@inproceedings{ma-etal-2026-fpco,
title = "{FPCO}-Dialog: A Multi-Turn False-Premise Benchmark for Correction and Cooperation in Vision-Language Models",
author = "Ma, Jiayuan and
Lu, Yuqi and
Guo, Weiyang and
Wang, Chenrui and
Shu, Junyi and
Liu, Xuebo and
Zhang, Min and
Li, Jing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.286/",
pages = "6547--6570",
ISBN = "979-8-89176-459-0",
abstract = "Vision-language models (VLMs) are increasingly deployed in multi-turn settings where users may describe visual content with incorrect assumptions. Yet existing evaluations rarely isolate how models respond when the same visually grounded false premise persists across dialogue turns. We introduce FPCO-Dialog, a benchmark for evaluating correction and cooperation behavior in VLMs under repeated false premises. FPCO-Dialog contains 1,080 images and 10,800 question turns, stratified by visual complexity, object category, and false-premise class, and uses a 10-turn protocol in which a correct dialogue prefix is followed by repeated false-premise referring expressions. We evaluate 20 commercial and open-source VLMs with a model-agnostic protocol and CorrTP@K, a correction-rate metric over false-premise turns, scored by two independent detectors. FPCO-Dialog reveals substantial and persistent cross-model differences in aggregate correction tendency, model-specific turn-wise dynamics, and systematic variation across false-premise types under the benchmark{'}s substitution distribution. The dataset, evaluation protocol, model outputs, detector labels, and code are available at https://github.com/lab-klc/FPCO-Dialog."
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<abstract>Vision-language models (VLMs) are increasingly deployed in multi-turn settings where users may describe visual content with incorrect assumptions. Yet existing evaluations rarely isolate how models respond when the same visually grounded false premise persists across dialogue turns. We introduce FPCO-Dialog, a benchmark for evaluating correction and cooperation behavior in VLMs under repeated false premises. FPCO-Dialog contains 1,080 images and 10,800 question turns, stratified by visual complexity, object category, and false-premise class, and uses a 10-turn protocol in which a correct dialogue prefix is followed by repeated false-premise referring expressions. We evaluate 20 commercial and open-source VLMs with a model-agnostic protocol and CorrTP@K, a correction-rate metric over false-premise turns, scored by two independent detectors. FPCO-Dialog reveals substantial and persistent cross-model differences in aggregate correction tendency, model-specific turn-wise dynamics, and systematic variation across false-premise types under the benchmark’s substitution distribution. The dataset, evaluation protocol, model outputs, detector labels, and code are available at https://github.com/lab-klc/FPCO-Dialog.</abstract>
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%0 Conference Proceedings
%T FPCO-Dialog: A Multi-Turn False-Premise Benchmark for Correction and Cooperation in Vision-Language Models
%A Ma, Jiayuan
%A Lu, Yuqi
%A Guo, Weiyang
%A Wang, Chenrui
%A Shu, Junyi
%A Liu, Xuebo
%A Zhang, Min
%A Li, Jing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ma-etal-2026-fpco
%X Vision-language models (VLMs) are increasingly deployed in multi-turn settings where users may describe visual content with incorrect assumptions. Yet existing evaluations rarely isolate how models respond when the same visually grounded false premise persists across dialogue turns. We introduce FPCO-Dialog, a benchmark for evaluating correction and cooperation behavior in VLMs under repeated false premises. FPCO-Dialog contains 1,080 images and 10,800 question turns, stratified by visual complexity, object category, and false-premise class, and uses a 10-turn protocol in which a correct dialogue prefix is followed by repeated false-premise referring expressions. We evaluate 20 commercial and open-source VLMs with a model-agnostic protocol and CorrTP@K, a correction-rate metric over false-premise turns, scored by two independent detectors. FPCO-Dialog reveals substantial and persistent cross-model differences in aggregate correction tendency, model-specific turn-wise dynamics, and systematic variation across false-premise types under the benchmark’s substitution distribution. The dataset, evaluation protocol, model outputs, detector labels, and code are available at https://github.com/lab-klc/FPCO-Dialog.
%U https://aclanthology.org/2026.emnlp-main.286/
%P 6547-6570
Markdown (Informal)
[FPCO-Dialog: A Multi-Turn False-Premise Benchmark for Correction and Cooperation in Vision-Language Models](https://aclanthology.org/2026.emnlp-main.286/) (Ma et al., EMNLP 2026)
ACL
- Jiayuan Ma, Yuqi Lu, Weiyang Guo, Chenrui Wang, Junyi Shu, Xuebo Liu, Min Zhang, and Jing Li. 2026. FPCO-Dialog: A Multi-Turn False-Premise Benchmark for Correction and Cooperation in Vision-Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 6547–6570, Budapest, Hungary. Association for Computational Linguistics.