Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning
Keywords: Neural ODEs, Transformers, Adaptive finetune
TL;DR: This paper introduces a new transformer architecture using non-autonomous neural ODEs that allows for adaptive fine-tuning while analyzing internal model dynamics through spectral analysis.
Abstract: Recent advancements in large language models (LLMs) based on transformer architectures have sparked significant interest in understanding their inner workings. In this paper, we introduce a novel approach to modeling transformer architectures using highly flexible non-autonomous neural ordinary differential equations (ODEs). Our proposed model parameterizes all weights of attention and feed-forward blocks through neural networks, expressing these weights as functions of a continuous layer index. Through spectral analysis of the model's dynamics, we uncover an increase in eigenvalue magnitude that challenges the weight-sharing assumption prevalent in existing theoretical studies. We also leverage the Lyapunov exponent to examine token-level sensitivity, enhancing model interpretability. Our neural ODE transformer demonstrates performance comparable to or better than vanilla transformers across various configurations and datasets, while offering flexible fine-tuning capabilities that can adapt to different architectural constraints.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 3683
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