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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 :)
The text was updated successfully, but these errors were encountered:
🚀 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 :)
The text was updated successfully, but these errors were encountered: