How to Train Data-Efficient LLMs
<|reference_start|>How to Train Data-Efficient LLMs: 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.<|reference_end|>
@article{sachdeva2024how,
    title={How to Train Data-Efficient LLMs},
    author={Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, Jianmo Ni, Lichan
  Hong, Ed H. Chi, James Caverlee, Julian McAuley, Derek Zhiyuan Cheng},
    journal={arXiv preprint arXiv:2402.09668},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09668},
    primaryClass={cs.LG cs.AI cs.CL}
}