College
College of Engineering & Technology (Batten)
Department
Engineering Management and Systems Engineering
Graduate Level
Doctoral
Graduate Program/Concentration
Systems Engineering
Presentation Type
No Preference
Abstract
Large Language Models (LLMs) have significantly advanced conversational AI by enabling dialogic information-seeking and task execution across diverse domains. However, their extensive parameters and broad domain scope lead to “data hallucinations.” These shortcomings are particularly evident in dynamic and diverse environments like India’s healthcare sector, where myriad languages, regional practices, and cultural nuances demand specialized, localized expertise rather than one-size-fits-all generalist models. This paper introduces a meta-clustering framework that integrates Distilled Language Models (DLMs) and Small/Specialized Language Models (SLMs) with meta-learning principles to address these limitations. By drawing on evidence from works such as MedHalu and Med-HALT, the framework seeks to minimize hallucinations and improve interpretability. Smaller, specialized models can more effectively concentrate on localized tasks or knowledge, thus reducing computational overhead and maintaining acceptable performance levels.
The core principle is the dynamic grouping of DLMs/SLMs into orchestrated clusters, each tuned to a distinct set of linguistic, cultural, or technical parameters. This concept borrows from ensemble clustering strategies in large-scale person re-identification, where diversity in model architectures and training data can strengthen robustness. For example, in India’s healthcare setting, SLMs trained on regionally specific protocols can collaborate within meta-clusters. Distributed meta-learning strategies (akin to G-Meta) optimize training and inference, ensuring the framework can scale across geographically separated GPU nodes and resource-constrained data centers. This operational efficiency is critical for healthcare, which must manage a continuous influx of patient data, often under limited computational capabilities. The framework’s inherent modularity positions it for rapid adaptation to shifting patient demographics, emergent health crises, and evolving clinical guidelines, thus serving as a versatile backbone for “wicked problems” extending beyond healthcare into sectors like disaster management or multilingual governance.
Keywords
Large Language Models, Small Language Models, Artificial Intelligence, Artificial Agents, Meta-Analysis, Cluster Analysis, Data Hallucinations
Included in
Artificial Intelligence and Robotics Commons, Computer and Systems Architecture Commons, Databases and Information Systems Commons, Dynamic Systems Commons, Systems Architecture Commons, Systems Engineering Commons
Meta-Clustering for Specialized Language Models: Enhancing Contextual Adaptation and Mitigating Hallucinations in Diverse Healthcare Environments
Large Language Models (LLMs) have significantly advanced conversational AI by enabling dialogic information-seeking and task execution across diverse domains. However, their extensive parameters and broad domain scope lead to “data hallucinations.” These shortcomings are particularly evident in dynamic and diverse environments like India’s healthcare sector, where myriad languages, regional practices, and cultural nuances demand specialized, localized expertise rather than one-size-fits-all generalist models. This paper introduces a meta-clustering framework that integrates Distilled Language Models (DLMs) and Small/Specialized Language Models (SLMs) with meta-learning principles to address these limitations. By drawing on evidence from works such as MedHalu and Med-HALT, the framework seeks to minimize hallucinations and improve interpretability. Smaller, specialized models can more effectively concentrate on localized tasks or knowledge, thus reducing computational overhead and maintaining acceptable performance levels.
The core principle is the dynamic grouping of DLMs/SLMs into orchestrated clusters, each tuned to a distinct set of linguistic, cultural, or technical parameters. This concept borrows from ensemble clustering strategies in large-scale person re-identification, where diversity in model architectures and training data can strengthen robustness. For example, in India’s healthcare setting, SLMs trained on regionally specific protocols can collaborate within meta-clusters. Distributed meta-learning strategies (akin to G-Meta) optimize training and inference, ensuring the framework can scale across geographically separated GPU nodes and resource-constrained data centers. This operational efficiency is critical for healthcare, which must manage a continuous influx of patient data, often under limited computational capabilities. The framework’s inherent modularity positions it for rapid adaptation to shifting patient demographics, emergent health crises, and evolving clinical guidelines, thus serving as a versatile backbone for “wicked problems” extending beyond healthcare into sectors like disaster management or multilingual governance.