Enhancing Multilingual Information Retrieval in Mixed Human Resources Environments: A RAG Model Implementation for Multicultural Enterprise
Syed Rameel Ahmad
TL;DR
This study tackles multilingual information retrieval in a heterogeneous workforce by implementing a Retrieval-Augmented Generation (RAG) architecture tailored for a multicultural enterprise. Key components include a data ingestion strategy with 1000-token chunks and 200-token overlap, a QA-focused prompt to curb hallucinations, a translation and language-detection pipeline (favoring Google Translator), and a speech-enabled interface that routes through English-LMM processing while preserving language context. The implementation evaluates multiple LLMs, with GPT-4 emerging as the preferred model for context retention, coherence, and accuracy, and delivers results via WhatsApp and a mobile HR app. Practically, the system reduces HR query load, sustains high user engagement, and robustly handles multilingual and voice interactions, demonstrating a scalable path for enterprise-wide AI-assisted information retrieval. It also outlines future work in multilingual TTS/STT optimization, cost reduction, and regional dialect expansion to broaden accessibility and efficiency in global organizations.
Abstract
The advent of Large Language Models has revolutionized information retrieval, ushering in a new era of expansive knowledge accessibility. While these models excel in providing open-world knowledge, effectively extracting answers in diverse linguistic environments with varying levels of literacy remains a formidable challenge. Retrieval Augmented Generation (RAG) emerges as a promising solution, bridging the gap between information availability and multilingual comprehension. However, deploying RAG models in real-world scenarios demands careful consideration of various factors. This paper addresses the critical challenges associated with implementing RAG models in multicultural environments. We delve into essential considerations, including data feeding strategies, timely updates, mitigation of hallucinations, prevention of erroneous responses, and optimization of delivery speed. Our work involves the integration of a diverse array of tools, meticulously combined to facilitate the seamless adoption of RAG models across languages and literacy levels within a multicultural organizational context. Through strategic tweaks in our approaches, we achieve not only effectiveness but also efficiency, ensuring the accelerated and accurate delivery of information in a manner that is tailored to the unique requirements of multilingual and multicultural settings.