WikiHowQA: A Comprehensive Benchmark for Multi-Document Non-Factoid Question Answering
Valeria Bolotova-Baranova, Vladislav Blinov, Sofya Filippova, Falk Scholer, Mark Sanderson
Abstract
Answering non-factoid questions (NFQA) is a challenging task, requiring passage-level answers that are difficult to construct and evaluate. Search engines may provide a summary of a single web page, but many questions require reasoning across multiple documents. Meanwhile, modern models can generate highly coherent and fluent, but often factually incorrect answers that can deceive even non-expert humans. There is a critical need for highquality resources for multi-document NFQA (MD-NFQA) to train new models and evaluate answers' grounding and factual consistency in relation to supporting documents. To bridge this gap, we present WIKIHOWQA, 1 a new multi-document NFQA benchmark built on WikiHow, a website dedicated to answering "how-to" questions. The benchmark includes 11,746 human-written answers along with 74,527 supporting documents. We describe the unique challenges of the resource, provide strong baselines, and propose a novel human evaluation framework that utilizes highlighted relevant supporting passages to mitigate issues such as assessor unfamiliarity with the question topic. All code and data, including the automatic code for preparing the human evaluation, are publicly available.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 8352d975-e75f-4f65-b6c7-ffdb39853b69Cited by top-tier papers10
- Explainability for Transparent Conversational Information-SeekingWeronika Lajewska, Damiano Spina, Johanne R. Trippas, Krisztian BalogSIGIR 2024 · 17 citations
- UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and GranularitiesWoongyeong Yeo, Kangsan Kim, Soyeong Jeong, Jinheon Baek et al.ACL 2026 · 14 citations
- Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile AgentsShihan Deng, Weikai Xu, Hongda Sun, Wei Liu et al.ACL 2024 · 10 citations
- FinTextQA: A Dataset for Long-form Financial Question AnsweringJian Chen, Peilin Zhou, Yining Hua, Loh Xin et al.ACL 2024 · 7 citations
- PAGED: A Benchmark for Procedural Graphs Extraction from DocumentsWeihong Du, Wenrui Liao, Hongru Liang, Wenqiang LeiACL 2024 · 4 citations
Builds on12
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
- Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksPatrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni et al.NeurIPS 2020 · 19,162 citations
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionMike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad et al.ACL 2020 · 1,224 citations
- Evaluating the Factual Consistency of Abstractive Text SummarizationWojciech Kryscinski, Bryan McCann, Caiming Xiong, Richard SocherEMNLP 2020 · 67 citations
- Intrinsic Evaluation of Summarization DatasetsRishi Bommasani, Claire CardieEMNLP 2020 · 52 citations
Related papers
- WikiWhy: Answering and Explaining Cause-and-Effect QuestionsMatthew Ho, Aditya Sharma, Justin Chang, Michael Saxon et al.ICLR 2023 · 8 citations
- PhantomWiki: On-Demand Datasets for Reasoning and Retrieval EvaluationAlbert Gong, Kamile Stankeviciute, Chao Wan, Anmol Kabra et al.ICML 2025
- CofCA: A STEP-WISE Counterfactual Multi-hop QA benchmarkJian Wu, Linyi Yang, Zhen Wang, Manabu Okumura et al.ICLR 2025
- Learning to Ground Instructional Articles in Videos through NarrationsEffrosyni Mavroudi, Triantafyllos Afouras, Lorenzo TorresaniICCV 2023 · 28 citations
- ASQA: Factoid Questions Meet Long-Form AnswersIvan Stelmakh, Yi Luan, Bhuwan Dhingra, Ming-Wei ChangEMNLP 2022 · 51 citations
