How to Train Data-Efficient LLMs

Developments The authors compare a number of sampling methods and demonstrate that an LLM that choosing high quality pre-training data with a simple prompt can result in outperforming models that converge 70% faster while rejecting 90% of data. The resulting model they call Ask-LLM.

Method The authors a number of sampling methods including those that were heuristic-based including compute-efficient density/perplexity estimation. The models that were most , gains were primarily found when using a

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This is a pretraining .... datapoint.
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Does the previous paragraph demarcated within ### and ### contain info
rmative signal for pre-raining a large-language model?
An informaive datapoint should be well-formatted, contain some usable knowledge of the world, and strictly NOT have any harmful, racist, sexist, etc. content. 

OPTIONS: 
- yes
- no

Results The LLM-based quality filtering yields a "Pareto optimal efficiency between data quanity and model quality", helping to reduce environment and thereby becoming a net social-good.