pyg-team/pytorch_geometric

Implementation of LLM-to-LM GNN

Open

#9,361 opened on May 26, 2024

View on GitHub
 (3 comments) (0 reactions) (1 assignee)Python (3,514 forks)batch import
1 - Priority P1examplefeaturehelp wanted

Repository metrics

Stars
 (19,985 stars)
PR merge metrics
 (Avg merge 35d 1h) (14 merged PRs in 30d)

Description

🚀 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 :)

Contributor guide

Research direction
Implement the LLM to LM interpreter architecture as described in the paper, focusing on integrating an LLM for explanation generation and using it to fine tune a smaller language model combined with a GNN for text attributed graph learning.
Tech stack
pythonpytorch
Domain
machine learning
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
4
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
3-5 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
PyTorchPyG basicsGNN knowledge
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
30