Large Language Model-guided Document Selection
- URL: http://arxiv.org/abs/2406.04638v1
- Date: Fri, 7 Jun 2024 04:52:46 GMT
- Title: Large Language Model-guided Document Selection
- Authors: Xiang Kong, Tom Gunter, Ruoming Pang,
- Abstract summary: Large Language Model (LLM) pre-training exhausts an ever growing compute budget.
Recent research has demonstrated that careful document selection enables comparable model quality with only a fraction of the FLOPs.
We explore a promising direction for scalable general-domain document selection.
- Score: 23.673690115025913
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large Language Model (LLM) pre-training exhausts an ever growing compute budget, yet recent research has demonstrated that careful document selection enables comparable model quality with only a fraction of the FLOPs. Inspired by efforts suggesting that domain-specific training document selection is in fact an interpretable process [Gunasekar et al., 2023], as well as research showing that instruction-finetuned LLMs are adept zero-shot data labelers [Gilardi et al.,2023], we explore a promising direction for scalable general-domain document selection; employing a prompted LLM as a document grader, we distill quality labels into a classifier model, which is applied at scale to a large, and already heavily-filtered, web-crawl-derived corpus autonomously. Following the guidance of this classifier, we drop 75% of the corpus and train LLMs on the remaining data. Results across multiple benchmarks show that: 1. Filtering allows us to quality-match a model trained on the full corpus across diverse benchmarks with at most 70% of the FLOPs, 2. More capable LLM labelers and classifier models lead to better results that are less sensitive to the labeler's prompt, 3. In-context learning helps to boost the performance of less-capable labeling models. In all cases we use open-source datasets, models, recipes, and evaluation frameworks, so that results can be reproduced by the community.
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