WixQA：面向企业检索增强生成的多数据集基准
WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generation
AI总结：
本文提出 WixQA 企业 RAG 基准，包含三类基于 Wix 帮助中心知识库快照的问答数据集并开放基线结果，以支持检索与生成组件的端到端评估。
摘要：
检索增强生成（RAG）是现代问答（QA）系统的基石，能够基于外部知识生成有依据的回答。尽管近期进展由开放域数据集推动，但企业问答系统需要能够反映用户在日常支持场景中提出的具体、特定领域问题的数据集。关键在于，评估端到端 RAG 系统不仅需要包含问答对，还需要包含答案所源自的特定知识库（KB）快照。为满足这一需求，我们推出 WixQA，这是一个基准套件，其问答数据集精确基于已发布的知识库语料，能够对检索和生成组件进行整体评估。WixQA 包含三个不同的问答数据集，均源自 Wix.com 客户支持交互，并基于公开的 Wix 帮助中心知识库快照：（i）WixQA-ExpertWritten，包含200个真实用户查询及专家撰写的多步骤答案；（ii）WixQA-Simulated，包含200个从用户对话中提炼并经专家验证的问答对；（iii）WixQA-Synthetic，包含6,222个由 LLM 生成的问答对，系统地从知识库中的每篇文章生成一对。我们以 MIT 许可证发布知识库快照及数据集，并提供全面的基线结果，为在真实企业环境中评估企业 RAG 系统构建了独特基准。
英文摘要：
Retrieval-Augmented Generation (RAG) is a cornerstone of modern question answering (QA) systems, enabling grounded answers based on external knowledge. Although recent progress has been driven by open-domain datasets, enterprise QA systems need datasets that mirror the concrete, domain-specific issues users raise in day-to-day support scenarios. Critically, evaluating end-to-end RAG systems requires benchmarks comprising not only question--answer pairs but also the specific knowledge base (KB) snapshot from which answers were derived. To address this need, we introduce WixQA, a benchmark suite featuring QA datasets precisely grounded in the released KB corpus, enabling holistic evaluation of retrieval and generation components. WixQA includes three distinct QA datasets derived from Wix.com customer support interactions and grounded in a snapshot of the public Wix Help Center KB: (i) WixQA-ExpertWritten, 200 real user queries with expert-authored, multi-step answers; (ii) WixQA-Simulated, 200 expert-validated QA pairs distilled from user dialogues; and (iii) WixQA-Synthetic, 6,222 LLM-generated QA pairs, with one pair systematically derived from each article in the knowledge base. We release the KB snapshot alongside the datasets under MIT license and provide comprehensive baseline results, forming a unique benchmark for evaluating enterprise RAG systems in realistic enterprise environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
