Language Model Scaling Law

A Language Model Scaling Law is an deep learning scaling law that describes how language model performance relates to language model size, language model training data, or language model computational resources.

• Context:
  • It can (typically) help in understanding the trade-offs between model size, training data, and computational resources in language model development.
  • It can (typically) show that larger models are more sample-efficient, achieving similar or better performance with fewer data and training steps than smaller models.
  • It can (typically) advocate for the use of larger batch sizes for training large models, with the ideal batch size scaling with the gradient noise of the model.
  • It can (often) demonstrate that model architecture factors such as depth or width have minimal effect on performance compared to scaling parameters like model size, data, and compute.
  • ...
  • It can range from being a Empirical Language Model Scaling Law to being a Theoretical Language Model Scaling Law.
  • It can range from being a Simple Language Model Scaling Law focusing on a single variable (e.g., parameter count) to being a Complex Language Model Scaling Law incorporating multiple factors (e.g., parameters, data, compute).
  • ...
  • It can suggest optimal training efficiency is achieved by training large models on modest datasets and halting training before full convergence.
  • It can guide resource allocation decisions in natural language processing research and development.
  • It can reveal that increasing training data size leads to improved generalization in language models, typically following a logarithmic relationship.
  • It can predict the performance ceiling of language models and estimate the resources needed to achieve certain performance levels.
  • It can be used to extrapolate the potential capabilities of future, larger language models.
  • It can ) highlight that larger models can be significantly more compute-efficient when trained with the appropriate compute budget than smaller models trained to convergence.
  • It can ) show that performance gains from increasing model size exhibit diminishing returns at very large scales.
  • It can ) indicate that training time and computational cost for language models scale superlinearly with increases in model or data size.
  • It can ) be used to inform the design of large language models (LLMs), helping researchers and engineers make decisions about model architecture and training strategies.
  • It can ) reveal unexpected emergent properties in very large language models, such as in-context learning abilities.
  • It can ) help in comparing the efficiency of different language model architectures, such as Transformer variants.
  • It can ) be applied to various language modeling tasks, including but not limited to text generation, machine translation, and question answering.
  • It can ) break down or change behavior at extreme scales, necessitating new models or explanations for language model scaling behavior.
  • It can ) inform AI safety considerations by predicting the capabilities of advanced language models.
  • It can ) provide insights into predicting and mitigating overfitting by maintaining a balance between model size and dataset size.
  • It can ) enable researchers to forecast final performance based on early training data through predictable training curves.
  • ...
• Example(s):
  • GPT-3 Scaling Law: relating model size to language model performance in the GPT-3 family of models, where:
    • Model Performance (measured by test loss or perplexity) improves as a power law of the number of model parameters.
    • The relationship follows the form: Loss ∝ (Number of Parameters)^(-0.076), showing consistent improvement across several orders of magnitude of model size.
    • This scaling law predicted the performance of the full GPT-3 model (175 billion parameters) based on smaller variants.
  • Chinchilla Scaling Law: relating model size and training tokens to language model performance, where:
    • Optimal Model Size for a given compute budget is determined by balancing model size and training dataset size.
    • The law suggests that many language models are overparameterized and undertrained relative to the optimal allocation of compute.
    • It proposes that for each doubling of compute, model size and training tokens should both increase by approximately 20%.
  • PaLM Scaling Law: demonstrating few-shot learning improvements in the Pathways Language Model (PaLM), where:
    • Few-shot performance on various tasks improves smoothly with model scale, following a power law.
    • The scaling behavior holds across a wide range of model sizes, from 8 billion to 540 billion parameters.
    • Some tasks show a discontinuous jump in performance at certain model sizes, suggesting emergent abilities.
  • Multilingual Scaling Law: describing how language model performance scales across multiple languages, where:
    • Cross-lingual transfer improves with model size, allowing larger models to perform better on low-resource languages.
    • The scaling behavior varies across languages, with some benefiting more from increased model size than others.
    • Language-specific performance gaps tend to narrow as models become larger, but some disparities persist.
  • Kaplan et al. Scaling Laws: encompassing multiple aspects of language model scaling, where:
    • Performance scales smoothly as a power-law with model size, dataset size, and amount of compute used for training.
    • The power-law exponent for performance improvement with compute is around 0.050–0.057 for large models.
    • Optimal allocation of a fixed compute budget suggests training very large models and stopping significantly short of convergence.
    • The relationship between model size and optimal compute budget follows: Optimal compute ∝ (Number of Parameters)^(0.73–0.9).
  • ...
• Counter-Example(s):
  • Task-Specific Plateaus: cases where increasing language model size does not improve performance on certain specialized tasks, such as:
    • Arithmetic Reasoning tasks, where performance may plateau despite increasing model size.
    • Common Sense Reasoning tasks, which may require different scaling approaches or architectural changes.
  • Data-Limited Scenarios: situations where the available training data becomes a limiting factor, including:
    • Low-Resource Languages, where the scarcity of high-quality training data may limit the benefits of model scaling.
    • Specialized Domains with limited available text corpora, where increasing model size may lead to overfitting.
  • Efficiency-Focused Approaches: methods that achieve comparable performance with smaller models, such as:
    • Distillation Techniques, where smaller models are trained to mimic larger ones, potentially breaking the usual scaling laws.
    • Sparse Models, which may exhibit different scaling behaviors compared to dense models of similar parameter counts.
  • Non-Autoregressive Models: language models with different architectures that may not follow the same scaling laws, like:
    • BERT-style bidirectional encoder models, which may have different scaling properties compared to autoregressive models like GPT.
    • T5-style encoder-decoder models, which might exhibit unique scaling behaviors due to their architecture.
• See: AI System Scaling Law, Model Size Scaling, Training Data Scaling, Computational Scaling in NLP, Language Model Evaluation Metrics, Emergent Abilities in Language Models, Resource-Performance Trade-offs in NLP, Cross-Lingual Transfer Learning

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References

2023

• (Wei et al., 2023) ⇒ Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, William Fedus. (2023). "Emergent Abilities of Large Language Models." In: Transactions on Machine Learning Research. arXiv:2206.07682
  • NOTE: This paper discusses how language model scaling laws relate to emergent abilities, providing insights into unexpected behaviors that arise as models become larger.

2022

• (Hoffmann et al., 2022) ⇒ Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Sigler, Mia Xu Chen, Sharan Narang, Saffron Huang, Colin Hubert, Steven Kapturowski, John Aslanides, George van den Driessche, Dani Yogatama, Jared Kaplan, Aaron Goyal, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Demis Hassabis, Laurent Sifre. (2022). "Training Compute-Optimal Large Language Models." In: arXiv preprint arXiv:2203.15556. arXiv:2203.15556
  • NOTE: This paper introduces the Chinchilla scaling law, which provides insights into the optimal balance between model size and training data for language models.

2020

• (Kaplan et al., 2020) ⇒ Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei. (2020). "Scaling Laws for Neural Language Models." In: arXiv preprint arXiv:2001.08361. arXiv:2001.08361
  • NOTES:
    • The paper identifies power-law relationships between model size, dataset size, and compute, showing that performance improves predictably with scale across these factors.
    • The paper demonstrates that model architecture, such as depth or width, has a minimal effect on performance compared to scaling parameters like model size, data, and compute.
    • The paper reveals that larger models are more sample-efficient, achieving similar or better performance with fewer data and training steps than smaller models.
    • The paper suggests that optimal training efficiency is achieved by training large models on modest datasets and halting training well before full convergence.
    • The paper finds that overfitting can be predicted and mitigated by maintaining a balance between model size and dataset size, using a simple ratio to avoid diminishing returns.
    • The paper emphasizes that training curves follow predictable patterns, enabling researchers to forecast final performance based on early training data.
    • The paper highlights that larger models, when trained with the appropriate compute budget, can be significantly more compute-efficient than smaller models trained to convergence.
    • The paper shows that performance scales smoothly across multiple orders of magnitude, with no significant deviation in trends, even as model size increases dramatically.
    • The paper advocates for the use of larger batch sizes for training large models, with the ideal batch size scaling with the gradient noise of the model.
    • The paper concludes that scaling model size is more impactful than increasing data size or training time, recommending a focus on larger models for future improvements.

2018

• (Hestness et al., 2018) ⇒ Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, Yanqi Zhou. (2018). "Deep Learning Scaling is Predictable, Empirically." In: arXiv preprint arXiv:1712.00409. arXiv:1712.00409
  • NOTE: While not specifically focused on language models, this paper provides early evidence of power-law scaling in deep learning, which laid the groundwork for later language model scaling laws.

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