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GraphLLM: Boosting Graph Reasoning Ability of Large Language Model

Ziwei Chai, Tianjie Zhang, Liang Wu, Kaiqiao Han, Xiaohai Hu, Xuanwen Huang, Yang Yang

arXiv:2310.05845v1cs.CLcs.AI

TL;DR

LLMs remain underperforming on fundamental graph reasoning tasks, and Graph2Text requires them to infer structure from sequential text and handle lengthy contexts. GraphLLM integrates a graph transformer with an LLM. Across four tasks, it reports improved accuracy, reduced context length, and faster inference.

  • Problem

    LLMs have underwhelming performance on fundamental graph reasoning tasks, while Graph2Text requires recovering implicit graph structure from sequential text and handling lengthy contexts.

    Source: §1 · pp. 1–2

  • Method

    GraphLLM integrates a graph transformer with an LLM and projects graph representations into a graph-enhanced prefix for generating responses.

    Source: §1 · p. 2, §3.4 · p. 5

  • Results

    Across four fundamental graph reasoning tasks, GraphLLM reports a 54.44% average accuracy improvement, 96.45% context reduction, and 3.42x inference acceleration.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The authors present GraphLLM as guidance for future research on enabling LLMs to comprehend graph data and tackle advanced graph-related tasks.

    Source: §6 · p. 9

    The approach assumes nodes or entities can be characterized by textual features, such as social-network user profiles or molecular atom properties.

    Source: §3.1 · p. 3

Abstract

from arXiv · show

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.

1 INTRODUCTION

LLMs remain weak on fundamental graph reasoning tasks, while Graph2Text makes them recover graph structure from sequential text and contend with lengthy contexts. GraphLLM integrates graph learning with LLMs, and the paper reports higher accuracy and reduced context across four tasks.

1 INTRODUCTION

  • Graphs represent entities as nodes and relationships as edges, and graph reasoning is relevant to applications such as drug discovery and chip design.The introduction describes graph understanding as less explored than LLM understanding of audio and images.Source: §1 · p. 1
  • LLMs have shown underwhelming performance on fundamental graph reasoning tasks, motivating the question of what hinders their ability.The introduction cites a 33.5% accuracy result for shortest paths on graphs with up to 20 nodes and reports underwhelming results after fine-tuning LLaMA2-7B/13B.Source: §1 · p. 1
  • Graph2Text can hinder graph reasoning because LLMs must infer implicit graph structure from sequential text and identify essential information in lengthy descriptions.The paper notes that this may make learning from graph descriptions inefficient and may challenge identification of information needed for reasoning.Source: §1 · p. 2
  • GraphLLM improves average accuracy from 43.75% to 98.19% across four fundamental graph reasoning tasks and reduces context length by 96.45%.It combines graph learning models and LLMs end-to-end, condensing graph information into a fixed-length prefix.Source: §1 · p. 2
  • Across four fundamental graph reasoning tasks, GraphLLM reports a 54.44% average accuracy improvement, 96.45% context reduction, and 3.42x inference acceleration.The evaluated tasks are text substructure counting, maximum triplet sum, shortest path, and bipartite graph matching.Source: §1 · p. 2

2 PRELIMINARY

The preliminary section formalizes an input graph as textual node features paired with graph structure, and describes fine-tuning and prefix tuning as relevant LLM procedures.

2 PRELIMINARY

  • An input graph consists of n nodes with textual features and a graph structure E that indicates whether pairs of nodes are related.The graph structure is defined as a binary relation over the node descriptions.Source: §2 · p. 3
  • Fine-tuning on graph reasoning tasks aims to learn task-specific LLM parameters by minimizing an objective over instruction pairs and input graphs.The passage defines the training setup using a dataset of instruction pairs and a task-specific objective function.Source: §2 · p. 3
  • Prefix tuning prepends K trainable continuous tokens to the keys and values at each LLM transformer layer.The prefixes are initialized from a trainable parameter tensor, and the prefixed keys and values are used in attention computation.Source: §2 · p. 3

3 GRAPHLLM

GraphLLM combines node-text understanding with graph-structure modeling, then supplies the resulting graph representation to an LLM through a learned prefix. Its framework assumes graph entities have textual features.

3.1 GENERAL FRAMEWORK OF GRAPHLLM

  • GraphLLM handles node and structure understanding in three stages, assuming graph entities can be characterized by textual features.The stages are node understanding, structure understanding, and graph-enhanced prefix tuning.Source: §3.1 · pp. 3–4

3.2 ENCODER-DECODER FOR NODE UNDERSTANDING

  • A textual encoder-decoder extracts task-relevant information from node descriptions and uses a learned query to produce node representations.The encoder-decoder is newly initialized and updated under guidance from the pretrained LLM; the implementation uses 0.05B parameters with a LLaMA 2 7B backbone.Source: §3.2 · pp. 4–5, §3.1 · p. 4

3.3 GRAPH TRANSFORMER FOR STRUCTURE UNDERSTANDING

  • The graph transformer separately updates node representations and positional encodings, with RRWP-derived pair features encoding structural relationships.For node-level tasks the graph representation is the target node representation; graph-level tasks use mean pooling over node representations.Source: §3.3 · p. 5

3.4 GRAPH-ENHANCED PREFIX TUNING FOR LLMS

  • GraphLLM projects the graph representation into a tunable prefix prepended to each LLM attention layer, incorporating graph context into response generation.When WU is zero, GraphLLM reduces to vanilla prefix tuning.Source: §3.4 · pp. 5–6

4 EXPERIMENT

The experiments evaluate GraphLLM on four graph-reasoning tasks designed to test node and structure understanding. Across the tasks, GraphLLM outperforms Graph2Text-based methods while reducing context and inference time.

4.1 EXPERIMENTAL SETTINGS

  • Four tasks use graphs with textual node descriptions of around 50 tokens to test both node and structure understanding.They cover molecular substructure counting, maximum triplet sum, shortest path, and bipartite graph matching; each task has 2,000/2,000/6,000 training/validation/test instances.Source: §4.1 · pp. 6–7

4.2 PERFORMANCE ON GRAPH REASONING TASKS (Q1)

  • 98.19% vs 47.35% Exact Match Accuracy: GraphLLM surpasses the top Graph2Text-based method averaged across four tasks.Graph2Text-based prompting performs poorly, fine-tuned Graph2Text methods still trail GraphLLM, and adjacency versus edge-list descriptions yield no consistent improvement.Source: Table 2 · p. 7, §4.2 · p. 8
  • 2.61%, 99.8%, 12.22%, and 15.16% relative improvements: GraphLLM with LLaMA 2 7B exceeds GPT-4 few-shot CoT across the four tasks.The GPT-3.5-turbo and GPT-4 Graph2Text evaluation used 30 random samples due to cost.Source: §4.2 · p. 8, Table 3 · p. 8

4.3 COMPARATIVE ANALYSIS ON CONTEXT REDUCTION (Q2)

  • 96.45% average context reduction: GraphLLM encodes node descriptions and structure in a fixed-length prefix of five additional tokens.On substructure counting at 45 nodes, GraphLLM retains 0.9645 accuracy while LLaMA 2 Graph2Text methods exceed the 4096-token context limit.Source: §4.3 · p. 8, Table 4 · p. 9, Figure 4 · p. 9

4.4 ANALYSIS ON COMPUTATIONAL EFFICIENCY (Q3)

  • 3.42× speedup: GraphLLM is faster than the best-performing Graph2Text method on substructure counting.The reported context reduction more than offsets the additional time from the graph learning module.Source: §4.4 · p. 9

5 RELATED WORK

Prior graph–LLM work includes interfaces to graph models or APIs and end-to-end Graph2Text fine-tuning. GraphLLM is presented as an end-to-end integration of graph learning models and LLMs.

5 RELATED WORK

  • Graph-model or API interfaces can limit access to internal graph reasoning, hindering seamless integration of graph learning with LLMs.Source: §5 · p. 9
  • An end-to-end attempt to fine-tune OPT-2.5B on a Graph2Text corpus of basic graph-reasoning tasks did not elicit graph reasoning ability.Source: §5 · p. 9
  • GraphLLM is presented as a unified, end-to-end integration of graph learning models and LLMs.Source: §5 · p. 9

6 DISCUSSION

GraphLLM integrates LLMs with graph learning models in an end-to-end approach to improve graph reasoning. The authors also aim for the work to guide research on enabling LLMs to comprehend graph data and handle advanced graph tasks.

6 DISCUSSION

  • GraphLLM is an end-to-end approach that integrates LLMs with graph learning models to enhance graph reasoning and inform future work on graph-capable LLMs.Source: §6 · p. 9

A.1 DETAILS OF TEXTUAL TRANSFORMER ENCODER-DECODER

  • Figure 6 depicts the textual transformer encoder-decoder: unmasked self-attention contextualizes node text, and decoder queries extract information for node representations.Source: Figure 6 · p. 13, §A.1 · p. 13

A.2 COMPLETE FORMULATION OF GRAPH TRANSFORMER

  • A graph transformer layer combines multi-head attention and a feed-forward network, with residual connections and layer normalization associated with both components.Source: §A.2 · pp. 13–14

B SETUP

The setup reports GraphLLM hyperparameters for four datasets and describes the fairness and search procedures used for comparisons.

  • Fine-tuned LLM baselines were trained for the same number of epochs as GraphLLM to ensure a fair comparison.Source: B SETUP · p. 14
  • The LoRA rank search used {4, 8, 16}, and the prefix-tuning token search used {5, 10, 20}.Source: B SETUP · p. 14
  • Table 5 lists GraphLLM hyperparameters for four datasets.Source: Table 5 · p. 14

C SUPPLEMENTAL EXPERIMENT RESULTS

GraphLLM has an inference-time advantage over Graph2Text-based methods, and the advantage grows more pronounced at larger LLM scales.

C.1 INFERENCE TIME

  • Table 6 reports inference times across four graph-reasoning tasks; GraphLLM is faster than Graph2Text methods, with the advantage more pronounced at larger LLM scales.Source: §C.1 · p. 14

C.2 ABLATION STUDY ON GRAPH TRANSFORMER

The ablation reports lower average accuracies for GIN and GAT variants and indicates the importance of separating node and structural information.

C.2 ABLATION STUDY ON GRAPH TRANSFORMER

  • The authors indicate that decoupling node information from structural information is essential to GraphLLM's structure understanding and graph reasoning.Source: §C.2 · pp. 14–15
  • GIN and GAT variants average 24.2% and 17.3% accuracy, respectively, across the four graph reasoning tasks.Source: §C.2 · p. 14, Table 7 · p. 15

Substructure Counting

The examples cover substructure counting, maximum triplet sum, shortest path, and bipartite matching. Several shown answers give differing totals, while the matching example concludes that six applicants can be assigned jobs.

Substructure Counting

  • For Atom 1, the worked reasoning lists two carbon-carbon-oxygen triangles, while another answer states there are three.The two listed triangles are Atom 1–Atom 2–Atom 3 and Atom 1–Atom 2–Atom 8.Source: Substructure Counting · p. 16

Maximum Triplet Sum

  • The worked maximum-triplet calculation totals 180 from three ages of 60, while another answer gives 160.The calculation selects Person 1, Person 14, and Person 12.Source: Maximum Triplet Sum · p. 17

Shortest Path

  • For the route from wormhole 1 to wormhole 2, one answer totals 100 pounds of dark matter, while another says 80 pounds.The 100-pound calculation adds requirements of 20, 20, 40, and 20 pounds.Source: Shortest Path · p. 18

Bipartite Graph Matching

  • The bipartite-matching example concludes that at most six applicants can find jobs they are interested in.Its assignment leaves Job 7 vacant because Applicant 5 has already been assigned to Job 5.Source: Bipartite Graph Matching · p. 19
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