Large Language Models (LLMs) show impressive inductive reasoning capabilities, enabling them to generate hypotheses that could generalize effectively to new instances when guided by in-context demonstrations. However, in real-world applications, LLMs' hypothesis generation is not solely determined by these demonstrations but is significantly shaped by task-specific model priors. Despite their critical influence, the distinct contributions of model priors versus demonstrations to hypothesis generation have been underexplored. This study bridges this gap by systematically evaluating three inductive reasoning strategies across five real-world tasks with three LLMs. Our empirical findings reveal that, hypothesis generation is primarily driven by the model's inherent priors; removing demonstrations results in minimal loss of hypothesis quality and downstream usage. Further analysis shows the result is consistent across various label formats with different label configurations, and prior is hard to override, even under flipped labeling. These insights advance our understanding of the dynamics of hypothesis generation in LLMs and highlight the potential for better utilizing model priors in real-world inductive reasoning tasks.
A Survey of Inductive Reasoning for Large Language Models
East China Normal University（华东师范大学）；Shanghai Innovation Institute（上海创新研究院）；Fudan University（复旦大学）；Xi’an Jiaotong University（西安交通大学）；Shanghai AI Laboratory（上海人工智能实验室）
On LLM-Based Scientific Inductive Reasoning Beyond Equations
Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, Tsinghua University（计算机科学与技术系，人工智能研究院，BNRist中心，清华大学）；Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University（江苏语言能力协同创新中心，江苏师范大学）；Beijing Language and Culture University（北京语言文化大学）；Xiamen University（厦门大学）；Harbin Institute of Technology（哈尔滨工业大学）
MIRAGE: Evaluating and Explaining Inductive Reasoning Process in Language Models
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Context-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning
The Chinese University of Hong Kong（香港中文大学）；International Digital Economy Academy（国际数字经济研究院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Cambridge（剑桥大学）
Mars: Situated Inductive Reasoning in an Open-World Environment
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；BIGAI（北京智源人工智能研究院）；State Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）；Department of Automation, Tsinghua University（清华大学自动化系）
Code-driven Number Sequence Calculation: Enhancing the inductive Reasoning Abilities of Large Language Models
East China Normal University（东华大学）；Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；Xi’an Jiaotong University（西安交通大学）；Shanghai AI Laboratory（上海人工智能实验室）
Knowledge Reasoning Language Model: Unifying Knowledge and Language for Inductive Knowledge Graph Reasoning
The Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, China（合肥工业大学大数据知识工程教育部重点实验室）；School of Computer Science and Information Engineering, Hefei University of Technology, China（合肥工业大学计算机与信息学院）；Nanjing University of Science and Technology, China（南京理工大学）；China Unicom Digital Technology Co., Ltd., Beijing, China（联通数字科技有限公司）；China Unicom Internet of Things Co., Ltd., Nanjing, China（联通物联网有限责任公司）；Shandong Inspur Science Research Institute, Jinan, China（山东浪潮科学研究院）；Griffith University, Australia（格里菲斯大学）
Reasoning Can Hurt the Inductive Abilities of Large Language Models
School of Information Sciences University of Illinois at Urbana-Champaign（信息科学学院伊利诺伊大学厄巴纳-香槟分校）；Computer Science and Engineering HKUST（计算机科学与工程香港科技大学）；Research Scientist, Intel Labs（英特尔实验室研究员）；School of Information Sciences University of Illinois Urbana-Champaign（信息科学学院伊利诺伊大学厄巴纳-香槟分校）
The Role of Deductive and Inductive Reasoning in Large Language Models
University of Edinburgh（爱丁堡大学）；University of Manchester（曼彻斯特大学）；University of Washington（华盛顿大学）；Tsinghua University（清华大学）；Skai Intelligence（Skai智能科技）；University of Copenhagen（哥本哈根大学）
The Sword, Shield, and Achilles' Heel: Characterizing the Linguistic Inductive Bias of Large Language Models for Spatial Reasoning in Navigation Planning
East China Normal University（东华大学）；Information Engineering University（信息工程大学）；Zhengzhou University（郑州大学）
PACR: Progressively Ascending Confidence Reward for LLM Reasoning
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Microsoft Research Asia (MSRA)（微软亚洲研究院）；University of Illinois at Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）
R-APS: Compositional Reasoning and In-Context Meta-Learning for Constrained Design via Reflective Adversarial Pareto Search
Idiap Research Institute（Idiap研究 institute）；École Polytechnique Fédérale de Lausanne（瑞士联邦理工学院）；Honda Research Institute Europe（本田欧洲研究机构）；Department of Computer Science, University of Manchester（曼彻斯特大学计算机科学系）；National Biomarker Centre, CRUK-MI, University of Manchester（曼彻斯特大学国家生物标志物中心）
Neuro Symbolic Reasoning for Planning: Counterexample Guided Inductive Synthesis using Large Language Models and Satisfiability Solving
Florida International University（佛罗里达国际大学）；SRI International（SRI国际（斯坦福国际研究院））；United States Military Academy（美国军事学院（西点军校））；University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Central Florida（中佛罗里达大学）；Vanderbilt University（范德堡大学）
Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement
Massachusetts Institute of Technology（麻省理工学院）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Washington（华盛顿大学）；University of Southern California（南加州大学）
Expanding the Scope: Inductive Knowledge Graph Reasoning with Multi-Starting Progressive Propagation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；National Institute of Healthcare Data Science, Nanjing University（南京大学健康医疗大数据国家研究院）
Cumulative Path-Level Semantic Reasoning for Inductive Knowledge Graph Completion
Nanjing University of Science and Technology（南京理工大学）；Jinan University（济南大学）；Guilin University of Electronic Technology（桂林电子科技大学）；Shanxi University（山西大学）；Nanyang Technological University（南洋理工大学）；Beijing University Of Technology（北京理工大学）
