# From Questions to Insightful Answers: Building an Informed Chatbot for University Resources

arXiv: https://arxiv.org/abs/2405.08120 · HTML: https://mlsanity.com/paper/2405.08120/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation.
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 13 May 2024.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Ragas: Automated Evaluation of Retrieval Augmented Generation](https://mlsanity.com/paper/2309.15217/) (2023)
- [Information Leakage in Embedding Models](https://mlsanity.com/paper/2004.00053/) (2020)
- [A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge](https://mlsanity.com/paper/2310.11703/) (2023)
- [LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge](https://mlsanity.com/paper/2401.10036/) (2024)
- [MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses using Large Language Models](https://mlsanity.com/paper/2403.08607/) (2024)

## Built on by (most cited first, 12 of 12)

- [Who Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented Generation](https://mlsanity.com/paper/2509.13772/) (2025)
- [RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice](https://mlsanity.com/paper/2502.14930/) (2025)
- [URAG: Implementing a Unified Hybrid RAG for Precise Answers in University Admission Chatbots -- A Case Study at HCMUT](https://mlsanity.com/paper/2501.16276/) (2025)
- [NOVI : Chatbot System for University Novice with BERT and LLMs](https://mlsanity.com/paper/2409.06192/) (2024)
- [AI Learning Algorithms: Deep Learning, Hybrid Models, and Large-Scale Model Integration](https://mlsanity.com/paper/2410.09186/) (2024)
- [Poison Attacks and Adversarial Prompts Against an Informed University Virtual Assistant](https://mlsanity.com/paper/2412.06788/) (2024)
- [Talking to Data: Designing Smart Assistants for Humanities Databases](https://mlsanity.com/paper/2506.00986/) (2025)
- [Fine-Tuning BERT for Domain-Specific Question Answering: Toward Educational NLP Resources at University Scale](https://mlsanity.com/paper/2512.05179/) (2025)
- [ACE-TA: An Agentic Teaching Assistant for Grounded Q&A, Quiz Generation, and Code Tutoring](https://mlsanity.com/paper/2604.09572/) (2026)
- [Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligence](https://mlsanity.com/paper/2605.14266/) (2026)
- [Towards Developing a Multimodal Chat Assistant for University Stakeholders: RAG-based Approach](https://mlsanity.com/paper/2607.01115/) (2026)
- [A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2609.07075/) (2026)

## Abstract (arXiv)

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.