Reasoning is an important task for large language models (LLMs). Among all the reasoning paradigms, inductive reasoning is one of the fundamental types, which is characterized by its particular-to-general thinking process and the non-uniqueness of its answers. The inductive mode is crucial for knowledge generalization and aligns better with human cognition, so it is a fundamental mode of learning, hence attracting increasing interest. Despite the importance of inductive reasoning, there is no systematic summary of it. Therefore, this paper presents the first comprehensive survey of inductive reasoning for LLMs. First, methods for improving inductive reasoning are categorized into three main areas: post-training, test-time scaling, and data augmentation. Then, current benchmarks of inductive reasoning are summarized, and a unified sandbox-based evaluation approach with the observation coverage metric is derived. Finally, we offer some analyses regarding the source of inductive ability and how simple model architectures and data help with inductive tasks, providing a solid foundation for future research.

翻译：推理是大语言模型（LLM）的一项重要任务。在所有推理范式中，归纳推理是基础类型之一，其特点是具有从特殊到一般的思维过程以及答案的非唯一性。归纳模式对于知识泛化至关重要，且更符合人类认知，因此是一种基本的学习模式，正日益受到关注。尽管归纳推理具有重要性，但目前尚缺乏对其的系统性总结。为此，本文首次对大语言模型的归纳推理进行了全面综述。首先，将改进归纳推理的方法归纳为三大领域：后训练、测试时扩展和数据增强。然后，总结了当前的归纳推理基准，并提出了一种统一的基于沙盒的评估方法以及观测覆盖率指标。最后，我们分析了归纳能力的来源，并探讨了简单的模型架构和数据如何有助于归纳任务，为未来研究奠定了坚实基础。