Domain Specific Large Language Models: Source Coding

This research paper presents a novel idea and framework for deploying Large Language Models (LLMs) on resource constrained environments through domain specific optimization techniques. The main idea around this paper is to leverage domain specific knowledge structures and applying information-theoretic principles from source coding theory for creating LLM-based generative systems that can be practical for deployment on devices like smartphones, drones etc. We propose a hierarchical menu-driven prompt interface paradigm that optimally allocates computational and storage resources based on the probabilistic distribution of domain specific concepts. The application of this approach is demonstrated through concrete examples in medical diagnosis, agricultural advisory systems, and maritime inspection domains. We identify critical challenges in implementing such systems on resource-constrained platforms, including model compression, latency minimization and energy efficiency. Furthermore, we discuss the design principles for creating domain-specific prompt interfaces that leverage source coding concepts to minimize average token requirements while maintaining semantic fidelity.