@inproceedings{lin-etal-2025-llm,
title = "On {LLM}-Based Scientific Inductive Reasoning Beyond Equations",
author = "Lin, Brian S. and
Yuan, Jiaxin and
Zhou, Zihan and
Wang, Shouli and
Wang, Shuo and
Kong, Cunliang and
Shi, Qi and
Li, Yuxuan and
Yang, Liner and
Liu, Zhiyuan and
Sun, Maosong",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.476/",
pages = "9382--9405",
ISBN = "979-8-89176-332-6",
abstract = "As large language models (LLMs) increasingly exhibit human-like capabilities, a fundamental question emerges: How can we enable LLMs to learn the underlying patterns from limited examples in entirely novel environments and apply them effectively? This question is central to the ability of LLMs in inductive reasoning. Existing research on LLM-based inductive reasoning can be broadly categorized based on whether the underlying rules are expressible via explicit mathematical equations. However, many recent studies in the beyond-equations category have emphasized rule design without grounding them in specific scenarios. Inspired by the parallels between inductive reasoning and human scientific discovery, we propose the task of LLM-Based Scientific Inductive Reasoning Beyond Equations and introduce a new benchmark, SIRBench-V1, to evaluate the inductive reasoning abilities of LLMs in scientific settings. Our experimental results show that current LLMs still struggle with this task, underscoring its difficulty and the need for further advancement in this area."
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<abstract>As large language models (LLMs) increasingly exhibit human-like capabilities, a fundamental question emerges: How can we enable LLMs to learn the underlying patterns from limited examples in entirely novel environments and apply them effectively? This question is central to the ability of LLMs in inductive reasoning. Existing research on LLM-based inductive reasoning can be broadly categorized based on whether the underlying rules are expressible via explicit mathematical equations. However, many recent studies in the beyond-equations category have emphasized rule design without grounding them in specific scenarios. Inspired by the parallels between inductive reasoning and human scientific discovery, we propose the task of LLM-Based Scientific Inductive Reasoning Beyond Equations and introduce a new benchmark, SIRBench-V1, to evaluate the inductive reasoning abilities of LLMs in scientific settings. Our experimental results show that current LLMs still struggle with this task, underscoring its difficulty and the need for further advancement in this area.</abstract>
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%0 Conference Proceedings
%T On LLM-Based Scientific Inductive Reasoning Beyond Equations
%A Lin, Brian S.
%A Yuan, Jiaxin
%A Zhou, Zihan
%A Wang, Shouli
%A Wang, Shuo
%A Kong, Cunliang
%A Shi, Qi
%A Li, Yuxuan
%A Yang, Liner
%A Liu, Zhiyuan
%A Sun, Maosong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F lin-etal-2025-llm
%X As large language models (LLMs) increasingly exhibit human-like capabilities, a fundamental question emerges: How can we enable LLMs to learn the underlying patterns from limited examples in entirely novel environments and apply them effectively? This question is central to the ability of LLMs in inductive reasoning. Existing research on LLM-based inductive reasoning can be broadly categorized based on whether the underlying rules are expressible via explicit mathematical equations. However, many recent studies in the beyond-equations category have emphasized rule design without grounding them in specific scenarios. Inspired by the parallels between inductive reasoning and human scientific discovery, we propose the task of LLM-Based Scientific Inductive Reasoning Beyond Equations and introduce a new benchmark, SIRBench-V1, to evaluate the inductive reasoning abilities of LLMs in scientific settings. Our experimental results show that current LLMs still struggle with this task, underscoring its difficulty and the need for further advancement in this area.
%U https://aclanthology.org/2025.emnlp-main.476/
%P 9382-9405
Markdown (Informal)
[On LLM-Based Scientific Inductive Reasoning Beyond Equations](https://aclanthology.org/2025.emnlp-main.476/) (Lin et al., EMNLP 2025)
ACL
- Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, and Maosong Sun. 2025. On LLM-Based Scientific Inductive Reasoning Beyond Equations. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 9382–9405, Suzhou, China. Association for Computational Linguistics.