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, Wei Zhang
| Challenge: | Inductive reasoning is an important task for large language models (LLMs). |
| Approach: | They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation. |
| Outcome: | The proposed method improves inductive reasoning in large language models. |
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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. |
Towards Reasoning in Large Language Models: A Survey (2023.findings-acl)
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| Challenge: | Reasoning is a fundamental aspect of human intelligence that plays a crucial role in many intellectual activities. |
| Approach: | They propose to improve LLMs' ability to elicit reasoning by providing exemplars or prompts to model reasoning. |
| Outcome: | This paper provides a comprehensive overview of the state of knowledge on reasoning in large language models. |
On LLM-Based Scientific Inductive Reasoning Beyond Equations (2025.emnlp-main)
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Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing research on inductive reasoning models emphasizes rule design without grounding them in specific scenarios. |
| Approach: | They propose to use LLMs to learn underlying patterns from limited examples in entirely new environments. |
| Outcome: | The proposed benchmark evaluates the inductive reasoning abilities of large language models in scientific settings. |
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. |
On the Role of Model Prior in Real-World Inductive Reasoning (2025.emnlp-main)
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| Challenge: | Existing studies have evaluated the inductive reasoning capabilities of Large Language Models (LLMs) by evaluating their ability to generate textual hypotheses based on in-context input-output pairs and test these hypothese based upon unseen examples. |
| Approach: | They evaluated three inductive reasoning strategies across five real-world tasks with three LLMs and found that hypothesis generation is primarily driven by the model’s inherent priors. |
| Outcome: | The proposed models generate high-quality hypotheses that can generalize to new instances when guided by in-context demonstrations. |
Current Advances in LLM Reasoning (2026.acl-tutorials)
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
| Approach: | This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) and discusses two types of methods to improve models’ reasoning: advanced inference time methods, structured and self-improvement inference methods, and post-training methods, such as RLHF, DPO, and GRPO. |
| Outcome: | This tutorial examines evaluation strategies to assess the reasoning abilities of large language models and discusses two types of methods to improve models’ reasoning. |
The Role of Deductive and Inductive Reasoning in Large Language Models (2025.acl-long)
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Chengkun Cai, Xu Zhao, Haoliang Liu, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, Jenq-Neng Hwang, Lei Li
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning tasks, yet their reliability in problem-solving remains debatable. |
| Approach: | They propose a framework that integrates both deductive and inductive reasoning approaches to enhance LLM reasoning by progressively adapting its reasoning pathways based on problem complexity. |
| Outcome: | The proposed framework achieves 70.3% accuracy on AIW, compared to 62.2% for Tree of Thought, while maintaining lower computational costs. |
InductionBench: LLMs Fail in the Simplest Complexity Class (2025.acl-long)
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| Challenge: | Existing benchmarks focus on deductive reasoning, largely overlooking inductive reasoning. |
| Approach: | They propose a benchmark to evaluate the inductive reasoning ability of large language models. |
| Outcome: | The proposed benchmark demonstrates that even the most advanced modelw struggle to master the simplest complexity classes within the subregular hierarchy of functions. |
Large Language Models for Mathematical Reasoning: Progresses and Challenges (2024.eacl-srw)
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| Challenge: | a survey examines the landscape of mathematical problem-solving techniques . large language models have proven to be potent assets in unraveling nuances of mathematical reasoning . |
| Approach: | They examine the evolution of Large Language Models (LLMs) for solving mathematical problems . they examine the spectrum of LLM-oriented techniques proposed for solving math problems - and their challenges . |
| Outcome: | The survey examines the spectrum of proposed LLM-oriented techniques in solving math problems. |
Do Large Language Models excel in Complex Logical Reasoning with Formal Language? (2025.emnlp-main)
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| Challenge: | Existing studies on LLMs have focused on formal language, but evaluations of their performance are limited. |
| Approach: | They propose to use a formal language to evaluate LLMs across logical reasoning problems using formal languages. |
| Outcome: | The proposed model outperforms Instruct models in three dimensions, taxonomy of tasks, and format of trajectories, and achieves the best generalization performance across other languages. |