从问题到有洞见的答案：构建一款面向大学资源的知情聊天机器人
From Questions to Insightful Answers: Building an Informed Chatbot for University Resources
AI总结：
本研究提出基于大语言模型和RAG流水线的大学资源聊天机器人BARKPLUG V.2，以密西西比州立大学为案例，经RAGAS量化评估得分0.96，SUS可用性调查验证其体验良好。
摘要：
本文提出BARKPLUG V.2，这是一款基于大语言模型（LLM）的聊天机器人系统，采用检索增强生成（RAG）流水线构建，旨在提升学术环境下的用户体验与信息获取效率。BARKPLUG V.2的目标是以交互方式，为用户提供大学校园内各类资源的相关信息，包括学术院系、项目、校园设施以及学生资源。本系统将大学数据作为外部语料库，将其接入RAG流水线，用于完成特定领域的问答任务。我们以密西西比州立大学为案例研究，采用检索增强生成评估框架（Retrieval Augmented Generation Assessment, RAGAS）等量化指标，评估系统生成准确且相关回复的效果。此外，我们还通过使用系统可用性量表（System Usability Scale, SUS）的主观满意度调查，评估该系统的可用性。本系统展现出出色的量化性能，RAGAS平均得分为0.96，且可用性评估也验证了其良好的使用体验。
英文摘要：
This paper presents BARKPLUG V.2, a Large Language Model (LLM)-based chatbot system built using Retrieval Augmented Generation (RAG) pipelines to enhance the user experience and access to information within academic settings.The objective of BARKPLUG V.2 is to provide information to users about various campus resources, including academic departments, programs, campus facilities, and student resources at a university setting in an interactive fashion. Our system leverages university data as an external data corpus and ingests it into our RAG pipelines for domain-specific question-answering tasks. We evaluate the effectiveness of our system in generating accurate and pertinent responses for Mississippi State University, as a case study, using quantitative measures, employing frameworks such as Retrieval Augmented Generation Assessment(RAGAS). Furthermore, we evaluate the usability of this system via subjective satisfaction surveys using the System Usability Scale (SUS). Our system demonstrates impressive quantitative performance, with a mean RAGAS score of 0.96, and experience, as validated by usability assessments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
