMs. This synergy equips LLMs with the ability to proficiently interpret and reason on graph data, harnessing the superior expressive power of graph learning models. Our empirical evaluations across four fundamental graph reasoning tasks validate the effectiveness of GraphLLM. The results exhibit a substantial average accuracy enhancement of 54.44%, alongside a noteworthy context reduction of 96.45% across various graph reasoning tasks.

Related papers

• A Hierarchical Language Model For Interpretable Graph Reasoning [47.460255447561906]
  We introduce Hierarchical Language Model for Graph (HLM-G), which employs a two-block architecture to capture node-centric local information and interaction-centric global structure. The proposed scheme allows LLMs to address various graph queries with high efficacy, efficiency, and robustness, while reducing computational costs on large-scale graph tasks. Comprehensive evaluations across diverse graph reasoning and real-world tasks of node, link, and graph-levels highlight the superiority of our method.
  arXiv  Detail & Related papers  (2024-10-29T00:28:02Z)
• 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)
• Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models [90.98855064914379]
  We introduce ProGraph, a benchmark for large language models (LLMs) to process graphs. Our findings reveal that the performance of current LLMs is unsatisfactory, with the best model achieving only 36% accuracy. We propose LLM4Graph datasets, which include crawled documents and auto-generated codes based on 6 widely used graph libraries.
  arXiv  Detail & Related papers  (2024-09-29T11:38:45Z)
• 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)
• Parameter-Efficient Tuning Large Language Models for Graph Representation Learning [62.26278815157628]
  We introduce Graph-aware. Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning. We use a graph neural network (GNN) to encode structural information from neighboring nodes into a graph prompt. We validate our approach through comprehensive experiments conducted on 8 different text-rich graphs, observing an average improvement of 2% in hit@1 and Mean Reciprocal Rank (MRR) in link prediction evaluations.
  arXiv  Detail & Related papers  (2024-04-28T18:36:59Z)
• Can Graph Descriptive Order Affect Solving Graph Problems with LLMs? [38.1577036285387]
  Large language models (LLMs) have achieved significant success in reasoning tasks, including mathematical reasoning and logical deduction. Previous studies have explored LLMs' graph reasoning abilities through various techniques. A critical factor has been mostly overlooked: the prompt sequential order in which graph descriptions are presented to the models.
  arXiv  Detail & Related papers  (2024-02-11T09:46:24Z)
• Talk like a Graph: Encoding Graphs for Large Language Models [15.652881653332194]
  We study the first comprehensive study of encoding graph-structured data as text for consumption by large language models (LLMs) We show that LLM performance on graph reasoning tasks varies on three fundamental levels: (1) the graph encoding method, (2) the nature of the graph task itself, and (3) interestingly, the very structure of the graph considered.
  arXiv  Detail & Related papers  (2023-10-06T19:55:21Z)
• Can Language Models Solve Graph Problems in Natural Language? [51.28850846990929]
  Large language models (LLMs) are increasingly adopted for a variety of tasks with implicit graphical structures. We propose NLGraph, a benchmark of graph-based problem solving simulating in natural language.
  arXiv  Detail & Related papers  (2023-05-17T08:29:21Z)
• Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT [10.879701971582502]
  We aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Inspired by the latest ChatGPT and Toolformer models, we propose the Graph-ToolFormer framework to teach LLMs themselves with prompts augmented by ChatGPT to use external graph reasoning API tools.
  arXiv  Detail & Related papers  (2023-04-10T05:25:54Z)

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.