@inproceedings{otake-etal-2025-bannerbench,
title = "{B}anner{B}ench: Benchmarking Vision Language Models for Multi-Ad Selection with Human Preferences",
author = "Otake, Hiroto and
Zhang, Peinan and
Sakai, Yusuke and
Mita, Masato and
Ouchi, Hiroki and
Watanabe, Taro",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1311/",
pages = "24145--24159",
ISBN = "979-8-89176-335-7",
abstract = "Web banner advertisements, which are placed on websites to guide users to a targeted landing page (LP), are still often selected manually because human preferences are important in selecting which ads to deliver. To automate this process, we propose a new benchmark, BannerBench, to evaluate the human preference-driven banner selection process using vision-language models (VLMs). This benchmark assesses the degree of alignment with human preferences in two tasks: a ranking task and a best-choice task, both using sets of five images derived from a single LP. Our experiments show that VLMs are moderately correlated with human preferences on the ranking task. In the best-choice task, most VLMs perform close to chance level across various prompting strategies. These findings suggest that although VLMs have a basic understanding of human preferences, most of them struggle to pinpoint a single suitable option from many candidates."
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<abstract>Web banner advertisements, which are placed on websites to guide users to a targeted landing page (LP), are still often selected manually because human preferences are important in selecting which ads to deliver. To automate this process, we propose a new benchmark, BannerBench, to evaluate the human preference-driven banner selection process using vision-language models (VLMs). This benchmark assesses the degree of alignment with human preferences in two tasks: a ranking task and a best-choice task, both using sets of five images derived from a single LP. Our experiments show that VLMs are moderately correlated with human preferences on the ranking task. In the best-choice task, most VLMs perform close to chance level across various prompting strategies. These findings suggest that although VLMs have a basic understanding of human preferences, most of them struggle to pinpoint a single suitable option from many candidates.</abstract>
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%0 Conference Proceedings
%T BannerBench: Benchmarking Vision Language Models for Multi-Ad Selection with Human Preferences
%A Otake, Hiroto
%A Zhang, Peinan
%A Sakai, Yusuke
%A Mita, Masato
%A Ouchi, Hiroki
%A Watanabe, Taro
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F otake-etal-2025-bannerbench
%X Web banner advertisements, which are placed on websites to guide users to a targeted landing page (LP), are still often selected manually because human preferences are important in selecting which ads to deliver. To automate this process, we propose a new benchmark, BannerBench, to evaluate the human preference-driven banner selection process using vision-language models (VLMs). This benchmark assesses the degree of alignment with human preferences in two tasks: a ranking task and a best-choice task, both using sets of five images derived from a single LP. Our experiments show that VLMs are moderately correlated with human preferences on the ranking task. In the best-choice task, most VLMs perform close to chance level across various prompting strategies. These findings suggest that although VLMs have a basic understanding of human preferences, most of them struggle to pinpoint a single suitable option from many candidates.
%U https://aclanthology.org/2025.findings-emnlp.1311/
%P 24145-24159
Markdown (Informal)
[BannerBench: Benchmarking Vision Language Models for Multi-Ad Selection with Human Preferences](https://aclanthology.org/2025.findings-emnlp.1311/) (Otake et al., Findings 2025)
ACL