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Chain-of-Thought Reasoning in AI Models by anrang561 is a document available to read on EtoBox.

What is Chain-of-Thought Reasoning in AI Models about?

This document explores the effectiveness of step-by-step reasoning in both humans and large language models, positing that reasoning is particularly beneficial when training data exhibits local structures of interrelated variables. The authors demonstrate that generating intermediate reasoning steps can improve inference accuracy, especially when direct predictions are hindered by the lack of co-occurring data. Their findings suggest that reasoning through intermediate variables allows for better estimation

Author

anrang561

Language

EN