A Meta-Clustering Framework for Enhancing Conversational AI in the Healthcare Sector

Abstract

Large Language Models (LLMs) have driven substantial progress in conversational AI, yet their extensive parameterization and broad domain training often lead to “data hallucinations” and limited contextual accuracy. These become challenging especially in geographies like India's healthcare sector, where the interaction of numerous languages, distinctive regional practices, and different culture, localized expertise dominant. This paper presents a metaclustering framework that combines Distilled Language Models (DLMs) and Small/Specialized Language Models (SLMs) with meta-learning techniques and introduces a novel Multi-State Model (MSM) approach. The MSM component formalizes the evolution of individual models from a standalone state to integration within a dynamically configured cluster. Through state transition equations, the framework captures, stores, processes, and retrieves localized information with enhanced trustworthiness. Each model's state, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s(t)$</tex>, is updated via a meta-learning function incorporating local input signals and global supervisory prompts. The evolution of the holistic system is modeled by state transition matrix, enabling better resource allocation and reducing model-induced hallucinations. Distributed meta-learning strategies, like G-Meta, continuously adjust parameters across geographically distributed GPU nodes. Integrating localized specialization with global consistency, the proposed approach bargains scalable solution for healthcare applications. This integrated approach, supported by MedHalu and Med-HALT, demonstrates improvements in interpretability and operational efficiency.

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