# Towards Developing a Multimodal Chat Assistant for University Stakeholders: RAG-based Approach

arXiv: https://arxiv.org/abs/2607.01115 · HTML: https://mlsanity.com/paper/2607.01115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 4 of 4)

- [A Survey on Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2402.00253/) (2024)
- [From Questions to Insightful Answers: Building an Informed Chatbot for University Resources](https://mlsanity.com/paper/2405.08120/) (2024)
- [A Structured Dataset of Disease-Symptom Associations to Improve Diagnostic Accuracy](https://mlsanity.com/paper/2506.13610/) (2025)
- [Bridging the Gap in Bangla Healthcare: Machine Learning Based Disease Prediction Using a Symptoms-Disease Dataset](https://mlsanity.com/paper/2601.12068/) (2026)

## Abstract (arXiv)

University stakeholders often face difficulties in accessing timely and reliable information, especially in developing countries, where there are very few intelligent support systems. Existing rule-based chatbots are unable to handle complex, domain-specific queries and are not well-equipped to adapt to evolving institutional policies. As a fill-in-the-gap solution, we present the multimodal university chatbot with retrieval-augmented generation. The system combines the large language model with semantic retrieval to produce context-based responses from institution-centric resources, such as the university handbook. The system accepts text and image queries through the vision-language model and applies quantized inference for rapid deployment on constrained hardware. A scalable backend built with FastAPI, adjoined with a responsive frontend developed with Next.js, ensures real-time usability. Our multimodal evaluation demonstrates that the system maintains strong satisfaction scores across both text and image queries, despite increased response time for visual inputs. Furthermore, quantitative evaluation shows that hallucination is reduced from 31.7% to 6.6% in our proposed RAG-based system, confirming the effectiveness of retrieval grounding.