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

• URL: http://arxiv.org/abs/2402.14293v1
• Date: Thu, 22 Feb 2024 05:15:27 GMT
• Title: Leveraging Large Language Models for Concept Graph Recovery and Question Answering in NLP Education
• Authors: Rui Yang, Boming Yang, Sixun Ouyang, Tianwei She, Aosong Feng, Yuang Jiang, Freddy Lecue, Jinghui Lu, Irene Li
• Abstract summary: Large Language Models (LLMs) have demonstrated promise in text-generation tasks. This study focuses on concept graph recovery and question-answering (QA) In TutorQA tasks, LLMs achieve up to 26% F1 score enhancement.
• Score: 14.908333207564574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the domain of Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated promise in text-generation tasks. However, their educational applications, particularly for domain-specific queries, remain underexplored. This study investigates LLMs' capabilities in educational scenarios, focusing on concept graph recovery and question-answering (QA). We assess LLMs' zero-shot performance in creating domain-specific concept graphs and introduce TutorQA, a new expert-verified NLP-focused benchmark for scientific graph reasoning and QA. TutorQA consists of five tasks with 500 QA pairs. To tackle TutorQA queries, we present CGLLM, a pipeline integrating concept graphs with LLMs for answering diverse questions. Our results indicate that LLMs' zero-shot concept graph recovery is competitive with supervised methods, showing an average 3% F1 score improvement. In TutorQA tasks, LLMs achieve up to 26% F1 score enhancement. Moreover, human evaluation and analysis show that CGLLM generates answers with more fine-grained concepts.

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