Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Path

• URL: http://arxiv.org/abs/2408.09529v1
• Date: Sun, 18 Aug 2024 16:26:39 GMT
• Title: Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Path
• Authors: Xinnan Dai, Qihao Wen, Yifei Shen, Hongzhi Wen, Dongsheng Li, Jiliang Tang, Caihua Shan,
• Abstract summary: 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.
• Score: 53.71787069694794
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have achieved great success in various reasoning tasks. In this work, we focus on the graph reasoning ability of LLMs. Although theoretical studies proved that LLMs are capable of handling graph reasoning tasks, empirical evaluations reveal numerous failures. To deepen our understanding on this discrepancy, 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 three fundamental tasks. Meanwhile, we perform a real-world investigation on knowledge graphs and make consistent observations with our findings. The codes and datasets are available.

Related papers

• 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)
• Graph Reasoning with Large Language Models via Pseudo-code Prompting [25.469214467011362]
  This paper investigates whether prompting via pseudo-code instructions can improve the performance of large language models (LLMs) in solving graph problems. Our experiments demonstrate that using pseudo-code instructions generally improves the performance of all considered LLMs.
  arXiv  Detail & Related papers  (2024-09-26T14:52:40Z)
• Can LLM Graph Reasoning Generalize beyond Pattern Memorization? [46.93972334344908]
  We evaluate whether large language models (LLMs) can go beyond semantic, numeric, structural, reasoning patterns in the synthetic training data and improve utility on real-world graph-based tasks. We find that while post-training alignment is most promising for real-world tasks, empowering LLM graph reasoning to go beyond pattern remains an open research question.
  arXiv  Detail & Related papers  (2024-06-23T02:59:15Z)
• 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)
• 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)
• GraphLLM: Boosting Graph Reasoning Ability of Large Language Model [7.218768686958888]
  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%.
  arXiv  Detail & Related papers  (2023-10-09T16:42:00Z)
• Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data [13.524529952170672]
  Large language models (LLMs) have achieved impressive performance on many natural language processing tasks. We aim to assess whether LLMs can effectively process graph data and leverage topological structures to enhance performance. By comparing LLMs' performance with specialized graph models, we offer insights into the strengths and limitations of employing LLMs for graph analytics.
  arXiv  Detail & Related papers  (2023-10-07T23:25:22Z)
• 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)

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.