Core Concepts
Large language models can improve reasoning tasks with Iter-CoT, enhancing reasoning chains autonomously.
Abstract
Large language models (LLMs) benefit from chain-of-thought (CoT) prompting.
Iter-CoT rectifies errors and selects challenging yet answerable questions.
Demonstrations are crucial for model generalizability.
Experimental results show Iter-CoT outperforms existing methods on various tasks.
Stats
大規模言語モデル(LLM)は、イテレーションCoTで理論的なタスクを改善します。
イテレーションCoTは、エラーを修正し、挑戦的でありながらも回答可能な質問を選択します。
Quotes
"Iter-CoT has two advantages: it adopts iterative bootstrapping that enables LLMs to rectify errors autonomously."
"Experimental results exhibit Iter-CoT superior performance on three distinct reasoning tasks on ten datasets."