Unveiling LLM Mechanisms Through Neural ODEs and Control Theory

TL;DR

This paper introduces a novel framework combining Neural ODEs and control theory to improve the interpretability and controllability of large language models, demonstrating enhanced output consistency and explainability.

Contribution

It presents a new approach that integrates Neural ODEs with control mechanisms to better understand and manage LLM behavior, which is a novel contribution.

Findings

Improved output consistency in LLMs

Enhanced interpretability of model mechanisms

Effective control of language model outputs

Abstract

This paper proposes a framework combining Neural Ordinary Differential Equations (Neural ODEs) and robust control theory to enhance the interpretability and control of large language models (LLMs). By utilizing Neural ODEs to model the dynamic evolution of input-output relationships and introducing control mechanisms to optimize output quality, we demonstrate the effectiveness of this approach across multiple question-answer datasets. Experimental results show that the integration of Neural ODEs and control theory significantly improves output consistency and model interpretability, advancing the development of explainable AI technologies.