HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering (2025.findings-emnlp)
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| Challenge: | graph neural networks capture structured graph information, but lack integration at the reasoning level. |
| Approach: | They propose a framework that leverages graph structural information to reason interpretable academic QA results. |
| Outcome: | The proposed framework outperforms sota baselines on OpenAlex and DBLP datasets. |
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