pyg-team/pytorch_geometric

Implementation of LLM-to-LM GNN

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#9,361 创建于 2024年5月26日

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 (3 评论) (0 反应) (1 负责人)Python (19,985 star) (3,514 fork)batch import
1 - Priority P1examplefeaturehelp wanted

描述

🚀 The feature, motivation and pitch

Paper: Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text Attributed Graph Representation Learning (ICLR 2024)

The paper uses the power of an LLM to get a meaningful explanation, which is then used to fine-tune a smaller LM+GNN. Really interesting paper. Would be great to have an example of it in PyG :)

贡献者指南

技术栈
python
领域
machine learning
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
5
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
over 1 week
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
active
清晰度议题是否清楚说明期望改动、验收标准和下一步。
clear
前置要求
PyTorch Geometric basicsGraph Neural NetworksUnderstanding of LLMsPaper reading comprehension
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
20
研究方向
Study the paper 'Harnessing Explanations: LLM to LM Interpreter for Enhanced Text Attributed Graph Representation Learning' from ICLR 2024. The implementation requires combining an LLM (e.g., from Hugging Face transformers) with a GNN model in PyTorch Geometric. Follow the architecture described in Section 3. Review existing PyG examples for reference, such as 'examples/ogbn arxiv.py' for node classification. Implement a new module 'LLMtoLMGNN' in torch geometric.nn. The feature is not yet assigned, so contributions are welcome. Ensure to include proper documentation and tests.