Need of retrieval augmented generation for conversational assistants for government social welfare scheme popularization

Vaidheeswaran Vaishnav 1, * and Guru Omkar Nathan 2

1 Dalhousie University, Bangalore, Karnataka, India.
2 Gitanjali Olympiad School, Bangalore, Karnataka, India.
 
Research Article
International Journal of Science and Research Archive, 2024, 13(01), 1299–1312.
Article DOI: 10.30574/ijsra.2024.13.1.1747
Publication history: 
Received on 08 August 2024; revised on 16 September 2024; accepted on 19 September 2024
 
Abstract: 
Governments allocate substantial resources to uplift under-privileged communities through various assistance schemes. Despite these efforts, the effectiveness of such initiatives often fails due to variety of reasons including cryptic nature of information available, lack of awareness to the needy people and or lack of transparency. In India, a vast and diverse developing democracy, these challenges are exacerbated by disparities in access to information. To address these issues, we explored the use of conversational AI technologies, including chatbots, which have seen successful application in many developed nations. Conversational AI agents often get challenged to a variety of factual inaccuracies and other types of hallucinations. Our study compares the accuracy of responses across different architectures: Our architectures integrate advancements in natural language processing, such as large language models (LLMs) and retrieval-augmented generation (RAG), along with automatic speech recognition (ASR), knowledge distillation, hybrid retrieval strategies, and text-to-speech systems. Overall this research experiments demonstrate that leveraging retrieval augmented generation using the same model embeddings substantially improves the quality of the output, its similarity to desired responses. This also reduces hallucinations. We also notice that larger models perform better than smaller parameter models.
This paper advocates for continued research in this field, suggesting that further advancements could substantially benefit those the schemes intend to serve.
 
Keywords: 
Government Schemes; Conversational AI; Retrieval Augmented Generation; Text-Speech; Hallucination
 
Full text article in PDF: 
 
Copyright information: 

Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0