@inproceedings{hong-etal-2026-exhibitionbench,
title = "{E}xhibition{B}ench: A Multi-Source, Multi-Task Benchmark for Evaluating {LLM}s as Exhibition Curation Assistants",
author = "Hong, Mengze and
Yanjie, Sun and
Zhu, Mengyao and
Zhang, Chen Jason and
Jiang, Di and
Ivanka, Huang Guoqiong and
LI, Haoyang and
Kou, Zhiqiang and
Zhang, Shuning and
Ni, Wangze and
Huang, Enhao and
Lei, Zeyang and
Zeng, Xia and
Wang, Sheng and
Liang, Di",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.228/",
pages = "3814--3825",
ISBN = "979-8-89176-460-6",
abstract = "Cultural reasoning lies at the core of many emerging AI applications, yet existing benchmarks provide limited insight into how well models understand and contextualize cultural artifacts. We introduce ExhibitionBench, the first benchmark that frames exhibition curation as a high-level evaluation testbed, comprising 300 institution-derived exhibition and thematic collection records, 23,658 artifacts, and three interconnected tasks that evaluate complementary aspects of cultural understanding and reasoning, translating them into user-perceivable utilities. Extensive experiments across 29 systems reveal a significant gap between frontier LLMs and expert-level competence, particularly on tasks that require collection-level coherence and thematic understanding. Performance gains from scaling, prompting, and multimodal inputs are limited and highly model-dependent, whereas tailored model optimizations can equip open-weight models with better capabilities that support on-premise deployment. These findings highlight the difficulty of reasoning over cultural collections and provide a practical benchmark for developing more culturally grounded and reliable AI systems. The full project is released at https://github.com/mengze-hong/ExhibitionBench."
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<abstract>Cultural reasoning lies at the core of many emerging AI applications, yet existing benchmarks provide limited insight into how well models understand and contextualize cultural artifacts. We introduce ExhibitionBench, the first benchmark that frames exhibition curation as a high-level evaluation testbed, comprising 300 institution-derived exhibition and thematic collection records, 23,658 artifacts, and three interconnected tasks that evaluate complementary aspects of cultural understanding and reasoning, translating them into user-perceivable utilities. Extensive experiments across 29 systems reveal a significant gap between frontier LLMs and expert-level competence, particularly on tasks that require collection-level coherence and thematic understanding. Performance gains from scaling, prompting, and multimodal inputs are limited and highly model-dependent, whereas tailored model optimizations can equip open-weight models with better capabilities that support on-premise deployment. These findings highlight the difficulty of reasoning over cultural collections and provide a practical benchmark for developing more culturally grounded and reliable AI systems. The full project is released at https://github.com/mengze-hong/ExhibitionBench.</abstract>
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%0 Conference Proceedings
%T ExhibitionBench: A Multi-Source, Multi-Task Benchmark for Evaluating LLMs as Exhibition Curation Assistants
%A Hong, Mengze
%A Yanjie, Sun
%A Zhu, Mengyao
%A Zhang, Chen Jason
%A Jiang, Di
%A Ivanka, Huang Guoqiong
%A LI, Haoyang
%A Kou, Zhiqiang
%A Zhang, Shuning
%A Ni, Wangze
%A Huang, Enhao
%A Lei, Zeyang
%A Zeng, Xia
%A Wang, Sheng
%A Liang, Di
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F hong-etal-2026-exhibitionbench
%X Cultural reasoning lies at the core of many emerging AI applications, yet existing benchmarks provide limited insight into how well models understand and contextualize cultural artifacts. We introduce ExhibitionBench, the first benchmark that frames exhibition curation as a high-level evaluation testbed, comprising 300 institution-derived exhibition and thematic collection records, 23,658 artifacts, and three interconnected tasks that evaluate complementary aspects of cultural understanding and reasoning, translating them into user-perceivable utilities. Extensive experiments across 29 systems reveal a significant gap between frontier LLMs and expert-level competence, particularly on tasks that require collection-level coherence and thematic understanding. Performance gains from scaling, prompting, and multimodal inputs are limited and highly model-dependent, whereas tailored model optimizations can equip open-weight models with better capabilities that support on-premise deployment. These findings highlight the difficulty of reasoning over cultural collections and provide a practical benchmark for developing more culturally grounded and reliable AI systems. The full project is released at https://github.com/mengze-hong/ExhibitionBench.
%U https://aclanthology.org/2026.emnlp-industry.228/
%P 3814-3825
Markdown (Informal)
[ExhibitionBench: A Multi-Source, Multi-Task Benchmark for Evaluating LLMs as Exhibition Curation Assistants](https://aclanthology.org/2026.emnlp-industry.228/) (Hong et al., EMNLP 2026)
ACL
- Mengze Hong, Sun Yanjie, Mengyao Zhu, Chen Jason Zhang, Di Jiang, Huang Guoqiong Ivanka, Haoyang LI, Zhiqiang Kou, Shuning Zhang, Wangze Ni, Enhao Huang, Zeyang Lei, Xia Zeng, Sheng Wang, and Di Liang. 2026. ExhibitionBench: A Multi-Source, Multi-Task Benchmark for Evaluating LLMs as Exhibition Curation Assistants. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 3814–3825, Budapest, Hungary. Association for Computational Linguistics.