Question Generation and Document-Grounded Question Answering from PDF Content

Large language models have demonstrated strong capabilities in natural language generation; however, directly applying their pretrained knowledge to document-specific educational tasks may result in responses that are insufficiently grounded in the target material. This paper presents a local retrieval-augmented generation (RAG) framework for automatic question generation and document-grounded question answering from PDF content. The proposed system integrates document text extraction and optical character recognition, overlapping text segmentation, semantic embedding, FAISS-based vector retrieval, and locally deployed large language models through Ollama. Dedicated prompting strategies are developed to generate four educational question formats: essay, short-answer, multiple-choice, and fill-in-the-blank questions. A refinement stage is further employed to improve the coverage and structure of the generated questions, while retrieval-based answer generation grounds responses in the most relevant portions of the source document. For interactive use, the framework is integrated into a Streamlit-based interface that enables PDF upload and conversational question answering. The implemented system demonstrates the feasibility of combining local language models, semantic retrieval, and task-specific prompting within a unified document-centered educational framework while reducing dependence on external cloud-based language-model services.

کلیدواژه ها:

Retrieval-Augmented Generation (RAG) ، Large Language Models ، Automatic Question Generation ، Question Answering ، Semantic Retrieval ، FAISS ، Local LLMs ، Educational NLP