Unlocking Emergent Modularity in Large Language Models to Enhance Downstream Generalization

Khái niệm cốt lõi

Emergent modularity spontaneously arises in pre-trained language models, and unlocking this modularity through fine-tuning as Emergent Mixture-of-Experts (EMoE) can improve downstream in-domain and out-of-domain generalization.

Tóm tắt

The content discusses the concept of emergent modularity in large language models and how it can be leveraged to enhance downstream task performance. Key highlights:

Modular Neural Networks (MNNs) have shown various advantages over monolithic models, but most language models are still treated as monolithic in the pre-train and fine-tune paradigm.

Recent works reveal that there exists implicit modularity in standard pre-trained transformers, referred to as Emergent Modularity (EM). This modular structure spontaneously exhibits during the early pre-training phase.

The authors propose a method called Emergent Mixture-of-Experts (EMoE) to externalize the EM in pre-trained language models without introducing any extra parameters. EMoE is derived by splitting the original Feed-Forward Networks (FFNs) layers into experts based on key vector clustering, and using the average of expert keys as the gating mechanism.

Experiments demonstrate that fine-tuning EMoE effectively improves downstream in-domain and out-of-domain generalization compared to vanilla fine-tuning, across various models and evaluation settings.

The authors provide analyses to show that EMoE indeed unlocks the EM in pre-trained models, and that its improvements stem from ameliorating the parameter updating during fine-tuning rather than directly impacting the inference.

Ablation studies further validate the robustness of EMoE to different hyperparameter configurations and its scalability to large language models like Llama2-7B and Llama-30B.

Thống kê

Only 3.0% and 6.3% neurons are activated during one forward process in T5-Base and ViT-B16, respectively.
EMoE achieves up to 0.84 and 1.58 improvements in ID and OOD performance, respectively, compared to vanilla fine-tuning.

Trích dẫn

"Recent works reveal that there exists implicit modularity in standard pre-trained transformers, namely Emergent Modularity."
"We find that fine-tuning EMoE achieves stronger generalization performance than vanilla fine-tuning across various experimental settings, demonstrating that unlocking the EM of LMs boosts the models' downstream generalization abilities."

Thông tin chi tiết chính được chắt lọc từ

Unlocking Emergent Modularity in Large Language Models

by Zihan Qiu,Ze... lúc arxiv.org 04-02-2024

https://arxiv.org/pdf/2310.10908.pdf

Yêu cầu sâu hơn

What are the potential implications of emergent modularity in large language models beyond downstream task performance, such as in terms of model interpretability, robustness, or few-shot learning

The implications of emergent modularity in large language models extend beyond just improving downstream task performance. One significant implication is enhanced model interpretability. By identifying and leveraging the modular structures that spontaneously emerge during training, researchers and practitioners can gain insights into how different parts of the model contribute to specific tasks. This can help in understanding the inner workings of the model and potentially uncovering hidden patterns or biases that may not be apparent in monolithic models.
Another implication is increased robustness. Modular neural networks have shown to be more adaptable and resilient to changes or perturbations in the input data. By unlocking and utilizing emergent modularity in language models, it may be possible to enhance the model's ability to generalize to new, unseen data and improve its performance in challenging scenarios.
Furthermore, emergent modularity can also benefit few-shot learning tasks. By identifying and isolating specific modules or experts within the model that are relevant to a particular task, fine-tuning or adapting the model for new tasks with limited data becomes more efficient. This targeted approach can lead to faster adaptation and improved performance in few-shot learning scenarios.

How can the insights from this work be extended to other types of neural architectures beyond transformers, such as convolutional or recurrent models

The insights from this work on emergent modularity in large language models can be extended to other types of neural architectures beyond transformers, such as convolutional or recurrent models. While the specific mechanisms of emergent modularity may vary across different architectures, the fundamental concept of identifying and leveraging modular structures within the model remains applicable.
For convolutional models, researchers can explore how emergent modularity manifests in the convolutional layers and how different parts of the network specialize in detecting specific features or patterns. By understanding and utilizing these modular components, convolutional models can potentially achieve better performance and efficiency in tasks such as image recognition or object detection.
Similarly, in recurrent models, the concept of emergent modularity can be applied to identify specialized modules within the recurrent layers that excel at capturing different temporal dependencies or sequences. By leveraging these modular structures, recurrent models can potentially improve their performance in tasks requiring sequential data processing, such as natural language processing or time series analysis.

Could the emergent modularity in language models be further leveraged to enable more efficient or targeted fine-tuning, for example by selectively updating only the relevant expert modules for a given downstream task

The emergent modularity in language models can be leveraged to enable more efficient and targeted fine-tuning by selectively updating only the relevant expert modules for a given downstream task. This targeted fine-tuning approach can help in reducing the computational cost and training time required for adapting the model to new tasks.
One way to achieve this is by dynamically adjusting the gating mechanism to prioritize the activation of specific experts based on the task at hand. By focusing the model's resources on the relevant modules, unnecessary updates to unrelated parts of the model can be avoided, leading to more efficient fine-tuning and improved performance on the target task.
Additionally, techniques such as sparse activation pruning or expert selection based on task-specific criteria can further enhance the efficiency of fine-tuning by ensuring that only the most relevant modules are updated during the adaptation process. This selective updating based on emergent modularity can lead to faster convergence, better generalization, and improved performance on downstream tasks.