Towards Developing a Multimodal Chat Assistant for University Stakeholders: RAG-based Approach
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Computer Science > Computation and Language
Title:Towards Developing a Multimodal Chat Assistant for University Stakeholders: RAG-based Approach
Abstract: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 this http URL, 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.
| Comments: | Accepted at 2025 28th International Conference on Computer and Information Technology (ICCIT) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.01115 [cs.CL] |
| (or arXiv:2607.01115v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01115
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | 2025 28th International Conference on Computer and Information Technology (ICCIT) |
| Related DOI: | https://doi.org/10.1109/ICCIT68739.2025.11490128
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