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Leveraging Large Language Models for Concept Graph Recovery and Question Answering in NLP Education

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Graphusion: Leveraging Large Language Models for Scientific Knowledge Graph Fusion and Construction in NLP Education

This is the GitHub repo for the paper submission.

Graphusion

TutorQA: TutorQA dataset, containing 6 tasks.

fusion_results: The results in txt format from Graphusion framework.

Graphusion: Python script for Graphusion implementation.

Link Prediction: Python script for Graphusion - Link Prediction module implementation:

  • src: Zero-shot and Ablation study code. Run with >> python xxxx.py. We include LLaMa, GPT code.

  • simple_baselines_src: Simple classifier baseline source code.

  • supervised_baselines_src: GCN-based models for supervised methods. Check the readme for more information.

  • embedding_src: code and embedding for applying LLaMa and GPT models.

  • RAG_src: Source code for RAG settings.

concept_data: text and labeled data used in the experiments.

data_generator: helpful code for making and cleaning TutorQA benchmark.

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