Automatic Question & Answer Generation Using Generative Large Language Model (LLM)
This provides a tool for educators and instructors to streamline text-based evaluations, though it is incremental as it applies existing LLM methods to a specific educational task.
The researchers tackled the challenge of manual question creation for student evaluations by implementing Automatic Question Answer Generation (AQAG) using a fine-tuned generative LLM, resulting in a customized model based on Meta-Llama 2-7B that integrates the RACE dataset for training.
In the realm of education, student evaluation holds equal significance to imparting knowledge. To be evaluated, students usually need to go through text-based academic assessment methods. Instructors need to make a diverse set of questions that need to be fair for all students to prove their adequacy over a particular topic. This can prove to be quite challenging as they may need to manually go through several different lecture materials. Our objective is to make this whole process much easier by implementing Automatic Question Answer Generation(AQAG), using a fine-tuned generative LLM. For tailoring the instructor's preferred question style (MCQ, conceptual, or factual questions), Prompt Engineering (PE) is being utilized. In this research, we propose to leverage unsupervised learning methods in NLP, primarily focusing on the English language. This approach empowers the base Meta-Llama 2-7B model to integrate the RACE dataset as training data for the fine-tuning process. Creating a customized model that will offer efficient solutions for educators, instructors, and individuals engaged in text-based evaluations. A reliable and efficient tool for generating questions and answers can free up valuable time and resources, thus streamlining their evaluation processes.