ItD: Large Language Models Can Teach Themselves Induction through
  Deduction
Wangtao Sun Haotian Xu Xuanqing Yu Pei Chen Shizhu He Jun Zhao Kang Liu
Although Large Language Models (LLMs) are showing impressive performance on a
wide range of Natural Language Processing tasks, researchers have found that
they still have limited ability to conduct induction. Recent works mainly adopt
``post processes'' paradigms to improve the performance of LLMs on induction
(e.g., the hypothesis search & refinement methods), but their performance is
still constrained by the inherent inductive capability of the LLMs. In this
paper, we propose a novel framework, Induction through Deduction (ItD), to
enable the LLMs to teach themselves induction through deduction. The ItD
framework is composed of two main components: a Deductive Data Generation
module to generate induction data and a Naive Bayesian Induction module to
optimize the fine-tuning and decoding of LLMs. Our empirical results showcase
the effectiveness of ItD on two induction benchmarks, achieving relative
performance improvement of 36% and 10% compared with previous state-of-the-art,
respectively. Our ablation study verifies the effectiveness of two key modules
of ItD. We also verify the effectiveness of ItD across different LLMs and
deductors. The data and code of this paper can be found at
https://anonymous.4open.science/r/ItD-E844.