Will we run out of data? Limits of LLM scaling based on human-generated data
This identifies a potential bottleneck for AI progress that could affect all LLM developers and users, though it is an incremental analysis based on existing trends.
The paper investigates constraints on large language model (LLM) scaling due to limited public human-generated text data, forecasting that models will exhaust this data stock between 2026 and 2032 if current trends continue.
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.