@inproceedings{chen-etal-2026-survey,
title = "A Survey of Inductive Reasoning for Large Language Models",
author = "Chen, Kedi and
Ruan, Dezhao and
Dan, Yuhao and
Wang, Yaoting and
Yan, Siyu and
Wu, Xuecheng and
Zhang, Yinqi and
Chen, Qin and
Zhou, Jie and
He, Liang and
Qi, Biqing and
Li, Linyang and
Guo, Qipeng and
Shi, Xiaoming and
Zhang, Wei",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1447/",
pages = "31356--31382",
ISBN = "979-8-89176-390-6",
abstract = "Reasoning is an important task for large language models (LLMs). Among all the reasoning paradigms, inductive reasoning is one of the basic 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 enhancement, test-time exploration, 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."
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%0 Conference Proceedings
%T A Survey of Inductive Reasoning for Large Language Models
%A Chen, Kedi
%A Ruan, Dezhao
%A Dan, Yuhao
%A Wang, Yaoting
%A Yan, Siyu
%A Wu, Xuecheng
%A Zhang, Yinqi
%A Chen, Qin
%A Zhou, Jie
%A He, Liang
%A Qi, Biqing
%A Li, Linyang
%A Guo, Qipeng
%A Shi, Xiaoming
%A Zhang, Wei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F chen-etal-2026-survey
%X Reasoning is an important task for large language models (LLMs). Among all the reasoning paradigms, inductive reasoning is one of the basic 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 enhancement, test-time exploration, 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.
%U https://aclanthology.org/2026.acl-long.1447/
%P 31356-31382
Markdown (Informal)
[A Survey of Inductive Reasoning for Large Language Models](https://aclanthology.org/2026.acl-long.1447/) (Chen et al., ACL 2026)
ACL
- Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, and Wei Zhang. 2026. A Survey of Inductive Reasoning for Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 31356–31382, San Diego, California, United States. Association for Computational Linguistics.