The DOI matches its Crossref record; 30 of the 32 references it cites were found in a registry or an index.
Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
Open accessAbstract
A key limitation in current datasets for multi-hop reasoning is that the required steps for answering the question are mentioned in it explicitly. In this work, we introduce StrategyQA, a question answering (QA) benchmark where the required reasoning steps are implicit in the question, and should be inferred using a strategy. A fundamental challenge in this setup is how to elicit such creative questions from crowdsourcing workers, while covering a broad range of potential strategies. We propose a data collection procedure that combines term-based priming to inspire annotators, careful control over the annotator population, and adversarial filtering for eliminating reasoning shortcuts. Moreover, we annotate each question with (1) a decomposition into reasoning steps for answering it, and (2) Wikipedia paragraphs that contain the answers to each step. Overall, StrategyQA includes 2,780 examples, each consisting of a strategy question, its decomposition, and evidence paragraphs. Analysis shows that questions in StrategyQA are short, topic-diverse, and cover a wide range of strategies. Empirically, we show that humans perform well (87%) on this task, while our best baseline reaches an accuracy of ∼ 66%.
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In the citing paper
“3 . 7% of questions are classified as requiring reasoning and 7 . 3% are multi-step.”
From SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge (2025) · cites this paper as Geva et al., 2021
In the citing paper
“Compared to fine-tuned models, LLMs keep the superiority on most datasets, such as StrategyQA [40] and ARC-C [28].”
From Harnessing the power of llms in practice: a survey on chatGPT and beyond (2024) · cites this paper as [40]
Citations
Cites
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MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.
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The Web as a Knowledge-Base for Answering Complex Questions
Talmor, A., & Berant, J. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1059 · Cited by 520
Self-citationClark, J. H., Choi, E., Collins, M., Garrette, D., Kwiatkowski, T., Nikolaev, V., & Palomaki, J. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00317 · Cited by 477
Welbl, J., Stenetorp, P., & Riedel, S. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.1162/tacl_a_00021 · Cited by 474
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
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Self-citationDROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
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“• DROP (Dua et al., 2019): A large dataset for numerical reasoning over paragraphs.”
BackgroundMethodInfluentialOverview of the third text Retrieval conference (TREC-3)
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Self-citationMulti-hop Reading Comprehension through Question Decomposition and Rescoring
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B reak It Down: A Question Understanding Benchmark
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Unsupervised Question Decomposition for Question Answering
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Self-citationQASC: A Dataset for Question Answering via Sentence Composition
Khot, T., Clark, P., Guerquin, M., Jansen, P., & Sabharwal, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6319 · Cited by 20
Self-citation
Flagged first, then by citations
2 of the 32 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Robertson, 1995, Okapi at TREC-3, 109
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Sorted by citations
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590
“…evaluate PaLM’s performance on the arithmetic datasets GSM8K (Cobbe et al., 2021), SVAMP (Patel et al., 2021), MAWPS (Koncel-Kedziorski et al., 2016), and AQuA (Ling et al., 2017), as well as the commonsense reasoning datasets CommonsenseQA (Talmor et al., 2019) and StrategyQA (Geva et al., 2021).”
Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1800 · Cited by 4,505
“We find that with the same prompts, chain-of-thought prompting improves performance across all three models (LaMDA, GPT-3, and PaLM) for all datasets except CSQA and StrategyQA for GPT-3 (Table 1, Table 4, Table 5).”
InfluentialA Comprehensive Overview of Large Language Models
Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3744746 · Cited by 1,257
Self-Consistency Improves Chain of Thought Reasoning in Language Models
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2203.11171 · Cited by 829
“In addition, we observed that language models can sometimes generate incorrect or nonsensical reasoning paths (e.g., the StrategyQA example in Table 4, the two population numbers are not exactly correct), and further work is needed to better ground models’ rationale generations.”
BackgroundMethodInfluentialRetrieval-Augmented Generation for Large Language Models: A Survey
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., & Wang, H. (2023) · arXiv · doi:10.48550/arxiv.2312.10997 · Cited by 876
Metadata
- Authors
- Geva, M., Khashabi, D., Segal, E., Khot, T., Roth, D., & Berant, J.
- Affiliations
- Tel Aviv University (IL)
- Allen Institute for Artificial Intelligence (US)
- University of Pennsylvania (US)
- Corresponding author
- Geva, Mor
- Khashabi, Daniel
- Segal, Elad
- Khot, Tushar
- Roth, Dan
- Berant, Jonathan
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press
- Published
- 2021
- Volume
- 9
- Pages
- 346-361
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W3159959439
- Cited by
- 242
- References
- 32
- Citations by year
- 2026: 77 · 2025: 71 · 2024: 38 · 2023: 42 · 2022: 6 · 2021: 8
- FWCI
- 8.27
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Transactions of the Association for Computational Linguistics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- question answering benchmark, multi-hop reasoning, StrategyQA, data collection, human performance
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