The Impact of Transformer Depth on Compositional Generalization in Language Models

Konsep Inti

Deeper transformer language models exhibit better compositional generalization than shallower models, even when controlling for total parameter count.

Abstrak

The authors investigate the impact of transformer depth on compositional generalization in language models. They construct three classes of transformer models (41M, 134M, and 374M parameters) where depth is traded off against width to maintain a constant total parameter count.
The key findings are:

Deeper models generally achieve lower perplexity during language modeling pretraining compared to shallower models, but the benefits of additional layers diminish rapidly.

Deeper models also exhibit better performance on compositional generalization tasks such as COGS, COGS-vf, GeoQuery, and English passivization. However, the benefits of depth again saturate quickly, with 4-6 layers often sufficient to achieve near-optimal performance.

The benefits of depth for compositional generalization cannot be fully explained by the deeper models' superior language modeling performance or in-distribution task performance. Depth appears to confer an independent advantage for compositional generalization.

Due to the approximately linear relationship between transformer depth and inference latency, the authors recommend using shallower models with a given parameter budget, as the performance gains from additional depth diminish quickly.

Statistik

The authors report the following key statistics:
"the perplexity of a single-layer model can be nearly twice that of the optimal model in the class."
"For 41M-parameter models the ratio between the perplexity of the single-layer model and that of the optimal (5-layer) model is 1.59; for the 134M-parameter models, the ratio is 1.86; and for the 374M-parameter models, the ratio is 1.99."

Kutipan

"Simply adding layers increases the total number of parameters; to address this confound between depth and size, we construct three classes of models which trade off depth for width such that the total number of parameters is kept constant (41M, 134M and 374M parameters)."
"Because model latency is approximately linear in the number of layers, these results lead us to the recommendation that, with a given total parameter budget, transformers can be made shallower than is typical without sacrificing performance."

Wawasan Utama Disaring Dari

The Impact of Depth on Compositional Generalization in Transformer Language Models

by Jackson Pett... pada arxiv.org 04-12-2024

https://arxiv.org/pdf/2310.19956.pdf

Pertanyaan yang Lebih Dalam

What other architectural factors, beyond depth, might influence a transformer's ability to generalize compositionally

In addition to depth, several other architectural factors can influence a transformer's ability to generalize compositionally. One crucial factor is the attention mechanism within the transformer. The design and implementation of attention heads, including the number of heads, the attention span, and the attention mechanism's focus, can significantly impact the model's compositional generalization capabilities. Furthermore, the choice of activation functions, normalization techniques, positional encodings, and the structure of the feed-forward networks can also play a role in how well a transformer can generalize compositionally. Additionally, the presence of residual connections, layer normalization, and dropout mechanisms can affect the flow of information and the model's ability to capture compositional relationships effectively.

How do the authors' findings on the diminishing returns of depth compare to other approaches for improving compositional generalization, such as data augmentation or specialized model architectures

The authors' findings on the diminishing returns of depth in transformers compared to other approaches for improving compositional generalization shed light on the trade-offs involved in model design. While increasing depth can enhance performance to a certain extent, the study highlights that the benefits of adding layers diminish rapidly as models get deeper. This contrasts with approaches like data augmentation or specialized model architectures, which may offer alternative paths to improving compositional generalization. Data augmentation techniques can help expose the model to a more diverse range of examples, potentially enhancing its ability to generalize compositionally. On the other hand, specialized model architectures tailored to specific tasks or linguistic phenomena may provide targeted solutions for improving compositional generalization without solely relying on depth as a factor. By comparing these different strategies, researchers and practitioners can make informed decisions about the most effective ways to enhance a transformer's compositional generalization capabilities.

Could the authors' insights on the relationship between depth, performance, and latency be extended to other types of neural networks beyond transformers

The insights provided by the authors on the relationship between depth, performance, and latency in transformers can be extended to other types of neural networks beyond transformers. The concept of diminishing returns with increasing depth is a fundamental aspect of neural network design and optimization. This principle applies to various architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning models. By understanding the trade-offs between model depth, performance improvements, and computational costs, researchers can make informed decisions when designing and training neural networks across different domains. The findings on latency and efficiency can guide the development of more computationally efficient models in various applications, ensuring that the trade-offs between model complexity and performance are carefully considered in neural network design.