LLaSA: Large Language and Structured Data Assistant

• URL: http://arxiv.org/abs/2411.14460v2
• Date: Sun, 09 Feb 2025 17:13:10 GMT
• Title: LLaSA: Large Language and Structured Data Assistant
• Authors: Yao Xu, Shizhu He, Jiabei Chen, Zeng Xiangrong, Bingning Wang, Guang Liu, Jun Zhao, Kang Liu,
• Abstract summary: Graph Neutral Networks (GNNs) have been introduced as an additional modality into the input of Large Language Models (LLMs) We propose textbfLarge textbfLanguage and textbfStructured Data textbfAssistant (LLaSA) to enhance LLMs' ability to handle structured data.
• Score: 27.452536284165273
• License:
• Abstract: Structured data, such as tables, graphs, and databases, play a critical role in plentiful NLP tasks such as question answering and dialogue system. Recently, inspired by Vision-Language Models, Graph Neutral Networks (GNNs) have been introduced as an additional modality into the input of Large Language Models (LLMs) to improve their performance on Structured Knowledge Grounding (SKG) tasks. However, those GNN-enhanced LLMs have the following limitations: (1) They employ diverse GNNs to model varying types of structured data, rendering them unable to uniformly process various forms of structured data. (2) The pretraining of GNNs is coupled with specific LLMs, which prevents GNNs from fully aligning with the textual space and limits their adaptability to other LLMs. To address these issues, we propose \textbf{L}arge \textbf{L}anguage and \textbf{S}tructured Data \textbf{A}ssistant (LLaSA), a general framework for enhancing LLMs' ability to handle structured data. Specifically, we represent various types of structured data in a unified hypergraph format, and use self-supervised learning to pretrain a hypergraph encoder, and a G-Former compressing encoded hypergraph representations with cross-attention. The compressed hypergraph representations are appended to the serialized inputs during training and inference stages of LLMs. Experimental results on multiple SKG tasks show that our pretrained hypergraph encoder can adapt to various LLMs and enhance their ability to process different types of structured data. Besides, LLaSA, with LoRA fine-tuning, outperforms previous SOTA method using full parameters tuning.

Related papers

• Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion [20.973071287301067]
  Large Language Models (LLMs) present massive inherent knowledge and superior semantic comprehension capability. Empirical evidence suggests that LLMs consistently perform worse than conventional knowledge graph completion approaches. We propose a novel instruction-tuning-based method, namely FtG, to address these challenges.
  arXiv  Detail & Related papers  (2024-12-12T09:22:04Z)
• Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision? [62.12375949429938]
  Building transferable Graph Neural Networks (GNNs) with CLIP pipeline is challenging because of three fundamental issues. We leverage multi-modal prompt learning to effectively adapt pre-trained GNN to downstream tasks and data. Our new paradigm embeds the graphs directly in the same space as the Large Language Models (LLMs) by learning both graph prompts and text prompts simultaneously.
  arXiv  Detail & Related papers  (2024-12-11T08:03:35Z)
• GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model [63.774726052837266]
  We introduce a new architecture that deeply integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs) We introduce three key innovations: (1) Structure-Aware Transformers, which incorporate GNN's message-passing capabilities directly into LLM's transformer layers; (2) Graph-Text Cross-Attention, which processes full, uncompressed text from graph nodes and edges; and (3) GNN-LLM Twin Predictor, enabling LLM's flexible autoregressive generation alongside GNN's scalable one-pass prediction.
  arXiv  Detail & Related papers  (2024-12-08T05:49:58Z)
• NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models [26.739650151993928]
  Graphs are a fundamental data structure for representing relationships in real-world scenarios. Applying Large Language Models (LLMs) to graph-related tasks poses significant challenges. We introduce Node Tokenizer for Large Language Models (NT-LLM), a novel framework that efficiently encodes graph structures.
  arXiv  Detail & Related papers  (2024-10-14T17:21:57Z)
• All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks [51.19110891434727]
  Large Language Models (LLMs) with pretrained knowledge and powerful semantic comprehension abilities have recently shown a remarkable ability to benefit applications using vision and text data. E-LLaGNN is a framework with an on-demand LLM service that enriches message passing procedure of graph learning by enhancing a limited fraction of nodes from the graph.
  arXiv  Detail & Related papers  (2024-07-20T22:09:42Z)
• LLaGA: Large Language and Graph Assistant [73.71990472543027]
  Large Language and Graph Assistant (LLaGA) is an innovative model to handle the complexities of graph-structured data. LLaGA excels in versatility, generalizability and interpretability, allowing it to perform consistently well across different datasets and tasks. Our experiments show that LLaGA delivers outstanding performance across four datasets and three tasks using one single model.
  arXiv  Detail & Related papers  (2024-02-13T02:03:26Z)
• Integrating Graphs with Large Language Models: Methods and Prospects [68.37584693537555]
  Large language models (LLMs) have emerged as frontrunners, showcasing unparalleled prowess in diverse applications. Merging the capabilities of LLMs with graph-structured data has been a topic of keen interest. This paper bifurcates such integrations into two predominant categories.
  arXiv  Detail & Related papers  (2023-10-09T07:59:34Z)
• Graph Neural Prompting with Large Language Models [32.97391910476073]
  Graph Neural Prompting (GNP) is a novel plug-and-play method to assist pre-trained language models in learning beneficial knowledge from knowledge graphs. Extensive experiments on multiple datasets demonstrate the superiority of GNP on both commonsense and biomedical reasoning tasks.
  arXiv  Detail & Related papers  (2023-09-27T06:33:29Z)
• Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [51.90524745663737]
  A key innovation is our use of explanations as features, which can be used to boost GNN performance on downstream tasks. Our method achieves state-of-the-art results on well-established TAG datasets. Our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-05-31T03:18:03Z)

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.