Pretraining data distribution shapes LLM in-context learning, study finds

A new theoretical framework and empirical study explore how the statistical properties of pretraining data influence in-context learning (ICL) in large language models. Researchers found that heavy-tailed pretraining distributions, while beneficial for task selection under distribution shifts, can hinder generalization, particularly in low-data scenarios. The study suggests that controlling these statistical properties is crucial for developing reliable LLMs with strong ICL capabilities. AI

IMPACT Provides theoretical and empirical insights into how pretraining data characteristics affect LLM adaptability, potentially guiding future model development for improved in-context learning.

RANK_REASON Academic paper detailing theoretical framework and empirical evaluation of LLM pretraining distributions and their impact on in-context learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• In-Context Learning
• Large Language Models
• Pretraining Distribution
• Waïss Azizian

• paper
• model release

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