Scaling Language Model Performance: The Primacy of Compute over Model Size and Dataset Size

Khái niệm cốt lõi

Model performance depends mostly on the amount of compute spent for training, independent of the specific allocation to model size and dataset size.

Tóm tắt

The paper proposes a new scaling law that suggests model performance depends primarily on the amount of compute spent for training, rather than the specific allocation between model size and dataset size.

The key insights are:

1. The authors find that the log scale of compute (model parameters in billions × training tokens in trillions) correlates linearly with the compression scores (bits per character) of various strong open-source language models, spanning more than three orders of magnitude.

2. This linear relationship challenges the current paradigm of scaling laws, which suggest that for compute-optimal training, the model size and the number of training tokens should be scaled equally.

3. The authors argue that for inference efficiency, training should prioritize smaller model sizes and larger training datasets.

4. Assuming the exhaustion of available web datasets, the authors suggest that scaling the model size might be the only way to further improve model performance.

5. The authors acknowledge limitations, such as the importance of data quality and the unclear applicable range of the proposed scaling law. They also note that compression scores may not fully capture all aspects of model capabilities, and future research is needed to explore the relationship between compute and other evaluation metrics.

Thống kê

The amount of training data for Mistral-7B is about 4T tokens.

Trích dẫn

"Our findings suggest that:

For inference efficiency, training should prioritize smaller model sizes and larger training datasets.
Assuming the exhaustion of available web datasets, scaling the model size might be the only way to further improve model performance."

Thông tin chi tiết chính được chắt lọc từ

More Compute Is What You Need

by Zhen Guo lúc arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19484.pdf

Yêu cầu sâu hơn

How might the proposed scaling law be affected by the quality and composition of the training dataset

The proposed scaling law's effectiveness can be influenced by the quality and composition of the training dataset. Higher-quality data may require fewer tokens per parameter for optimal performance, as indicated by previous research [9]. When dealing with a combination of high-quality synthetic and real-world data, the optimal token-per-parameter ratio can shift, impacting the allocation of compute resources. In such cases, the relationship between model size, dataset size, and compute expenditure may deviate from the linear scaling law proposed in the study. The composition of the dataset, including the diversity, relevance, and representativeness of the data, plays a crucial role in determining the efficiency and effectiveness of the training process. Therefore, variations in data quality and composition can introduce complexities that may challenge the straightforward application of the proposed scaling law.

What are the potential limitations or edge cases where the log-linear relationship between compute and model performance may not hold true

There are potential limitations and edge cases where the log-linear relationship between compute and model performance may not hold true. Extreme ratios between model size and training tokens, such as training a model with significantly fewer parameters on an excessively large number of tokens or vice versa, may not align with the proposed scaling law. In such cases, the linear correlation between compute expenditure and model performance may break down, leading to suboptimal results. Additionally, the applicability of the log-linear relationship could be constrained by the specific range of ratios between model size and dataset size. Beyond certain thresholds, the scaling law may not accurately predict the performance outcomes, highlighting the need for further research to identify the boundaries of its validity. These edge cases underscore the importance of considering the nuances and complexities of different training scenarios to ensure the scalability and effectiveness of language models.

How can the insights from this work be applied to develop more efficient and capable language models for real-world applications beyond just compression performance

The insights derived from this study offer valuable implications for developing more efficient and capable language models for real-world applications beyond compression performance. By prioritizing smaller model sizes and larger training datasets for inference efficiency, researchers and practitioners can optimize the utilization of compute resources while enhancing model performance. Moreover, considering the potential exhaustion of available web datasets, scaling the model size could emerge as a critical strategy to further improve model capabilities. These findings suggest a shift in focus towards balancing model size, dataset size, and compute expenditure to achieve optimal performance in practical applications. By integrating these insights into the design and training of language models, developers can enhance the efficiency, effectiveness, and scalability of AI systems for diverse real-world tasks and challenges.