The paper introduces the ItD framework to improve the inductive capability of Large Language Models (LLMs) through deduction. It consists of two main components: Deductive Data Generation and Naive Bayesian Induction. The framework is tested on two types of induction tasks: Instruction Induction and List Function, showcasing significant performance improvements compared to existing methods. ItD effectively leverages the deductive capability of LLMs to enhance their inductive abilities.
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