@inproceedings{hong-etal-2026-uxbench,
title = "{UXB}ench: Benchmarking User Experience in {AI} Assistants",
author = "Hong, Mengze and
Zeng, Xia and
Lei, Zeyang and
Wang, Sheng and
Zhang, Chen Jason and
Jiang, Di and
Fu, Taiming and
Huang, Jinfeng and
Liu, Mengqiao and
Chang, Qinghe and
Zou, Haosheng and
Zhou, Qiongyi and
He, Sijun and
Chen, Xiaoshuai and
Peng, Minlong and
Liang, Di",
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.1683/",
pages = "38299--38316",
ISBN = "979-8-89176-459-0",
abstract = "As AI assistants serve millions of users daily, evaluating user experience (UX) beyond general model capability has become increasingly important. We present UXBench, the first user-centric benchmark grounded in real user feedback signals for evaluating preference alignment and dialogue generation. The benchmark consists of three interconnected tasks, UX Judge, UX Eval, and UX Recovery, with 7,400 test instances extracted from over 70K interaction logs of a mainstream Chinese AI assistant. The dataset closely reflects real user distributions, covering 8 scenarios, 83 domains, and diverse failure patterns that pose severe challenges. Extensive experiments on 26 frontier language models provide novel insights into how well models perceive user experience and how improvements in model capability contribute to better dialogue engagement. Through analyses of model behavior and performance gaps, we document six important findings, demonstrating that user feedback prediction is a learnable capability and revealing different aspects that influence user experience. UXBench establishes a new evaluation landscape and calls for greater attention to tailored UX optimization, contributing toward a user-centric scaling law for the development of successful AI assistants. The full project is released at https://github.com/mengze-hong/UXBench."
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<abstract>As AI assistants serve millions of users daily, evaluating user experience (UX) beyond general model capability has become increasingly important. We present UXBench, the first user-centric benchmark grounded in real user feedback signals for evaluating preference alignment and dialogue generation. The benchmark consists of three interconnected tasks, UX Judge, UX Eval, and UX Recovery, with 7,400 test instances extracted from over 70K interaction logs of a mainstream Chinese AI assistant. The dataset closely reflects real user distributions, covering 8 scenarios, 83 domains, and diverse failure patterns that pose severe challenges. Extensive experiments on 26 frontier language models provide novel insights into how well models perceive user experience and how improvements in model capability contribute to better dialogue engagement. Through analyses of model behavior and performance gaps, we document six important findings, demonstrating that user feedback prediction is a learnable capability and revealing different aspects that influence user experience. UXBench establishes a new evaluation landscape and calls for greater attention to tailored UX optimization, contributing toward a user-centric scaling law for the development of successful AI assistants. The full project is released at https://github.com/mengze-hong/UXBench.</abstract>
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%0 Conference Proceedings
%T UXBench: Benchmarking User Experience in AI Assistants
%A Hong, Mengze
%A Zeng, Xia
%A Lei, Zeyang
%A Wang, Sheng
%A Zhang, Chen Jason
%A Jiang, Di
%A Fu, Taiming
%A Huang, Jinfeng
%A Liu, Mengqiao
%A Chang, Qinghe
%A Zou, Haosheng
%A Zhou, Qiongyi
%A He, Sijun
%A Chen, Xiaoshuai
%A Peng, Minlong
%A Liang, Di
%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 hong-etal-2026-uxbench
%X As AI assistants serve millions of users daily, evaluating user experience (UX) beyond general model capability has become increasingly important. We present UXBench, the first user-centric benchmark grounded in real user feedback signals for evaluating preference alignment and dialogue generation. The benchmark consists of three interconnected tasks, UX Judge, UX Eval, and UX Recovery, with 7,400 test instances extracted from over 70K interaction logs of a mainstream Chinese AI assistant. The dataset closely reflects real user distributions, covering 8 scenarios, 83 domains, and diverse failure patterns that pose severe challenges. Extensive experiments on 26 frontier language models provide novel insights into how well models perceive user experience and how improvements in model capability contribute to better dialogue engagement. Through analyses of model behavior and performance gaps, we document six important findings, demonstrating that user feedback prediction is a learnable capability and revealing different aspects that influence user experience. UXBench establishes a new evaluation landscape and calls for greater attention to tailored UX optimization, contributing toward a user-centric scaling law for the development of successful AI assistants. The full project is released at https://github.com/mengze-hong/UXBench.
%U https://aclanthology.org/2026.emnlp-main.1683/
%P 38299-38316
Markdown (Informal)
[UXBench: Benchmarking User Experience in AI Assistants](https://aclanthology.org/2026.emnlp-main.1683/) (Hong et al., EMNLP 2026)
ACL
- Mengze Hong, Xia Zeng, Zeyang Lei, Sheng Wang, Chen Jason Zhang, Di Jiang, Taiming Fu, Jinfeng Huang, Mengqiao Liu, Qinghe Chang, Haosheng Zou, Qiongyi Zhou, Sijun He, Xiaoshuai Chen, Minlong Peng, and Di Liang. 2026. UXBench: Benchmarking User Experience in AI Assistants. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 38299–38316, Budapest, Hungary. Association for Computational Linguistics.