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    <abstract>In recent years, the rapid advancement of large language models (LLMs) has significantly reshaped the landscape of scientific research. While LLMs have achieved notable success across various domains, their application in specialized fields such as lunar exploration remains underdeveloped, and their full potential in this domain has yet to be fully realized. To address this gap, we introduce Lunar Twins, the first LLMs designed specifically for lunar exploration, along with a collaborative framework that combines both large and small models. Additionally, we present Lunar GenData, a multi-agent collaborative workflow for generating lunar instructions, and establish the first specialized lunar dataset, which integrates real data from the Chang’e lunar missions. Lastly, we developed Lunar Eval, the first comprehensive evaluation suite for assessing the capabilities of LLMs in lunar exploration tasks. Experimental validation demonstrates that our approach not only enhances domain expertise in lunar exploration but also reveals preliminary indications of embodied intelligence potential.</abstract>
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%0 Conference Proceedings
%T Lunar Twins: We Choose to Go to the Moon with Large Language Models
%A Xiao, Xin-Yu
%A Liu, Yalei
%A Liu, Xiangyu
%A Li, Zengrui
%A Yin, Erwei
%A Xia, Qianchen
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F xiao-etal-2025-lunar
%X In recent years, the rapid advancement of large language models (LLMs) has significantly reshaped the landscape of scientific research. While LLMs have achieved notable success across various domains, their application in specialized fields such as lunar exploration remains underdeveloped, and their full potential in this domain has yet to be fully realized. To address this gap, we introduce Lunar Twins, the first LLMs designed specifically for lunar exploration, along with a collaborative framework that combines both large and small models. Additionally, we present Lunar GenData, a multi-agent collaborative workflow for generating lunar instructions, and establish the first specialized lunar dataset, which integrates real data from the Chang’e lunar missions. Lastly, we developed Lunar Eval, the first comprehensive evaluation suite for assessing the capabilities of LLMs in lunar exploration tasks. Experimental validation demonstrates that our approach not only enhances domain expertise in lunar exploration but also reveals preliminary indications of embodied intelligence potential.
%R 10.18653/v1/2025.findings-acl.69
%U https://aclanthology.org/2025.findings-acl.69/
%U https://doi.org/10.18653/v1/2025.findings-acl.69
%P 1325-1339

Markdown (Informal)

[Lunar Twins: We Choose to Go to the Moon with Large Language Models](https://aclanthology.org/2025.findings-acl.69/) (Xiao et al., Findings 2025)

• Lunar Twins: We Choose to Go to the Moon with Large Language Models (Xiao et al., Findings 2025)

ACL

• Xin-Yu Xiao, Yalei Liu, Xiangyu Liu, Zengrui Li, Erwei Yin, and Qianchen Xia. 2025. Lunar Twins: We Choose to Go to the Moon with Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 1325–1339, Vienna, Austria. Association for Computational Linguistics.