GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network

• URL: http://arxiv.org/abs/2402.11709v2
• Date: Fri, 7 Jun 2024 14:36:33 GMT
• Title: GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network
• Authors: Shuzhou Yuan, Ercong Nie, Michael Färber, Helmut Schmid, Hinrich Schütze,
• Abstract summary: Large Language Models (LLMs) exhibit strong In-Context Learning capabilities when prompts with demonstrations are used. Prompt-based fine-tuning proves to be an effective fine-tuning method in low-data scenarios, but high demands on computing resources limit its practicality. GNNavi employs a Graph Neural Network layer to precisely guide the aggregation and distribution of information flow during the processing of prompts.
• Score: 49.91919718254597
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) exhibit strong In-Context Learning (ICL) capabilities when prompts with demonstrations are used. However, fine-tuning still remains crucial to further enhance their adaptability. Prompt-based fine-tuning proves to be an effective fine-tuning method in low-data scenarios, but high demands on computing resources limit its practicality. We address this issue by introducing a prompt-based parameter-efficient fine-tuning (PEFT) approach. GNNavi leverages insights into ICL's information flow dynamics, which indicates that label words act in prompts as anchors for information propagation. GNNavi employs a Graph Neural Network (GNN) layer to precisely guide the aggregation and distribution of information flow during the processing of prompts by hardwiring the desired information flow into the GNN. Our experiments on text classification tasks with GPT-2 and Llama2 show GNNavi surpasses standard prompt-based fine-tuning methods in few-shot settings by updating just 0.2% to 0.5% of parameters. We compare GNNavi with prevalent PEFT approaches, such as prefix tuning, LoRA and Adapter in terms of performance and efficiency. Our analysis reveals that GNNavi enhances information flow and ensures a clear aggregation process.

Related papers

• How to Make LLMs Strong Node Classifiers? [70.14063765424012]
  Language Models (LMs) are challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs) We propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art (SOTA) GNNs on node classification tasks.
  arXiv  Detail & Related papers  (2024-10-03T08:27:54Z)
• 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)
• LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework [30.54068909225463]
  We aim to streamline the GNN design process and leverage the advantages of Large Language Models (LLMs) to improve the performance of GNNs on downstream tasks. We formulate a new paradigm, coined "LLMs-as-Consultants," which integrates LLMs with GNNs in an interactive manner. We empirically evaluate the effectiveness of LOGIN on node classification tasks across both homophilic and heterophilic graphs.
  arXiv  Detail & Related papers  (2024-05-22T18:17:20Z)
• 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)
• ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph [142.42275983201978]
  We propose a subgraph-aware self-attention mechanism to imitate the GNN for performing structured reasoning. We also adopt an adaptation tuning strategy to adapt the model parameters with 20,000 subgraphs with synthesized questions. Experiments show that ReasoningLM surpasses state-of-the-art models by a large margin, even with fewer updated parameters and less training data.
  arXiv  Detail & Related papers  (2023-12-30T07:18:54Z)
• Efficient and effective training of language and graph neural network models [36.00479096375565]
  We put forth an efficient and effective framework termed language model GNN (LM-GNN) to jointly train large-scale language models and graph neural networks. The effectiveness in our framework is achieved by applying stage-wise fine-tuning of the BERT model first with heterogenous graph information and then with a GNN model. We evaluate the LM-GNN framework in different datasets performance and showcase the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2022-06-22T00:23:37Z)
• Measuring and Sampling: A Metric-guided Subgraph Learning Framework for Graph Neural Network [11.017348743924426]
  We propose a Metric-Guided (MeGuide) subgraph learning framework for Graph neural network (GNN) MeGuide employs two novel metrics: Feature Smoothness and Connection Failure Distance to guide the subgraph sampling and mini-batch based training. We demonstrate the effectiveness and efficiency of MeGuide in training various GNNs on multiple datasets.
  arXiv  Detail & Related papers  (2021-12-30T11:00:00Z)
• Robust Optimization as Data Augmentation for Large-scale Graphs [117.2376815614148]
  We propose FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training. FLAG is a general-purpose approach for graph data, which universally works in node classification, link prediction, and graph classification tasks.
  arXiv  Detail & Related papers  (2020-10-19T21:51:47Z)

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.