Scaling Data-Constrained Language Models (2305.16264v4)

Abstract: The current trend of scaling LLMs involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling LLMs in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations.

Scaling Data-Constrained LLMs: An In-Depth Analysis

The paper "Scaling Data-Constrained LLMs" by Niklas Muennighoff and colleagues investigates the intricacies of scaling LLMs under data constraints — specifically where the unique data available for training is limited. This situation diverges from the usual paradigm where larger datasets are assumed to be readily available. The authors propose adaptations to existing scaling laws and perform a series of empirical experiments to validate these modifications.

Key Findings and Contributions

The authors' primary contribution lies in extending the Chinchilla scaling laws, which traditionally assume unlimited data, to account for repeated data in data-constrained scenarios. The proposed scaling laws incorporate a decay factor to model the diminishing returns of repeated data and excess parameters. This modification enables a more accurate prediction of the performance under data constraints.

Significant results include:

1. Empirical Validation of Scaling Laws: Through extensive experimentation involving over 400 models with varying sizes and epochs, the authors show that the value of repeated data diminishes gradually, contradicting the idea that each repetition significantly degrades performance. They empirically validate their scaling formula, demonstrating that models trained with several epochs of repeated data only see significant performance deterioration after around 16 epochs.
2. Optimal Compute Allocation: When data is limited, more compute should be allocated to increasing the number of training epochs rather than model size. The data-constrained scaling laws suggest an optimal balance between parameters and epochs, emphasizing that additional compute is better utilized by training smaller models over more epochs rather than larger models with fewer epochs.
3. Alternative Data Strategies: To mitigate data scarcity, the authors also explore complementary strategies such as augmenting the training dataset with Python code and relaxing data filtering constraints. For instance, training with a mix of natural language and code tokens can still deliver strong performance on natural language tasks, offering a practical solution when additional text data is not available.

Practical Implications

The findings have several practical implications:

1. Future LLM Training: Practitioners should consider repeated data more heavily and use the empirically validated scaling laws to derive optimal training configurations. This approach can help in resource-constrained settings where collecting vast amounts of unique data is impractical.
2. Augmenting Data with Code: The observation that mixing code with natural language data can maintain performance on natural language tasks while significantly boosting models' capabilities on tasks that benefit from state-tracking (like code generation) suggests a versatile approach. This strategy is particularly applicable in multilingual settings or for domain-specific applications where code data is abundant.
3. Filtering Decisions: The results indicate that filtering strategies should be selectively applied based on data quality. Perplexity filtering can enhance downstream performance when dealing with noisy datasets like OSCAR, while deduplication might have a varied impact, as seen with C4 and OSCAR datasets.

Theoretical Implications and Speculations

Theoretically, the proposed scaling laws refine our understanding of resource allocation in machine learning. By systematically analyzing how repeated data affects model performance, the authors provide a new lens through which to view training efficiency. These insights could prompt further exploration into:

• Optimization of Data-Efficient Architectures: Investigating architectures specifically designed to leverage repeated data efficiently.
• Scaling Laws for Other Modalities: Extending these findings to other data modalities and model architectures, exploring if similar diminishing returns patterns hold.

Conclusion and Future Directions

The paper "Scaling Data-Constrained LLMs" substantiates the viability of reusing data effectively under constrained settings, presenting an empirically validated extension of Chinchilla scaling laws. These insights enable more compute-efficient training practices and offer practical solutions for augmenting training datasets. Future research could explore optimizing architectures and training regimes under various data constraints, aiming to refine these scaling principles further and apply them across diverse domains.

Overall, by tackling the challenge of limited unique data availability and providing robust empirical analyses, this paper paves the way for more sustainable and efficient training of LLMs, ensuring continued advancements even in data-scarce scenarios.