Generative Chatbot Adaptation for Odia Language: A Critical Evaluation

Large Language Models (LLMs) have gained significant attention in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI) due to their ability to generate human-like text and facilitate conversational interactions. However, the majority of LLMs are majorly developed for English, limiting their accessibility and effectiveness for non-English speaking populations. In India, where only 10% of the population is proficient in English, the need for LLM models adapted to regional languages becomes crucial. This research paper focuses on the adaptability of LLMs to the Odia language, spoken by approximately 50 million people in India. With a primary objective to cater to the Odia-speaking community, we aim to evaluate existing LLM models such as ChatGPT, and Olive, an instruction following Odia LLM, specifically in the context of generating conversational outputs in Odia. We employ a critical evaluation approach to assess the performance, language understanding, and response generation capabilities of the LLM models for the Odia language. By conducting experiments and comparative analysis, we seek to determine the strengths, weaknesses, and potential areas of improvement for the existing LLM models. Our findings will contribute to the development of more effective and contextually accurate generative chatbots for the Odia language, enabling better communication and accessibility for the Odia-speaking population.