@inproceedings{hu-etal-2025-text2world,
title = "{T}ext2{W}orld: Benchmarking Large Language Models for Symbolic World Model Generation",
author = "Hu, Mengkang and
Chen, Tianxing and
Zou, Yude and
Lei, Yuheng and
Chen, Qiguang and
Li, Ming and
Mu, Yao and
Zhang, Hongyuan and
Shao, Wenqi and
Luo, Ping",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1337/",
doi = "10.18653/v1/2025.findings-acl.1337",
pages = "26043--26066",
ISBN = "979-8-89176-256-5",
abstract = "Recently, there has been growing interest in leveraging large language models (LLMs) to generate symbolic world models from textual descriptions. Although LLMs have been extensively explored in the context of world modeling, prior studies encountered several challenges, including evaluation randomness, dependence on indirect metrics, and a limited domain scope. To address these limitations, we introduce a novel benchmark, Text2World, based on planning domain definition language (PDDL), featuring hundreds of diverse domains and employing multi-criteria, execution-based metrics for a more robust evaluation. We benchmark current LLMs using Text2World and find that reasoning models trained with large-scale reinforcement learning outperform others. However, even the best-performing model still demonstrates limited capabilities in world modeling. Building on these insights, we examine several promising strategies to enhance the world modeling capabilities of LLMs, including test-time scaling, agent training, and more. We hope that Text2World can serve as a crucial resource, laying the groundwork for future research in leveraging LLMs as world models."
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%0 Conference Proceedings
%T Text2World: Benchmarking Large Language Models for Symbolic World Model Generation
%A Hu, Mengkang
%A Chen, Tianxing
%A Zou, Yude
%A Lei, Yuheng
%A Chen, Qiguang
%A Li, Ming
%A Mu, Yao
%A Zhang, Hongyuan
%A Shao, Wenqi
%A Luo, Ping
%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 hu-etal-2025-text2world
%X Recently, there has been growing interest in leveraging large language models (LLMs) to generate symbolic world models from textual descriptions. Although LLMs have been extensively explored in the context of world modeling, prior studies encountered several challenges, including evaluation randomness, dependence on indirect metrics, and a limited domain scope. To address these limitations, we introduce a novel benchmark, Text2World, based on planning domain definition language (PDDL), featuring hundreds of diverse domains and employing multi-criteria, execution-based metrics for a more robust evaluation. We benchmark current LLMs using Text2World and find that reasoning models trained with large-scale reinforcement learning outperform others. However, even the best-performing model still demonstrates limited capabilities in world modeling. Building on these insights, we examine several promising strategies to enhance the world modeling capabilities of LLMs, including test-time scaling, agent training, and more. We hope that Text2World can serve as a crucial resource, laying the groundwork for future research in leveraging LLMs as world models.
%R 10.18653/v1/2025.findings-acl.1337
%U https://aclanthology.org/2025.findings-acl.1337/
%U https://doi.org/10.18653/v1/2025.findings-acl.1337
%P 26043-26066
Markdown (Informal)
[Text2World: Benchmarking Large Language Models for Symbolic World Model Generation](https://aclanthology.org/2025.findings-acl.1337/) (Hu et al., Findings 2025)
ACL
- Mengkang Hu, Tianxing Chen, Yude Zou, Yuheng Lei, Qiguang Chen, Ming Li, Yao Mu, Hongyuan Zhang, Wenqi Shao, and Ping Luo. 2025. Text2World: Benchmarking Large Language Models for Symbolic World Model Generation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 26043–26066, Vienna, Austria. Association for Computational Linguistics.