# WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2505.08643 · HTML: https://mlsanity.com/paper/2505.08643/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 13 May 2025.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 14 of 14)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021)
- [JudgeBench: A Benchmark for Evaluating LLM-based Judges](https://mlsanity.com/paper/2410.12784/) (2024)
- [FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research](https://mlsanity.com/paper/2405.13576/) (2024)
- [doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset](https://mlsanity.com/paper/2011.06623/) (2020)
- [FinTextQA: A Dataset for Long-form Financial Question Answering](https://mlsanity.com/paper/2405.09980/) (2024)
- [MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents](https://mlsanity.com/paper/2109.12595/) (2021)
- [AmazonQA: A Review-Based Question Answering Task](https://mlsanity.com/paper/1908.04364/) (2019)
- [DoQA -- Accessing Domain-Specific FAQs via Conversational QA](https://mlsanity.com/paper/2005.01328/) (2020)
- [Applying Deep Learning to Answer Selection: A Study and An Open Task](https://mlsanity.com/paper/1508.01585/) (2015)
- [Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check](https://mlsanity.com/paper/2403.18243/) (2024)

## Built on by (most cited first, 10 of 10)

- [Stream RAG: Instant and Accurate Spoken Dialogue Systems with Streaming Tool Usage](https://mlsanity.com/paper/2510.02044/) (2025)
- [Corpus2Skill: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG](https://mlsanity.com/paper/2604.14572/) (2026)
- [Dr. Bench: A Multidimensional Evaluation for Deep Research Agents, from Answers to Reports](https://mlsanity.com/paper/2510.02190/) (2025)
- [Structured RAG for Answering Aggregative Questions](https://mlsanity.com/paper/2511.08505/) (2025)
- [Telenor Nordics Customer Service self-help corpus](https://mlsanity.com/paper/2605.26891/) (2026)
- [ChurnBench: A Drift-Aware Benchmark Demonstrating That Refresh Scheduling, Not Cache Age, Governs Staleness in Agentic AI](https://mlsanity.com/paper/2609.11515/) (2026)
- [Can One Adapted Model Do It All? Fine-Tuning Strategy Selection for Customer Support LLMs](https://mlsanity.com/paper/2609.27262/) (2026)
- [Follow the Entities: A Corpus Map for Agentic Search](https://mlsanity.com/paper/2609.37226/) (2026)
- [RIT-RAG: Navigating Document Corpora with Retrieval-Induced Trees](https://mlsanity.com/paper/2610.11370/) (2026)
- [CoverSynth: Coverage-Guided Generation of Retrieval Evaluation Sets](https://mlsanity.com/paper/2610.15409/) (2026)

## Abstract (arXiv)

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.