Enhancing Admission Inquiry Responses with Fine-Tuned Models and Retrieval-Augmented Generation

TL;DR

This paper presents a hybrid AI system combining fine-tuned language models with Retrieval-Augmented Generation to improve the efficiency and accuracy of university admission inquiry responses.

Contribution

It introduces a novel integration of fine-tuning and RAG tailored for university admissions, enhancing response relevance and correctness in complex, domain-specific contexts.

Findings

Improved response accuracy in admissions inquiries

Enhanced retrieval of relevant information from large datasets

Optimized response generation for better speed and quality

Abstract

University admissions offices face the significant challenge of managing high volumes of inquiries efficiently while maintaining response quality, which critically impacts prospective students' perceptions. This paper addresses the issues of response time and information accuracy by proposing an AI system integrating a fine-tuned language model with Retrieval-Augmented Generation (RAG). While RAG effectively retrieves relevant information from large datasets, its performance in narrow, complex domains like university admissions can be limited without adaptation, potentially leading to contextually inadequate responses due to the intricate rules and specific details involved. To overcome this, we fine-tuned the model on a curated dataset specific to admissions processes, enhancing its ability to interpret RAG-provided data accurately and generate domain-relevant outputs. This hybrid approach leverages RAG's ability to access up-to-date information and fine-tuning's capacity to embed nuanced domain understanding. We further explored optimization strategies for the response generation logic, experimenting with settings to balance response quality and speed, aiming for consistently high-quality outputs that meet the specific requirements of admissions communications.