@inproceedings{su-etal-2026-sell,
title = "Sell More, Play Less: Benchmarking {LLM} Realistic Selling Skill",
author = "Su, Xuanbo and
Hu, Wenhao and
Zhan, Le and
Xie, Yuting and
Lyu, Kailin and
Chen, Kaijie and
Li, Ziwei and
Wang, Yeqiang and
Su, Haibo and
Chen, Yunzhang and
Huang, Ling",
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.974/",
pages = "22456--22478",
ISBN = "979-8-89176-459-0",
abstract = "Sales dialogues require multi-turn, goal-directed persuasion under asymmetric incentives, which makes them a challenging setting for large language models (LLMs). Yet existing dialogue benchmarks rarely measure deal progression and outcomes. We introduce SalesLLM benchmark, a bilingual (ZH/EN) benchmark derived from realistic applications covering Financial Services and Consumer Goods, built from 30,074 scripted configurations and 1,805 curated multi-turn scenarios with controllable difficulty and personas. We propose a fully automatic evaluation pipeline that combines (i) an LLM judge for sales-process progress, and (ii) fine-tuned BERT classifiers for end-of-dialogue buying intent. To improve simulation fidelity, we train a user model, CustomerLM, with SFT and DPO on 8,000+ crowdworker-involved sales conversations, reducing role inversion from 17.44{\%} (GPT-4o) to 8.8{\%}. SalesLLM benchmark scores correlate strongly with human ratings (average Pearson r = 0.86; inter-annotator agreement Krippendorff{'}s {\ensuremath{\alpha}} = 0.86, 500 annotations per model). Across 15 mainstream LLMs, in Chinese the strongest models are competitive with typical (junior-to-intermediate) human salespeople {---} not with sales experts {---} while weaker ones fall below this baseline, and cross-lingual consistency remains poor. SalesLLM benchmark serves as a scalable benchmark for outcome-oriented sales agents. Our code and data are released at https://github.com/Bairong-Xdynamics/Benchmarking-LLM-Realistic-Selling-Skill"
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<abstract>Sales dialogues require multi-turn, goal-directed persuasion under asymmetric incentives, which makes them a challenging setting for large language models (LLMs). Yet existing dialogue benchmarks rarely measure deal progression and outcomes. We introduce SalesLLM benchmark, a bilingual (ZH/EN) benchmark derived from realistic applications covering Financial Services and Consumer Goods, built from 30,074 scripted configurations and 1,805 curated multi-turn scenarios with controllable difficulty and personas. We propose a fully automatic evaluation pipeline that combines (i) an LLM judge for sales-process progress, and (ii) fine-tuned BERT classifiers for end-of-dialogue buying intent. To improve simulation fidelity, we train a user model, CustomerLM, with SFT and DPO on 8,000+ crowdworker-involved sales conversations, reducing role inversion from 17.44% (GPT-4o) to 8.8%. SalesLLM benchmark scores correlate strongly with human ratings (average Pearson r = 0.86; inter-annotator agreement Krippendorff’s \ensuremathα = 0.86, 500 annotations per model). Across 15 mainstream LLMs, in Chinese the strongest models are competitive with typical (junior-to-intermediate) human salespeople — not with sales experts — while weaker ones fall below this baseline, and cross-lingual consistency remains poor. SalesLLM benchmark serves as a scalable benchmark for outcome-oriented sales agents. Our code and data are released at https://github.com/Bairong-Xdynamics/Benchmarking-LLM-Realistic-Selling-Skill</abstract>
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%0 Conference Proceedings
%T Sell More, Play Less: Benchmarking LLM Realistic Selling Skill
%A Su, Xuanbo
%A Hu, Wenhao
%A Zhan, Le
%A Xie, Yuting
%A Lyu, Kailin
%A Chen, Kaijie
%A Li, Ziwei
%A Wang, Yeqiang
%A Su, Haibo
%A Chen, Yunzhang
%A Huang, Ling
%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 su-etal-2026-sell
%X Sales dialogues require multi-turn, goal-directed persuasion under asymmetric incentives, which makes them a challenging setting for large language models (LLMs). Yet existing dialogue benchmarks rarely measure deal progression and outcomes. We introduce SalesLLM benchmark, a bilingual (ZH/EN) benchmark derived from realistic applications covering Financial Services and Consumer Goods, built from 30,074 scripted configurations and 1,805 curated multi-turn scenarios with controllable difficulty and personas. We propose a fully automatic evaluation pipeline that combines (i) an LLM judge for sales-process progress, and (ii) fine-tuned BERT classifiers for end-of-dialogue buying intent. To improve simulation fidelity, we train a user model, CustomerLM, with SFT and DPO on 8,000+ crowdworker-involved sales conversations, reducing role inversion from 17.44% (GPT-4o) to 8.8%. SalesLLM benchmark scores correlate strongly with human ratings (average Pearson r = 0.86; inter-annotator agreement Krippendorff’s \ensuremathα = 0.86, 500 annotations per model). Across 15 mainstream LLMs, in Chinese the strongest models are competitive with typical (junior-to-intermediate) human salespeople — not with sales experts — while weaker ones fall below this baseline, and cross-lingual consistency remains poor. SalesLLM benchmark serves as a scalable benchmark for outcome-oriented sales agents. Our code and data are released at https://github.com/Bairong-Xdynamics/Benchmarking-LLM-Realistic-Selling-Skill
%U https://aclanthology.org/2026.emnlp-main.974/
%P 22456-22478
Markdown (Informal)
[Sell More, Play Less: Benchmarking LLM Realistic Selling Skill](https://aclanthology.org/2026.emnlp-main.974/) (Su et al., EMNLP 2026)
ACL
- Xuanbo Su, Wenhao Hu, Le Zhan, Yuting Xie, Kailin Lyu, Kaijie Chen, Ziwei Li, Yeqiang Wang, Haibo Su, Yunzhang Chen, and Ling Huang. 2026. Sell More, Play Less: Benchmarking LLM Realistic Selling Skill. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 22456–22478, Budapest, Hungary. Association for Computational Linguistics.