Leveraging Large Language Models for Addressing Operational Challenges in Sri Lankan SMEs

Abstract

Small and Medium Enterprises (SMEs) are critical to Sri Lanka's economy but face challenges like regulatory complexity, limited technology access, and linguistic barriers. This research explores the use of Large Language Models (LLMs) to address these issues by localizing AI for Sinhala and Tamil. The study fine-tunes the Aya Model, a multilingual instruction-tuned LLM, using Sri Lankan business and regulatory datasets. Techniques such as data augmentation and cross-lingual transfer learning were applied to mitigate the scarcity of structured Sinhala data, ensuring cultural and linguistic alignment. Evaluation shows a 30% improvement in regulatory compliance accuracy, faster response times for customer queries, and enhanced operational efficiency. The localized LLM automates multilingual customer interactions and generates context-sensitive business insights, offering a scalable, cost-effective solution for SMEs. This study demonstrates how LLMs can bridge technological gaps, enabling Sri Lankan SMEs to achieve sustainable growth and competitiveness in a resource-constrained setting.