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HyperG: Hypergraph-Enhanced LLMs for Structured Knowledge

Sirui Huang, Hanqian Li, Yanggan Gu, Xuming Hu, Qing Li, Guandong Xu

TL;DR

HyperG, a hypergraph-based generation framework aimed at enhancing LLMs' ability to process structured knowledge, is proposed, which first augment sparse data with contextual information, leveraging the generative power of LLMs, and incorporate a prompt-attentive hypergraph learning (PHL) network to encode both the augmented information and the intricate structural relationships within the data.

Abstract

Given that substantial amounts of domain-specific knowledge are stored in structured formats, such as web data organized through HTML, Large Language Models (LLMs) are expected to fully comprehend this structured information to broaden their applications in various real-world downstream tasks. Current approaches for applying LLMs to structured data fall into two main categories: serialization-based and operation-based methods. Both approaches, whether relying on serialization or using SQL-like operations as an intermediary, encounter difficulties in fully capturing structural relationships and effectively handling sparse data. To address these unique characteristics of structured data, we propose HyperG, a hypergraph-based generation framework aimed at enhancing LLMs' ability to process structured knowledge. Specifically, HyperG first augment sparse data with contextual information, leveraging the generative power of LLMs, and incorporate a prompt-attentive hypergraph learning (PHL) network to encode both the augmented information and the intricate structural relationships within the data. To validate the effectiveness and generalization of HyperG, we conduct extensive experiments across two different downstream tasks requiring structured knowledge.

HyperG: Hypergraph-Enhanced LLMs for Structured Knowledge

TL;DR

HyperG, a hypergraph-based generation framework aimed at enhancing LLMs' ability to process structured knowledge, is proposed, which first augment sparse data with contextual information, leveraging the generative power of LLMs, and incorporate a prompt-attentive hypergraph learning (PHL) network to encode both the augmented information and the intricate structural relationships within the data.

Abstract

Given that substantial amounts of domain-specific knowledge are stored in structured formats, such as web data organized through HTML, Large Language Models (LLMs) are expected to fully comprehend this structured information to broaden their applications in various real-world downstream tasks. Current approaches for applying LLMs to structured data fall into two main categories: serialization-based and operation-based methods. Both approaches, whether relying on serialization or using SQL-like operations as an intermediary, encounter difficulties in fully capturing structural relationships and effectively handling sparse data. To address these unique characteristics of structured data, we propose HyperG, a hypergraph-based generation framework aimed at enhancing LLMs' ability to process structured knowledge. Specifically, HyperG first augment sparse data with contextual information, leveraging the generative power of LLMs, and incorporate a prompt-attentive hypergraph learning (PHL) network to encode both the augmented information and the intricate structural relationships within the data. To validate the effectiveness and generalization of HyperG, we conduct extensive experiments across two different downstream tasks requiring structured knowledge.

Paper Structure

This paper contains 23 sections, 11 equations, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Problem Definition
  3. Methodology
  4. Contextual Augmentation
  5. Structured Knowledge Learning
  6. Semantics Hypergraph Construction (SHC)
  7. Prompt-attentive Hypergraph Learning (PHL)
  8. Structured Knowledge Integration
  9. Encoding Structured Knowledge
  10. Training
  11. Experiments
  12. Experimental Setups
  13. Tasks
  14. Baselines
  15. Evaluation Protocol
  16. ...and 8 more sections

Figures (6)

  • Figure 1: An example illustrates the three aspects of the structural relationships in tables: i) Semantic Consistency, ii) Hierarchical Dependencies, and iii) Order Invariance. Additionally, it highlights the data sparsity issue iv), where incomplete data affects SQL queries over the table .
  • Figure 2: An overview of our proprosed HyperG framework.
  • Figure 3: The detailed architecture of PHL.
  • Figure 4: Performances of HyperG under different variances of order simulated by shuffling.
  • Figure 5: Visualization of the weights between cell nodes and different hyperedges in two random cases.
  • ...and 1 more figures