  The training of large language models (LLMs) is expensive. In this paper, we
study data-efficient approaches for pre-training LLMs, i.e., techniques that
aim to optimize the Pareto frontier of model quality and training resource/data
consumption. We seek to understand the tradeoffs associated with data selection
routines based on (i) expensive-to-compute data-quality estimates, and (ii)
maximization of coverage and diversity-based measures in the feature space. Our
first technique, Ask-LLM, leverages the zero-shot reasoning capabilities of
instruction-tuned LLMs to directly assess the quality of a training example. To
target coverage, we propose Density sampling, which models the data
distribution to select a diverse sample. In our comparison of 19 samplers,
involving hundreds of evaluation tasks and pre-training runs, we find that
Ask-LLM and Density are the best methods in their respective categories.
Coverage sampling can recover the performance of the full data, while models
trained on Ask-LLM data consistently outperform full-data training -- even when
we reject 90% of the original dataset, while converging up to 70% faster.
