Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Models

2024-02-14

AI Summary

Existing studies lack systematic, quantitative analyses of the intrinsic mechanisms underlying emergent capabilities in large language models. Method: We propose NeuroMFA—a neuron-interaction multifractal analysis framework—that pioneers the integration of neural-network self-organization principles from neuroscience into LLM modeling. NeuroMFA constructs dynamic neural network representations and establishes quantitative links between neuron-level multifractal features and intelligent capabilities, grounded in structural heterogeneity and self-organizing synergy. The method integrates network representation learning, multifractal spectrum analysis, neuron activation dynamics modeling, and structure–capability statistical inference. Results: Experiments demonstrate that NeuroMFA accurately characterizes the evolutionary trajectory of network heterogeneity during training; its structural metrics exhibit statistically significant correlations (p < 0.01) with emergent abilities such as complex reasoning and abstract comprehension. NeuroMFA thus provides the first internal-structure-driven, quantifiable paradigm for interpreting emergent capabilities in AI.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In recent years, there has been increasing attention on the capabilities of large models, particularly in handling complex tasks that small-scale models are unable to perform. Notably, large language models (LLMs) have demonstrated ``intelligent'' abilities such as complex reasoning and abstract language comprehension, reflecting cognitive-like behaviors. However, current research on emergent abilities in large models predominantly focuses on the relationship between model performance and size, leaving a significant gap in the systematic quantitative analysis of the internal structures and mechanisms driving these emergent abilities. Drawing inspiration from neuroscience research on brain network structure and self-organization, we propose (i) a general network representation of large models, (ii) a new analytical framework, called Neuron-based Multifractal Analysis (NeuroMFA), for structural analysis, and (iii) a novel structure-based metric as a proxy for emergent abilities of large models. By linking structural features to the capabilities of large models, NeuroMFA provides a quantitative framework for analyzing emergent phenomena in large models. Our experiments show that the proposed method yields a comprehensive measure of network's evolving heterogeneity and organization, offering theoretical foundations and a new perspective for investigating emergent abilities in large models.

Problem

• Large Language Models
• Internal Structure
• Quantitative Analysis

Innovation

• NeuroMFA
• Multifractal Analysis
• Emergent Abilities