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Shopping Reasoning Bench: An Expert-Authored Benchmark for Multi-Turn Conversational Shopping Assistants

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arXiv:2606. 12608v1 Announce Type: new 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.

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Authors: Shuxian Fan, Seonwoo Min, Youna Hu, Botao Xia, Jayakrishnan Unnikrishnan, Rowan Musselmann, Yifan Gao, Qingyu Yin, Priyanka Nigam, Bing Yin
Released: 2026-06-12 (27d ago)
Topic: Related to current project
Relation to my work: Read next because Shopping Reasoning Bench: An Expert-Authored Benchmark for Multi-Turn Conversational Shopping Assistants overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, under, eval, assistant, rate, test, language, model. Source: arxiv cs.CL (NLP).
arXiv: 2606.12608

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