New BLISS method speeds up LLM pretraining with efficient data selection

Researchers have developed BLISS, a novel method for selecting data to pretrain large language models more efficiently. Unlike previous methods, BLISS does not require external pretrained models and accounts for the long-term impact of data by using a proxy model and a score model. This bilevel optimization approach allows BLISS to predict influence scores for training samples, enabling the selection of high-quality data. Experiments with Pythia and LLaMA models showed that BLISS achieved a 1.7x speedup in reaching target performance compared to state-of-the-art methods. AI

IMPACT Enables faster and more efficient pretraining of large language models by optimizing data selection.

RANK_REASON The cluster contains an academic paper detailing a new method for language model pretraining. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• BLISS
• LLaMA
• LLM
• Pythia

• paper
• model release

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