A new preprint introduces SOAP, a matrix-structured optimizer that demonstrates faster and more accurate training for atom-level AI models compared to Adam, particularly in scenarios with limited labeled data. Separately, a 22-language study reveals that prompting Large Language Models (LLMs) to reason in English while responding in other languages significantly reduces their uncertainty gap across various languages. AI
IMPACT These findings could lead to more efficient AI training and improved performance for multilingual LLMs.
RANK_REASON The cluster contains two distinct research findings from preprints and studies, one on AI model training optimization and another on LLM reasoning.
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