The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
ASQA: Factoid Questions Meet Long-Form Answers
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“We compile three datasets that cover different types of questions and corpora-ASQA (Stelmakh et al., 2022), QAMPARI (Rubin et al., 2022), and ELI5 (Fan et al., 2019)-as shown in Table 1.”
From Enabling Large Language Models to Generate Text with Citations (2023) · cites this paper as Stelmakh et al., 2022
In the citing paper
“For ASQA, Stelmakh et al. (2022) also propose a QA-based evaluation which we found to be not as stable, and thus we do not report it in our paper.”
From Enabling Large Language Models to Generate Text with Citations (2023) · cites this paper as Stelmakh et al. (2022)
In the citing paper
“ASQA (Stelmakh et al., 2022) is a long-form factoid QA dataset characterized by inherently ambiguous questions that require multiple short answers to encapsulate different viewpoints.”
From Learning Fine-Grained Grounded Citations for Attributed Large Language Models (2024) · cites this paper as Stelmakh et al., 2022
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Citations
Cites
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Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575
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Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.11401 · Cited by 3,101
Know What You Don't Know: Unanswerable Questions for SQuAD
Rajpurkar, P., Jia, R., & Liang, P. (2018) · arXiv · doi:10.48550/arxiv.1806.03822 · Cited by 2,242
Know What You Don’t Know: Unanswerable Questions for SQuAD
Rajpurkar, P., Jia, R., & Liang, P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p18-2124 · Cited by 2,240
Natural Questions: A benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00276 · Cited by 2,069
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Cited by
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3703155 · Cited by 4,688
Active Retrieval Augmented Generation
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Enabling Large Language Models to Generate Text with Citations
Gao, T., Yen, H., Yu, J., & Chen, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.398 · Cited by 216
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Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review
Zhang, W., & Zhang, J. (2025) · Mathematics · doi:10.3390/math13050856 · Cited by 145
Metadata
- Authors
- Stelmakh, I., Luan, Y., Dhingra, B., & Chang, M.-W.
- Venue
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Published
- 2022
- Pages
- 8273-8288
- Cited by
- 63
- References
- 27
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- Stelmakh, I., Luan, Y., Dhingra, B., & Chang, M.-W. (2022). ASQA: Factoid Questions Meet Long-Form Answers. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/2022.emnlp-main.566
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
