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Distilling Large Language Models for Text-Attributed Graph Learning

Published: 21 October 2024 Publication History

Abstract

Text-Attributed Graphs (TAGs) are graphs of connected textual documents. Graph models can efficiently learn TAGs, but their training heavily relies on human-annotated labels, which are scarce or even unavailable in many applications. Large language models (LLMs) have recently demonstrated remarkable capabilities in few-shot and zero-shot TAG learning, but they suffer from scalability, cost, and privacy issues. Therefore, in this work, we focus on synergizing LLMs and graph models with their complementary strengths by distilling the power of LLMs into a local graph model on TAG learning. To address the inherent gaps between LLMs (generative models for texts) and graph models (discriminative models for graphs), we propose first to let LLMs teach an interpreter with rich rationale and then let a student model mimic the interpreter's reasoning without LLMs' rationale. We convert LLM's textual rationales to multi-level graph rationales to train the interpreter model and align the student model with the interpreter model based on the features of TAGs. Extensive experiments validate the efficacy of our proposed framework.

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    cover image ACM Conferences
    CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
    October 2024
    5705 pages
    ISBN:9798400704369
    DOI:10.1145/3627673
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    Published: 21 October 2024

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    Author Tags

    1. knowledge distillation
    2. large language models
    3. text-attributed graphs

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