insight - Algorithms and Data Structures - # Emergence and scaling laws in multilinear models for multitask sparse parity

Core Concepts

A simple multilinear model with orthogonal skill basis functions can analytically capture the emergence of new skills and the scaling laws of loss with training time, data size, model size, and optimal compute in the multitask sparse parity problem.

Abstract

The paper presents a framework for investigating emergence by representing skills as orthogonal functions that form a basis in function space. The authors apply this approach to the multitask sparse parity dataset, where each skill corresponds to a unique sparse parity task.
The key insights are:
Skills as basis functions: The authors define a basis of orthogonal functions (gk) that represent the different skills in the multitask sparse parity problem. Each skill function gk returns the parity of the relevant sparse bits if the control bits match the kth skill, and 0 otherwise.
Multilinear model: The authors propose a multilinear model that is expanded in the basis of skill functions gk. The multilinear structure gives rise to a layered model architecture and stage-like training dynamics, where one skill is completely learned before the next skill initiates learning.
Scaling laws: The authors derive scaling laws for the model's performance with respect to training time (T), dataset size (D), number of parameters (N), and optimal compute (C). The scaling exponents are shown to be -α/(α+1), -α/(α+1), -α, and -α/(α+2) respectively, where α+1 is the exponent of the power-law (Zipfian) input data distribution.
Predicting emergence: The authors demonstrate that their multilinear model, calibrated only on the first skill, can accurately predict the emergence of subsequent skills in a 2-layer neural network trained on the multitask sparse parity problem. This suggests that the layered structure shared by neural networks and the multilinear model can capture the dynamics of skill emergence.
Overall, the paper provides a principled framework for understanding emergence and scaling laws in deep learning models, using the multitask sparse parity problem as a testbed.

Stats

The number of training samples for the kth skill (dk) follows a power-law distribution with exponent α+1.
The target function f* is a sum of the skill basis functions gk, each multiplied by a target scale S.

Quotes

"Skills as basis functions. We establish a framework for investigating emergence by representing skills as orthogonal functions that form a basis in function space (Section 2)."
"Multilinear model. We propose an analytically tractable model that is expanded in the basis of skill functions, and is multilinear with respect to its parameters so that it possesses a layerwise structure (Section 3)."
"Scaling laws. We employed both intuitive (Section 4, Section 5, and Appendix D) and rigorous (Appendix J) derivations of scaling laws for our multilinear model, relating the model's performance to training time (T), dataset size (D), number of parameters (N), and optimal compute (C = N × T)."
"Predicting emergence. We demonstrate that our multilinear model, calibrated only on the first skill, can predict the emergence of subsequent skills in a 2-layer NN for varying training time, dataset size, and number of trainable parameters."

Key Insights Distilled From

by Yoonsoo Nam,... at **arxiv.org** 04-29-2024

Deeper Inquiries

In order to extend the multilinear model to capture more complex skill structures beyond the orthogonal basis functions, we can introduce additional layers or components to the model. One approach could be to incorporate non-linear transformations or interactions between the basis functions to allow for more intricate relationships between skills. This could involve introducing higher-order interactions, such as products or convolutions of the basis functions, to capture more complex patterns in the data. Additionally, incorporating attention mechanisms or recurrent connections could enable the model to capture dependencies and interactions between skills over time or across different parts of the input data. By expanding the model architecture in this way, we can enhance its capacity to represent and learn complex skill structures in the data.

The stage-like training dynamics observed in the multilinear model have several implications for the learning process and performance of the model. Firstly, the stage-like training indicates that the model learns skills in a sequential manner, with each skill being fully learned before the next skill is initiated. This can lead to more stable and interpretable learning outcomes, as the model focuses on mastering one skill at a time. Additionally, the stage-like training dynamics suggest that there may be distinct phases or stages in the learning process, each corresponding to the acquisition of a new skill or capability.
These stage-like training dynamics in the multilinear model could relate to the training dynamics of larger neural networks, especially in the context of deep learning models. In larger neural networks, the training process may also involve the sequential acquisition of skills or features, with the network gradually learning to represent more complex patterns in the data. The stage-like training dynamics could help prevent catastrophic forgetting and promote more structured learning, leading to better generalization and performance on complex tasks. Understanding and leveraging these stage-like dynamics in larger neural networks could potentially improve training efficiency, model interpretability, and overall learning outcomes.

The insights from this work on emergence and scaling laws can indeed be applied to understand the behavior of large language models and other complex deep learning systems. By studying the emergence of new skills or capabilities in deep learning models as they scale up in terms of training time, data size, and model complexity, we can gain valuable insights into how these models learn and adapt to new tasks. Understanding the scaling laws that govern the performance of deep learning models can help us predict how their capabilities will evolve as they are trained on larger datasets or with more computational resources.
In the context of large language models, such as transformer-based models, the insights from this work can provide a framework for analyzing the sudden emergence of new language abilities or understanding the scaling behavior of these models with respect to training time, data size, and model parameters. By applying the principles of emergence and scaling laws, researchers can better interpret the behavior of large language models, optimize their training processes, and improve their performance on a wide range of natural language processing tasks. Additionally, these insights can be extended to other complex deep learning systems in various domains, enabling researchers to better understand and optimize the learning dynamics of these models for improved performance and efficiency.

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