Shopping Reasoning Bench: An Expert-Authored Benchmark for Multi-Turn Conversational Shopping Assistants
The Shopping Reasoning Bench is a new benchmark created by retail domain experts, consisting of 525 missions (232 single-turn, 293 multi-turn) and 10,863 importance-weighted binary rubrics. It evaluates multi-turn reasoning capabilities such as preference refinement, trade-off analysis, and compatibility assessment in conversational shopping assistants. Evaluations of top models (GPT, Claude, Gemini) show overall pass rates of only 57-77%, with significant degradation in multi-turn tasks, highlighting a gap in expert-level advice.
[2606.12608] Shopping Reasoning Bench: An Expert-Authored Benchmark for Multi-Turn Conversational Shopping Assistants
[Submitted on 10 Jun 2026]
Title:Shopping Reasoning Bench: An Expert-Authored Benchmark for Multi-Turn Conversational Shopping Assistants
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Abstract:Conversational shopping assistants now serve hundreds of millions of customers, yet no existing benchmark jointly evaluates the open-ended multi-turn reasoning, domain expertise, and criterion-level quality that real shopping conversations demand. Shopping reasoning is unique among language model applications. Unlike factual question answering or verifiable code generation, it requires balancing subjective preferences, budget constraints, and cross-product trade-offs across multi-turn dialogue, capabilities absent from previous e-commerce and general-purpose benchmarks. We introduce the Shopping Reasoning Bench, an expert-authored benchmark of 525 missions (232 single-turn, 293 multi-turn) with 10863 importance-weighted binary rubrics authored by retail domain experts. These criteria are organized under a taxonomy of five reasoning categories and fifteen subcategories covering diverse demands such as preference refinement, trade-off analysis, and compatibility assessment. An evaluation of nine models across three families (GPT, Claude, Gemini) shows that pass rates reach only 57--77% overall. On multi-turn missions, all models score 13--29 points lower on optional above-and-beyond criteria than on required ones, and performance degrades 4--18 points as conversations progress. These gaps show that current models handle basic shopping assistance but fall short of expert-level advice, making Shopping Reasoning Bench a challenging testbed for future shopping assistant development.
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2606.12608 [cs.CL]
(or arXiv:2606.12608v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.12608
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Seonwoo Min [view email] [v1] Wed, 10 Jun 2026 19:04:09 UTC (339 KB)
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