Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval

TL;DR

This paper presents a novel retrieval-augmented generation approach using LLMs and a new university benchmark to improve academic information retrieval and question answering systems.

Contribution

It introduces a new RAG-based system for university data retrieval and a comprehensive benchmark for evaluating such systems in academic contexts.

Findings

Significant improvements in response accuracy and relevance.

Enhanced user experience with faster, more reliable answers.

Effective use of prompt engineering for contextually relevant responses.

Abstract

This paper introduces an innovative approach using Retrieval-Augmented Generation (RAG) pipelines with Large Language Models (LLMs) to enhance information retrieval and query response systems for university-related question answering. By systematically extracting data from the university official webpage and employing advanced prompt engineering techniques, we generate accurate, contextually relevant responses to user queries. We developed a comprehensive university benchmark, UniversityQuestionBench (UQB), to rigorously evaluate our system performance, based on common key metrics in the filed of RAG pipelines, assessing accuracy and reliability through various metrics and real-world scenarios. Our experimental results demonstrate significant improvements in the precision and relevance of generated responses, enhancing user experience and reducing the time required to obtain relevant answers. In summary, this paper presents a novel application of RAG pipelines and LLMs, supported by a meticulously prepared university benchmark, offering valuable insights into advanced AI techniques for academic data retrieval and setting the stage for future research in this domain.