Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning

2025-03-03

AI Summary

Understanding the internal mechanisms of Transformer-based large language models (LLMs) and developing architecture-adaptive tuning paradigms remains challenging due to the discrete, fixed-layer structure and opaque weight-sharing assumptions. Method: We model discrete layer weights as a continuous, non-autonomous neural ordinary differential equation (ODE) parameterized by layer index. We introduce token-level Lyapunov exponents to quantify dynamic sensitivity of attention and feed-forward modules, and integrate continuous parameterization, spectral analysis, and adaptive ODE solvers. Contribution/Results: Our framework reveals— for the first time—that weight spectra diverge with depth, undermining conventional weight-sharing assumptions. Empirically, it matches or surpasses standard Transformers across multiple configurations and datasets. Moreover, it enables hardware-aware elastic structural compression and fine-grained inter-layer adaptation, significantly enhancing deployment flexibility and analytical interpretability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Model transformer architectures using neural ODEs
• Analyze internal dynamics and token-level sensitivity
• Enable adaptive fine-tuning for architectural constraints

Innovation

• Neural ODEs model transformer weights dynamically
• Spectral analysis reveals eigenvalue magnitude increase
• Lyapunov exponent enhances token-level sensitivity analysis