Patterns Over Principles: The Fragility of Inductive Reasoning in LLMs under Noisy Observations

• URL: http://arxiv.org/abs/2502.16169v1
• Date: Sat, 22 Feb 2025 10:03:19 GMT
• Title: Patterns Over Principles: The Fragility of Inductive Reasoning in LLMs under Noisy Observations
• Authors: Chunyang Li, Weiqi Wang, Tianshi Zheng, Yangqiu Song,
• Abstract summary: We introduce Robust Rule Induction, a task that evaluates large language models' capability in inferring rules from data fused with noisy examples.<n>We also propose Sample-steered Rule Refinement (SRR), a method enhancing reasoning stability via observation diversification and execution-guided feedback.<n>Our findings challenge LLMs' reasoning, revealing susceptibility to hypothesis drift and pattern overfitting, while providing empirical evidence critical for developing human-like inductive systems.
• Score: 43.491353243991284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inductive reasoning, a cornerstone of human cognition, enables generalization from limited data but hasn't yet been fully achieved by large language models (LLMs). While modern LLMs excel at reasoning tasks, their ability to maintain stable and consistent rule abstraction under imperfect observations remains underexplored. To fill this gap, in this work, we introduce Robust Rule Induction, a task that evaluates LLMs' capability in inferring rules from data that are fused with noisy examples. To address this task, we further propose Sample-steered Rule Refinement (SRR), a method enhancing reasoning stability via observation diversification and execution-guided feedback. Experiments across arithmetic, cryptography, and list functions reveal: (1) SRR outperforms other methods with minimal performance degradation under noise; (2) Despite slight accuracy variation, LLMs exhibit instability under noise (e.g., 0% accuracy change with only 70% consistent score); (3) Counterfactual task gaps highlight LLMs' reliance on memorized patterns over genuine abstraction. Our findings challenge LLMs' reasoning robustness, revealing susceptibility to hypothesis drift and pattern overfitting, while providing empirical evidence critical for developing human-like inductive systems. Code and data are available at \href{https://github.com/lcy2723/Robust-Rule-Induction}{https://github.com/lcy2723/Robust-Rule-Induction}.

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