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Distilling Decomposition in LLMs by sbarrow801scribd is a document available to read on EtoBox.

This paper explores the effectiveness of distilling the problem decomposition capability from Large Language Models (LLMs) to improve reasoning tasks while reducing inference costs. The authors propose a two-stage approach that separates the decomposition and solving phases, demonstrating that decomposing tasks is easier and more generalizable than solving them. Results indicate that using distilled decomposer models alongside problem-solving LLMs can enhance reasoning efficiency without significant perform

Author

sbarrow801scribd

Language

EN