Comparison Of Large Language Models For Generating Contextually Relevant Questions

Molina Ivo Lodovico, Švábenský Valdemar, Minematsu Tsubasa, Chen Li, Okubo Fumiya, Shimada Atsushi. Arxiv 2024

[Paper]    
Applications Fine Tuning GPT Model Architecture Pretraining Methods Reinforcement Learning Survey Paper Training Techniques

This study explores the effectiveness of Large Language Models (LLMs) for Automatic Question Generation in educational settings. Three LLMs are compared in their ability to create questions from university slide text without fine-tuning. Questions were obtained in a two-step pipeline: first, answer phrases were extracted from slides using Llama 2-Chat 13B; then, the three models generated questions for each answer. To analyze whether the questions would be suitable in educational applications for students, a survey was conducted with 46 students who evaluated a total of 246 questions across five metrics: clarity, relevance, difficulty, slide relation, and question-answer alignment. Results indicate that GPT-3.5 and Llama 2-Chat 13B outperform Flan T5 XXL by a small margin, particularly in terms of clarity and question-answer alignment. GPT-3.5 especially excels at tailoring questions to match the input answers. The contribution of this research is the analysis of the capacity of LLMs for Automatic Question Generation in education.

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