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.

翻译：为解决可扩展且智能的问答（QA）挑战，我们提出一种创新解决方案，利用开源大语言模型（LLMs）保障数据隐私。我们采用LLaMA-2系列模型，并引入检索增强生成（RAG）、监督微调（SFT）以及基于人类反馈的强化学习（RLHF）的替代方法等增强技术。我们在某入门级计算机科学课程的Piazza数据集上进行实验，该数据集包含1万组问答对及1500组偏好数据，并对一小部分子集开展人工评估与自动大模型评估。初步实验证据表明，建模技术组合可使答案质量提升33%，其中RAG是重要补充。本研究为开发ChaTA——一种可定制于任何采用在线问答平台课程的智能问答助手——奠定了基础。