TL;DR
This paper introduces NeuroMFA, a novel multifractal analysis framework inspired by neuroscience, to quantitatively analyze the internal structures and emergent abilities of large models, revealing insights into their complex behaviors.
Contribution
It presents a new network representation, a multifractal analysis method, and a structure-based metric to link internal structures with emergent capabilities in large models.
Findings
NeuroMFA effectively measures network heterogeneity and organization.
Structural features correlate with emergent abilities.
Provides a new perspective for analyzing large model behaviors.
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…
Peer Reviews
Decision·ICLR 2025 Poster
1. Writing is clear, explains the new metric clearly. 2. Moving the discussion away from only scaling model size is important 3. The metric is well defined, making it replicable.
1. Text in Fig 1 is very small making it hard to read and there is a lot of white space that can be used to increase text size. 2. "While our NeuroMFA framework demonstrates correlations between the emergence metric and downstream performance by studying the self-organization of LLMs, it has not yet established a clear causal relationship. Future work should focus on identifying specific linguistic phenomena captured by LLMs and demonstrating how our analysis methods can reveal the learning proc
I genuinely enjoyed reading this paper. It introduces a new measure to study the training of LLMs that I have not seen in this way before. The paper generally is very thorough in its analyses / experimentation. I am sure this will be work that is of interest to the ICLR community.
I think this paper should definitely appear in the proceedings. I see some weaknesses in the way that methods are introduced, and results are described, which I list in the questions below. I hope my suggestions will be helpful to create more impactful paper.
The paper is extremely well contextualized and the presentation of the ideas and results are solids. The quality of the writing makes it really pleasant to read. It gives a really interesting direction of research by trying to bridge a gap with neuroscience and trying to create a new framework to link emerging properties and network topologies.
Some mathematics would have deserved better explanation in the article itself and not the appendix. You are measuring the degree of organisation in the networks and show that : 1. it increases during training 2. it correlates with previous definitions of emergence Your are yourself comparing it with the averaged weighted degree distribution of the network in the appendix which seem to correlates as well with emergence. I feel like your "degree of emergence" is itself one dimensional as wel
Code & Models
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Taxonomy
TopicsMachine Learning in Bioinformatics
