Position: Will we run out of data? Limits of LLM scaling based on human-generated data

Pablo Villalobos, Anson Ho, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, Marius Hobbhahn
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:49523-49544, 2024.

Abstract

We investigate the potential constraints on LLM scaling posed by the availability of public human-generated text data. We forecast the growing demand for training data based on current trends and estimate the total stock of public human text data. Our findings indicate that if current LLM development trends continue, models will be trained on datasets roughly equal in size to the available stock of public human text data between 2026 and 2032, or slightly earlier if models are overtrained. We explore how progress in language modeling can continue when human-generated text datasets cannot be scaled any further. We argue that synthetic data generation, transfer learning from data-rich domains, and data efficiency improvements might support further progress.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-villalobos24a, title = {Position: Will we run out of data? {L}imits of {LLM} scaling based on human-generated data}, author = {Villalobos, Pablo and Ho, Anson and Sevilla, Jaime and Besiroglu, Tamay and Heim, Lennart and Hobbhahn, Marius}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {49523--49544}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/villalobos24a/villalobos24a.pdf}, url = {https://proceedings.mlr.press/v235/villalobos24a.html}, abstract = {We investigate the potential constraints on LLM scaling posed by the availability of public human-generated text data. We forecast the growing demand for training data based on current trends and estimate the total stock of public human text data. Our findings indicate that if current LLM development trends continue, models will be trained on datasets roughly equal in size to the available stock of public human text data between 2026 and 2032, or slightly earlier if models are overtrained. We explore how progress in language modeling can continue when human-generated text datasets cannot be scaled any further. We argue that synthetic data generation, transfer learning from data-rich domains, and data efficiency improvements might support further progress.} }
Endnote
%0 Conference Paper %T Position: Will we run out of data? Limits of LLM scaling based on human-generated data %A Pablo Villalobos %A Anson Ho %A Jaime Sevilla %A Tamay Besiroglu %A Lennart Heim %A Marius Hobbhahn %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-villalobos24a %I PMLR %P 49523--49544 %U https://proceedings.mlr.press/v235/villalobos24a.html %V 235 %X We investigate the potential constraints on LLM scaling posed by the availability of public human-generated text data. We forecast the growing demand for training data based on current trends and estimate the total stock of public human text data. Our findings indicate that if current LLM development trends continue, models will be trained on datasets roughly equal in size to the available stock of public human text data between 2026 and 2032, or slightly earlier if models are overtrained. We explore how progress in language modeling can continue when human-generated text datasets cannot be scaled any further. We argue that synthetic data generation, transfer learning from data-rich domains, and data efficiency improvements might support further progress.
APA
Villalobos, P., Ho, A., Sevilla, J., Besiroglu, T., Heim, L. & Hobbhahn, M.. (2024). Position: Will we run out of data? Limits of LLM scaling based on human-generated data. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:49523-49544 Available from https://proceedings.mlr.press/v235/villalobos24a.html.

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