Papers with Inductive reasoning
Language Models as Inductive Reasoners (2024.eacl-long)
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| Challenge: | Inductive reasoning is a core component of human intelligence. |
| Approach: | They propose a task to induce natural language rules from natural language facts using natural language as representation for knowledge instead of formal language. |
| Outcome: | The proposed task surpasses baselines in both automatic and human evaluations. |
A Comprehensive Evaluation of Inductive Reasoning Capabilities and Problem Solving in Large Language Models (2024.findings-eacl)
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| Challenge: | Inductive reasoning is fundamental to both human and artificial intelligence. |
| Approach: | They evaluated the inductive reasoning abilities of current Large Language Models (LLMs) and their performance on symbolic tasks. |
| Outcome: | The proposed models fail on symbolic tasks and show that chain-of-thought prompts help them by decomposing the problem-solving process, but the LLMs learn limitedly. |
Generating Diverse Hypotheses for Inductive Reasoning (2025.naacl-long)
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| Challenge: | Recent studies suggest that large language models (LLMs) can engage in inductive reasoning by sampling multiple hypotheses about the rules and selecting the one that best explains the observations. |
| Approach: | They propose to increase the temperature parameter to enhance diversity by sampling multiple hypotheses and selecting the one that best explains the observations. |
| Outcome: | The proposed method improves diversity while maintaining text quality while increasing temperature. |
Patterns Over Principles: The Fragility of Inductive Reasoning in LLMs under Noisy Observations (2025.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated remarkable performance across various reasoning tasks. |
| Approach: | They propose a task that evaluates LLMs’ capability in inferring rules from data fused with noisy examples. |
| Outcome: | The proposed method outperforms other methods with minimal performance degradation under noise and counterfactual task gaps highlight LLMs’ reliance on memorized patterns over genuine abstraction. |