CustomerSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators
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Computer Science > Computation and Language
arXiv:2605.08334 (cs)
[Submitted on 8 May 2026 (v1), last revised 29 Jul 2026 (this version, v2)]
Title:CustomerSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators
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Abstract:We present CustomerSim, an environment and benchmark to evaluate the extent to which Multimodal Large Language Models (MLLMs) can simulate realistic, persona-driven customer behavior in chat-based retail environments. While prior work treats user simulation as surface-level dialog generation, we focus on a model's ability to seek information and make decisions that adhere to customer specifications in multiturn, agentic simulations. CustomerSim consists of a human-curated set of 360 personas over five product categories, alongside a suite of metrics measuring consistency between a customer simulator's actions and its specifications and conversational quality. We find several behavioral gaps across five open and closed-source state-of-the-art models. First, while models produce fluent conversations, they display significantly lower lexical diversity than human shoppers, and open-source models overdisclose their criteria in the opening turn. Second, models tend to be persuaded by sales agent tone and drift from persona specifications. Even the strongest closed-source models, Claude Opus 4.8 and GPT-5.6 Sol, achieves <74% alignment with its persona specifications. To address these limitations, we propose UserGRPO, a multi-turn, multi-objective reinforcement learning recipe optimizing both conversational fluency and decision alignment under persona specifications. UserGRPO raises the decision alignment of the baseline model from 0.417 to 0.652, a gain of 23.5 points, without meaningful cost to conversational quality, and these gains transfer to held-out product categories. We further find that stylistic prompting is the only intervention that makes surface form more human-like, yet it nearly halves persona adherence. Through CustomerSim, we provide a testbed for the community to investigate and improve the adherence of user simulators in goal-oriented settings.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.08334 [cs.CL] |
| (or arXiv:2605.08334v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.08334
arXiv-issued DOI via DataCite
|
Submission history
From: Yada Pruksachatkun Ms. [view email][v1] Fri, 8 May 2026 17:59:23 UTC (1,741 KB)
[v2] Wed, 29 Jul 2026 00:50:03 UTC (8,717 KB)
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View a PDF of the paper titled CustomerSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators, by Yada Pruksachatkun and 4 other authors
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