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

arXiv: https://arxiv.org/abs/2509.23836 · HTML: https://mlsanity.com/paper/2509.23836/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 98 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://mlsanity.com/paper/2305.04091/) (2023)
- [Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://mlsanity.com/paper/2405.02957/) (2024)
- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [WebCPM: Interactive Web Search for Chinese Long-form Question Answering](https://mlsanity.com/paper/2305.06849/) (2023)
- [EduAgent: Generative Student Agents in Learning](https://mlsanity.com/paper/2404.07963/) (2024)
- [ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues?](https://mlsanity.com/paper/2507.05639/) (2025)
- [Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branching](https://mlsanity.com/paper/2502.00969/) (2025)
- [EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association](https://mlsanity.com/paper/2505.15196/) (2025)
- [Investigating LLM Applications in E-Commerce](https://mlsanity.com/paper/2408.12779/) (2024)
- [Cite Before You Speak: Enhancing Context-Response Grounding in E-commerce Conversational LLM-Agents](https://mlsanity.com/paper/2503.04830/) (2025)
- [Evaluating Recabilities of Foundation Models: A Multi-Domain, Multi-Dataset Benchmark](https://mlsanity.com/paper/2508.21354/) (2025)
- [ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT](https://mlsanity.com/paper/2506.04929/) (2025)

## Built on by (most cited first, 3 of 3)

- [Flow caching for autoregressive video generation](https://mlsanity.com/paper/2602.10825/) (2026)
- [EComStage: Stage-wise and Orientation-specific Benchmarking for Large Language Models in E-commerce](https://mlsanity.com/paper/2601.02752/) (2026)
- [What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents](https://mlsanity.com/paper/2604.04468/) (2026)

## Abstract (arXiv)

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.