@inproceedings{wang-etal-2026-llp,
title = "{LLP}: {LLM}-Based Product Pricing in {E}-commerce",
author = "Wang, Hairu and
You, Sheng and
Zhang, Qingheng and
Xie, Xike and
Han, Shuguang and
Wu, Yuchen and
Huang, Fei and
Chen, Jufeng",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.3/",
pages = "25--42",
ISBN = "979-8-89176-460-6",
abstract = "Setting appropriate prices for second-hand products remains challenging for inexperienced individual sellers on Consumer-to-Consumer (C2C) e-commerce platforms. Existing regression-based approaches rely on static product{--}price mappings, making it difficult to capture subtle condition differences expressed in colloquial product descriptions. Inspired by recent breakthroughs in Large Language Models (LLMs), we present LLP, a retrieval-then-generation framework that generates price suggestions based on dynamically retrieved comparable products. LLP employs bidirectional reasoning to generate high-quality rationales as supervision, and subsequently applies retrieval-aware policy optimization to further improve both reasoning faithfulness and pricing accuracy. At inference time, LLP employs entropy-guided filtering to discard low-confidence price predictions. Extensive evaluations demonstrate the effectiveness of our method. It raises the static adoption rate (SAR) from 40{\%} to 72{\%}, while increasing GMV by 0.7{\%} and CTCVR by 1.4{\%}. LLP is now fully deployed in Xianyu, serving millions of individual sellers daily."
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<abstract>Setting appropriate prices for second-hand products remains challenging for inexperienced individual sellers on Consumer-to-Consumer (C2C) e-commerce platforms. Existing regression-based approaches rely on static product–price mappings, making it difficult to capture subtle condition differences expressed in colloquial product descriptions. Inspired by recent breakthroughs in Large Language Models (LLMs), we present LLP, a retrieval-then-generation framework that generates price suggestions based on dynamically retrieved comparable products. LLP employs bidirectional reasoning to generate high-quality rationales as supervision, and subsequently applies retrieval-aware policy optimization to further improve both reasoning faithfulness and pricing accuracy. At inference time, LLP employs entropy-guided filtering to discard low-confidence price predictions. Extensive evaluations demonstrate the effectiveness of our method. It raises the static adoption rate (SAR) from 40% to 72%, while increasing GMV by 0.7% and CTCVR by 1.4%. LLP is now fully deployed in Xianyu, serving millions of individual sellers daily.</abstract>
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%0 Conference Proceedings
%T LLP: LLM-Based Product Pricing in E-commerce
%A Wang, Hairu
%A You, Sheng
%A Zhang, Qingheng
%A Xie, Xike
%A Han, Shuguang
%A Wu, Yuchen
%A Huang, Fei
%A Chen, Jufeng
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F wang-etal-2026-llp
%X Setting appropriate prices for second-hand products remains challenging for inexperienced individual sellers on Consumer-to-Consumer (C2C) e-commerce platforms. Existing regression-based approaches rely on static product–price mappings, making it difficult to capture subtle condition differences expressed in colloquial product descriptions. Inspired by recent breakthroughs in Large Language Models (LLMs), we present LLP, a retrieval-then-generation framework that generates price suggestions based on dynamically retrieved comparable products. LLP employs bidirectional reasoning to generate high-quality rationales as supervision, and subsequently applies retrieval-aware policy optimization to further improve both reasoning faithfulness and pricing accuracy. At inference time, LLP employs entropy-guided filtering to discard low-confidence price predictions. Extensive evaluations demonstrate the effectiveness of our method. It raises the static adoption rate (SAR) from 40% to 72%, while increasing GMV by 0.7% and CTCVR by 1.4%. LLP is now fully deployed in Xianyu, serving millions of individual sellers daily.
%U https://aclanthology.org/2026.emnlp-industry.3/
%P 25-42
Markdown (Informal)
[LLP: LLM-Based Product Pricing in E-commerce](https://aclanthology.org/2026.emnlp-industry.3/) (Wang et al., EMNLP 2026)
ACL
- Hairu Wang, Sheng You, Qingheng Zhang, Xike Xie, Shuguang Han, Yuchen Wu, Fei Huang, and Jufeng Chen. 2026. LLP: LLM-Based Product Pricing in E-commerce. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 25–42, Budapest, Hungary. Association for Computational Linguistics.