A Survey of Inductive Reasoning for Large Language Models

Researchers present the first comprehensive survey of inductive reasoning in large language models, categorizing improvement methods into post-training, test-time scaling, and data augmentation approaches. The survey establishes unified benchmarks and evaluation metrics for assessing how LLMs perform particular-to-general reasoning tasks that better align with human cognition.

This survey addresses a significant gap in LLM research by systematizing inductive reasoning—a fundamental cognitive process where models generalize from specific examples to broader principles. Unlike deductive reasoning, which derives specific conclusions from general rules, inductive reasoning mirrors how humans naturally learn and adapt, making it essential for building more human-aligned AI systems. The research categorizes three distinct approaches to improving inductive capabilities: post-training methods that refine models after initial development, test-time scaling strategies that enhance performance during inference, and data augmentation techniques that expand training datasets. The establishment of unified benchmarks with observation coverage metrics provides researchers with standardized tools to measure progress, addressing the current fragmentation in evaluation approaches across the field. This systematization carries practical implications for AI development teams and researchers seeking to build more capable reasoning systems. Organizations developing applications requiring generalization from limited examples—such as few-shot learning scenarios, anomaly detection, or pattern recognition—stand to benefit from clearer methodologies. The survey's analysis of model architectures and data's role in inductive tasks offers actionable insights for practitioners optimizing their systems. Moving forward, the field should focus on bridging the gap between benchmark performance and real-world generalization, as well as understanding why certain architectural choices and training approaches yield superior inductive capabilities. The research also opens questions about whether improved inductive reasoning translates to better performance in complex reasoning chains and multi-domain applications.

• →Inductive reasoning enables LLMs to generalize from specific examples to broader principles, fundamental for knowledge development and human-aligned AI
• →Three primary improvement methods exist: post-training refinement, test-time scaling optimization, and data augmentation strategies
• →Unified benchmarks with observation coverage metrics provide standardized evaluation approaches for measuring inductive reasoning progress
• →Simple model architectures and thoughtfully designed data significantly contribute to inductive task performance
• →The survey provides foundational analysis for understanding the sources of inductive ability in large language models