Mix-Ecom: Towards Mixed-Type E-Commerce Dialogues with Complex Domain Rules

• URL: http://arxiv.org/abs/2509.23836v1
• Date: Sun, 28 Sep 2025 12:19:27 GMT
• Title: Mix-Ecom: Towards Mixed-Type E-Commerce Dialogues with Complex Domain Rules
• Authors: Chenyu Zhou, Xiaoming Shi, Hui Qiu, Xiawu Zheng, Haitao Leng, Yankai Jiang, Shaoguo Liu, Tingting Gao, Rongrong Ji,
• Abstract summary: This work first introduces a novel corpus, called Mix-ECom, which is constructed based on real-world customer-service dialogues with post-processing to remove user privacy and add CoT process.<n>Specifically, Mix-ECom contains 4,799 samples with multiply dialogue types in each e-commerce dialogue, covering four dialogue types (QA, recommendation, task-oriented dialogue, and chit-chat), three e-commerce task types (pre-sales, logistics, after-sales), and 82 e-commerce rules.<n>Results show that current e-commerce agents lack sufficient capabilities to handle e-commerce dialogues,
• Score: 68.95862698403302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: E-commerce agents contribute greatly to helping users complete their e-commerce needs. To promote further research and application of e-commerce agents, benchmarking frameworks are introduced for evaluating LLM agents in the e-commerce domain. Despite the progress, current benchmarks lack evaluating agents' capability to handle mixed-type e-commerce dialogue and complex domain rules. To address the issue, this work first introduces a novel corpus, termed Mix-ECom, which is constructed based on real-world customer-service dialogues with post-processing to remove user privacy and add CoT process. Specifically, Mix-ECom contains 4,799 samples with multiply dialogue types in each e-commerce dialogue, covering four dialogue types (QA, recommendation, task-oriented dialogue, and chit-chat), three e-commerce task types (pre-sales, logistics, after-sales), and 82 e-commerce rules. Furthermore, this work build baselines on Mix-Ecom and propose a dynamic framework to further improve the performance. Results show that current e-commerce agents lack sufficient capabilities to handle e-commerce dialogues, due to the hallucination cased by complex domain rules. The dataset will be publicly available.

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