Exploring Deductive and Inductive Reasoning Capabilities of Large Language Models in Procedural Planning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Deductive and inductive reasoning are fundamental components of human cognition . authors present a benchmark to assess their performance in procedural planning . |
| Approach: | They propose a benchmark to assess the deductive and inductive reasoning abilities of LLMs . they propose IMSE to enable LLM to generate multiple similar procedural plans . |
| Outcome: | The proposed method improves inductive reasoning abilities of LLMs, the authors show . they show that LLM models show excellent deductive reasoning capabilities but suboptimal inductive performance. |
Similar Papers
A Comprehensive Evaluation of Inductive Reasoning Capabilities and Problem Solving in Large Language Models (2024.findings-eacl)
Copied to clipboard
| 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. |
The Role of Deductive and Inductive Reasoning in Large Language Models (2025.acl-long)
Copied to clipboard
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. |
A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)
Copied to clipboard
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. |
InductionBench: LLMs Fail in the Simplest Complexity Class (2025.acl-long)
Copied to clipboard
| 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. |
Do Large Language Models excel in Complex Logical Reasoning with Formal Language? (2025.emnlp-main)
Copied to clipboard
| 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. |
Towards Reasoning in Large Language Models: A Survey (2023.findings-acl)
Copied to clipboard
| 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. |
ItD: Large Language Models Can Teach Themselves Induction through Deduction (2024.acl-long)
Copied to clipboard
| Challenge: | Recent studies have shown that Large Language Models (LLMs) have limited ability to conduct induction. |
| Approach: | They propose a framework to enable LLMs to teach themselves induction through deduction. |
| Outcome: | The proposed framework improves performance on two induction benchmarks and shows that it can be used to teach induction through deduction. |
PARADISE: Evaluating Implicit Planning Skills of Language Models with Procedural Warnings and Tips Dataset (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have focused on whether large language models are capable of planning or executing plans. |
| Approach: | They propose an abductive reasoning task using wikiHow to test the effectiveness of small models over large models. |
| Outcome: | The proposed task demonstrates the effectiveness of small models over large models in most scenarios. |
Comparing Inferential Strategies of Humans and Large Language Models in Deductive Reasoning (2024.acl-long)
Copied to clipboard
| Challenge: | Recent advances in the domain of large language models (LLMs) have showcased their capability in executing deductive reasoning tasks. |
| Approach: | They examine inferential strategies employed by large language models through a detailed evaluation of their responses to propositional logic problems. |
| Outcome: | The proposed model shows that it displays reasoning patterns similar to humans, including strategies like supposition following or chain construction. |
LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models excel in various natural language tasks but struggle with long-horizon planning problems requiring structured reasoning. |
| Approach: | They propose to integrate large language models into AP and NLP planning frameworks by reviewing current research and identifying critical challenges and future directions. |
| Outcome: | The proposed frameworks are used to support reliable off-the-shelf AP planners. |