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arxiv:2402.09668

How to Train Data-Efficient LLMs

Published on Feb 15
· Featured in Daily Papers on Feb 16
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Abstract

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.

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This was a fascinating read. Amazing research

@Noveen , have you considered fine-tuning an encoder based model with the data from "ASK-LLM Sampling" for cheaper classification? Also will you publish that dataset?

Really cool research! We need more papers like this targeting efficiencies and using simple techniques to do so.

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Paper author

Thanks for reading and your kind words, Derek!

Good idea about fine-tuning a smaller model on these Ask-LLM scores - we haven't explored this as of yet. Regarding open-sourcing the Ask-LLM scores - I'm not sure whether that would be possible but I'll try to explore this further.

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