Unleashing the Power of LLMs as Multi-Modal Encoders for Text and Graph-Structured Data

• URL: http://arxiv.org/abs/2410.11235v1
• Date: Tue, 15 Oct 2024 03:40:20 GMT
• Title: Unleashing the Power of LLMs as Multi-Modal Encoders for Text and Graph-Structured Data
• Authors: Jiacheng Lin, Kun Qian, Haoyu Han, Nurendra Choudhary, Tianxin Wei, Zhongruo Wang, Sahika Genc, Edward W Huang, Sheng Wang, Karthik Subbian, Danai Koutra, Jimeng Sun,
• Abstract summary: Graph-structured information offers rich contextual information that can enhance language models. Existing methods for integrating graph and text embeddings are limited in their ability to fully exploit the heterogeneous nature of these modalities. We propose Janus, a framework that leverages Large Language Models (LLMs) to jointly encode text and graph data.
• Score: 42.18348019901044
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph-structured information offers rich contextual information that can enhance language models by providing structured relationships and hierarchies, leading to more expressive embeddings for various applications such as retrieval, question answering, and classification. However, existing methods for integrating graph and text embeddings, often based on Multi-layer Perceptrons (MLPs) or shallow transformers, are limited in their ability to fully exploit the heterogeneous nature of these modalities. To overcome this, we propose Janus, a simple yet effective framework that leverages Large Language Models (LLMs) to jointly encode text and graph data. Specifically, Janus employs an MLP adapter to project graph embeddings into the same space as text embeddings, allowing the LLM to process both modalities jointly. Unlike prior work, we also introduce contrastive learning to align the graph and text spaces more effectively, thereby improving the quality of learned joint embeddings. Empirical results across six datasets spanning three tasks, knowledge graph-contextualized question answering, graph-text pair classification, and retrieval, demonstrate that Janus consistently outperforms existing baselines, achieving significant improvements across multiple datasets, with gains of up to 11.4% in QA tasks. These results highlight Janus's effectiveness in integrating graph and text data. Ablation studies further validate the effectiveness of our method.

Related papers

• TabGLM: Tabular Graph Language Model for Learning Transferable Representations Through Multi-Modal Consistency Minimization [2.1067477213933503]
  TabGLM (Tabular Graph Language Model) is a novel multi-modal architecture designed to model both structural and semantic information from a table. It transforms each row of a table into a fully connected graph and serialized text, which are encoded using a graph neural network (GNN) and a text encoder, respectively. Evaluations across 25 benchmark datasets demonstrate substantial performance gains.
  arXiv  Detail & Related papers  (2025-02-26T05:32:45Z)
• Evaluating and Improving Graph to Text Generation with Large Language Models [46.529034150391595]
  Large language models (LLMs) have demonstrated immense potential across various tasks. We conduct a comprehensive evaluation of prompting current open-source LLMs on graph-to-text generation tasks. We introduce a new graph-to-text dataset, PlanGTG, annotated with two sub-tasks: reordering and attribution.
  arXiv  Detail & Related papers  (2025-01-24T13:53:54Z)
• A Pure Transformer Pretraining Framework on Text-attributed Graphs [50.833130854272774]
  We introduce a feature-centric pretraining perspective by treating graph structure as a prior. Our framework, Graph Sequence Pretraining with Transformer (GSPT), samples node contexts through random walks. GSPT can be easily adapted to both node classification and link prediction, demonstrating promising empirical success on various datasets.
  arXiv  Detail & Related papers  (2024-06-19T22:30:08Z)
• Hierarchical Compression of Text-Rich Graphs via Large Language Models [63.75293588479027]
  Text-rich graphs are prevalent in data mining contexts like e-commerce and academic graphs. This paper introduces Hierarchical Compression'' (HiCom), a novel method to align the capabilities of LLMs with the structure of text-rich graphs. HiCom can outperform both GNNs and LLM backbones for node classification on e-commerce and citation graphs.
  arXiv  Detail & Related papers  (2024-06-13T07:24:46Z)
• TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations [15.873944819608434]
  Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions. This paper introduces a new self-supervised learning framework, Text-And-Graph Multi-View Alignment (TAGA), which integrates TAGs' structural and semantic dimensions. Our framework demonstrates strong performance in zero-shot and few-shot scenarios across eight real-world datasets.
  arXiv  Detail & Related papers  (2024-05-27T03:40:16Z)
• Parameter-Efficient Tuning Large Language Models for Graph Representation Learning [62.26278815157628]
  We introduce Graph-aware. Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning. We use a graph neural network (GNN) to encode structural information from neighboring nodes into a graph prompt. We validate our approach through comprehensive experiments conducted on 8 different text-rich graphs, observing an average improvement of 2% in hit@1 and Mean Reciprocal Rank (MRR) in link prediction evaluations.
  arXiv  Detail & Related papers  (2024-04-28T18:36:59Z)
• Efficient End-to-end Language Model Fine-tuning on Graphs [21.23522552579571]
  Learning from Text-Attributed Graphs (TAGs) has attracted significant attention due to its wide range of real-world applications. We introduce LEADING, a novel and efficient approach for end-to-end fine-tuning of language models on TAGs. Our proposed approach demonstrates superior performance, achieving state-of-the-art (SOTA) results on the ogbn-arxiv leaderboard.
  arXiv  Detail & Related papers  (2023-12-07T22:35:16Z)
• Empower Text-Attributed Graphs Learning with Large Language Models (LLMs) [5.920353954082262]
  We propose a plug-and-play approach to empower text-attributed graphs through node generation using Large Language Models (LLMs) We employ an edge predictor to capture the structural information inherent in the raw dataset and integrate the newly generated samples into the original graph. Experiments demonstrate the outstanding performance of our proposed paradigm, particularly in low-shot scenarios.
  arXiv  Detail & Related papers  (2023-10-15T16:04:28Z)
• 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)
• 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)
• ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings [20.25180279903009]
  We propose Contrastive Graph-Text pretraining (ConGraT) for jointly learning separate representations of texts and nodes in a text-attributed graph (TAG) Our method trains a language model (LM) and a graph neural network (GNN) to align their representations in a common latent space using a batch-wise contrastive learning objective inspired by CLIP. Experiments demonstrate that ConGraT outperforms baselines on various downstream tasks, including node and text category classification, link prediction, and language modeling.
  arXiv  Detail & Related papers  (2023-05-23T17:53:30Z)
• Learning on Large-scale Text-attributed Graphs via Variational Inference [44.558681850874336]
  This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. We propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization framework.
  arXiv  Detail & Related papers  (2022-10-26T13:40:57Z)
• GraphCoCo: Graph Complementary Contrastive Learning [65.89743197355722]
  Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. This paper proposes an effective graph complementary contrastive learning approach named GraphCoCo to tackle the above issue.
  arXiv  Detail & Related papers  (2022-03-24T02:58:36Z)
• Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning [73.0598186896953]
  We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training.
  arXiv  Detail & Related papers  (2020-04-29T14:22:42Z)

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.