The article discusses the critical need for a formal mathematical framework in integrating Large Language Models (LLMs) into Multi-Agent Systems (MAS). To address inefficiencies resulting from ad hoc use of LLMs, the L Function is introduced as a method to quantify and optimize their performance in dynamic contexts like finance and healthcare. By balancing output brevity and contextual alignment through mathematical rigor, the L Function aims to enhance the effectiveness of LLMs across various demanding environments, articulating specific dimensions of optimization related to task and historical context.
The integration of LLMs into multi-agent systems requires a formal model like the L Function to manage context, task relevance, and resource constraints effectively.
Traditional heuristics are inadequate for real-time environments such as healthcare and finance, creating a pressing need for a rigorous mathematical framework for optimizing LLM operations.
The L Function serves as a unifying mathematical construct, allowing for quantification and minimization of inefficiencies in LLM outputs, ensuring contextual alignment.
By decomposing contextual deviation using parameters like task-specific and historical deviation, the L Function helps balance brevity and relevance in outputs generated by LLMs.
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