ChaTA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMs

Abstract: To address the challenges of scalable and intelligent question-answering (QA), we introduce an innovative solution that leverages open-source Large Language Models (LLMs) to ensure data privacy. We use models from the LLaMA-2 family and augmentations including retrieval augmented generation (RAG), supervised fine-tuning (SFT), and an alternative to reinforcement learning with human feedback (RLHF). We perform our experiments on a Piazza dataset from an introductory CS course with 10k QA pairs and 1.5k pairs of preferences data and conduct both human evaluations and automatic LLM evaluations on a small subset. We find preliminary evidence that modeling techniques collectively enhance the quality of answers by 33%, and RAG is an impactful addition. This work paves the way for the development of ChaTA, an intelligent QA assistant customizable for courses with an online QA platform.