The DOI matches its Crossref record; 19 of the 21 references it cites were found in a registry or an index.
Compositional Questions Do Not Necessitate Multi-hop Reasoning
Open accessAbstract
Multi-hop reading comprehension (RC) questions are challenging because they require reading and reasoning over multiple paragraphs.We argue that it can be difficult to construct large multi-hop RC datasets.For example, even highly compositional questions can be answered with a single hop if they target specific entity types, or the facts needed to answer them are redundant.Our analysis is centered on HOTPOTQA, where we show that single-hop reasoning can solve much more of the dataset than previously thought.We introduce a single-hop BERT-based RC model that achieves 67 F1-comparable to state-of-theart multi-hop models.We also design an evaluation setting where humans are not shown all of the necessary paragraphs for the intended multi-hop reasoning but can still answer over 80% of questions.Together with detailed error analysis, these results suggest there should be an increasing focus on the role of evidence in multi-hop reasoning and possibly even a shift towards information retrieval style evaluations with large and diverse evidence collections.
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“While dense retrievers like Dense Passage Retrieval (DPR) [14] and late-interaction models like ColBERT [15] improve recall on single-hop queries, they often struggle with multi-hop reasoning, where the answer depends on composing information from multiple disjoint documents [20, 6].”
From Replace, Don't Expand: Mitigating Context Dilution in Multi-Hop RAG via Fixed-Budget Evidence Assembly (2025) · cites this paper as [20]
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Cites
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
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Self-citationTriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1147 · Cited by 1,690
Self-citationSemantic Parsing on Freebase from Question-Answer Pairs
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QANet: Combining Local Convolution with Global Self-Attention for Reading ComprehensionPreprint
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Simple and Effective Multi-Paragraph Reading Comprehension
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Self-citationWhat Makes Reading Comprehension Questions Easier?
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DCN+: Mixed Objective and Deep Residual Coattention for Question AnsweringPreprint
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Understanding Dataset Design Choices for Multi-hop Reasoning
Chen, J., & Durrett, G. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1405 · Cited by 82
Research works printed but not linked: 1
- Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2017. Constructing Datasets for Multi-hop Reading Comprehension Across Documents. In TACL.
Cited by
Sorted by citations
Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
Clark, C., Yatskar, M., & Zettlemoyer, L. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1418 · Cited by 381
“Recent concern about dataset bias has led researchers to re-examine many popular datasets, resulting in the discovery of a wide variety of biases (Agrawal et al., 2018; Anand et al., 2018; Min et al., 2019; Schwartz et al., 2017).”
BackgroundSelf-citationPretrained Transformers Improve Out-of-Distribution Robustness
Hendrycks, D., Liu, X., Wallace, E., Dziedzic, A., Krishnan, R., & Song, D. X. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.244 · Cited by 406
Self-citation♫ MuSiQue: Multihop Questions via Single-hop Question Composition
Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00475 · Cited by 283
Evaluating Models’ Local Decision Boundaries via Contrast Sets
Gardner, M., Artzi, Y., Basmov, V., Berant, J., Bogin, B., Chen, S., Dasigi, P., Dua, D., Elazar, Y., Gottumukkala, A., et al. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.117 · Cited by 323
Self-citationConstructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
Ho, X., Duong Nguyen, A.-K., Sugawara, S., & Aizawa, A. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.580 · Cited by 287
“Min et al. (2019) clas-sified the comparison questions in HotpotQA in three types: multi-hop, context-dependent multi-hop, and single-hop.”
BackgroundMethodInfluential
Metadata
- Authors
- Min, S., Wallace, E., Singh, S., Gardner, M., Hajishirzi, H., & Zettlemoyer, L.
- Affiliations
- University of Washington (US)
- Allen Institute for Artificial Intelligence (US)
- University of California, Irvine (US)
- Corresponding author
- Wallace, Eric
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 4249-4257
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2953163841
- Cited by
- 138
- References
- 21
- Citations by year
- 2026: 6 · 2025: 3 · 2024: 3 · 2023: 20 · 2022: 11 · 2021: 38 · 2020: 39 · 2019: 18
- FWCI
- 10.14
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1616112, 1252835, 1562364)
- Allen Institute for Artificial Intelligence
- Samsung
- Office of Naval Research (N00014-17-S-B001, N00014-18, N00014-18-1-2826, N00014, N00014-17)
- Army Research Office (W911NF-16-1-, W911NF)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- HotpotQA, dataset construction, information retrieval evaluation, error analysis
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