| 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. |
Similar Papers
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. |
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. |
On LLM-Based Scientific Inductive Reasoning Beyond Equations (2025.emnlp-main)
Copied to clipboard
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. |
Injecting structural hints: Using language models to study inductive biases in language learning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study examines the cognitive inductive biases that make language learning possible. |
| Approach: | They structurally bias transformer language models by pretraining on synthetic data . they then evaluate their inductive biases by fine-tuning on three different languages . |
| Outcome: | The proposed method predisposes transformer models to three types of inductive biases . it also fine-tunes the models on three typologically-distant human languages . |
LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs (2024.findings-acl)
Copied to clipboard
| Challenge: | Knowledge Graph (KG) inductive reasoning is widely adopted in various applications. |
| Approach: | They propose a framework for low-resource inductive reasoning using Large Language Models to generate a graph-structural prompt for pre-trained KGs. |
| Outcome: | The proposed framework outperforms previous methods in three-shot, one-shot and zero-shot reasoning tasks. |
On the Role of Model Prior in Real-World Inductive Reasoning (2025.emnlp-main)
Copied to clipboard
| 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. |
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. |
How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases (2023.acl-long)
Copied to clipboard
| Challenge: | a recent study found that pre-training can teach language models to rely on hierarchical syntactic features . aaron ramirez: we find that pretraining on simpler language induces a hierarchic bias . |
| Approach: | They find that pre-training can teach language models to rely on hierarchical syntactic features . authors: this suggests that in cognitively plausible language acquisition settings, models may be more data-efficient . |
| Outcome: | a recent study shows that pre-training can teach language models to rely on hierarchical features . the findings suggest that in plausible language acquisition settings, language models may be more data-efficient than previously thought . |
Instruction Induction: From Few Examples to Natural Language Task Descriptions (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models can perform unseen tasks by conditioning on a few input-output demonstrations, but task inference is implicit and the ability of models to explicitly reason about it remains unexplored. |
| Approach: | They propose an instruction induction challenge in which a model is asked to generate a natural language instruction that fits a set of labeled examples. |
| Outcome: | The proposed model achieves 65.7% of human performance while the original model only reaches 9.8% of human performances. |
Semantic Frame Induction from a Real-World Corpus (2025.acl-srw)
Copied to clipboard
| Challenge: | Existing studies on semantic frame induction have demonstrated that pre-trained language models (PLMs) have led to more accurate results. |
| Approach: | They conduct semantic frame induction using the Colossal Clean Crawled Corpus and assess the applicability of existing frame inducing methods to real-world data. |
| Outcome: | The proposed methods outperform existing methods on real-world data and can induce frames corresponding to novel concepts. |