A Framework for Adaptive Knowledge-Augmented Mizo Large Language Models Using Retrieval-Augmented Generation and Continual Learning
TL;DR
This paper presents an adaptive knowledge-augmented framework for Mizo Large Language Models by combining Retrieval-Augmented Generation (RAG) with continual learning that harnesses semantic retrieval with dense embeddings and FAISS indexing, adaptive evidence re-ranking, parameter-efficient fine-tuning, and incremental knowledge updating to enhance factual accuracy and decrease hallucinations.
Abstract
The deployment of Large Language Models (LLMs) for low-resource languages is challenging due to the lack of linguistic resources, sparse digital content and the absence of structured knowledge bases. In this paper, we present an adaptive knowledge-augmented framework for Mizo Large Language Models by combining Retrieval-Augmented Generation (RAG) with continual learning. This methodology harnesses semantic retrieval with dense embeddings and FAISS indexing, adaptive evidence re-ranking, parameter-efficient fine-tuning, and incremental knowledge updating to enhance factual accuracy and decrease hallucinations. Experimental evaluation shows better retrieval performance, greater text creation quality, and superior human evaluation scores than typical multilingual LLMs and static RAG methods. Moreover, the continual learning technique allows for effective integration of newly accessible Mizo resources, without re-training the model from scratch. The suggested architecture offers a scalable, stable and reusable method for the development of intelligent language technologies for Mizo and other low-resource languages.