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

Shuzhou Yuan, Ercong Nie, Michael Färber, Helmut Schmid, Hinrich Schuetze


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.
Anthology ID:
2024.findings-acl.237
Volume:
Findings of the Association for Computational Linguistics ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand and virtual meeting
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3987–4001
Language:
URL:
https://aclanthology.org/2024.findings-acl.237
DOI:
Bibkey:
Cite (ACL):
Shuzhou Yuan, Ercong Nie, Michael Färber, Helmut Schmid, and Hinrich Schuetze. 2024. GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network. In Findings of the Association for Computational Linguistics ACL 2024, pages 3987–4001, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
Cite (Informal):
GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network (Yuan et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.237.pdf