The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
AmbigQA: Answering Ambiguous Open-domain Questions
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“A significant challenge in open-domain question answering is the potential temporal mismatch between the Wikipedia dump and the question-answer pairs in the dataset, which can lead to missing answers in the dataset, as highlighted in the AmbigQA study [34].”
From The power of noise: Redefining retrieval for rag systems (2024) · cites this paper as [34]
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (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/n19-1423 · Cited by 54,325
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
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Self-citationDense 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
Self-citationNatural 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
TriviaQA: 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-citationTriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D. S., & Zettlemoyer, L. (2017) · arXiv · doi:10.48550/arxiv.1705.03551 · Cited by 1,650
Self-citationReading Wikipedia to answer open-domain questions
Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1171 · Cited by 1,546
Latent retrieval for weakly supervised open domain question answering
Lee, K., Chang, M.-W., & Toutanova, K. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1612 · Cited by 771
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Lost in the Middle: How Language Models Use Long Contexts
Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00638 · Cited by 1,612
“We use the ambiguity annotations of Min et al. (2020) to create a subset unambiguous questions.”
MethodA Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
Fan, W., Ding, Y., Ning, L., Wang, S., Li, H., Yin, D., Chua, T.-S., & Li, Q. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671470 · Cited by 827
UNIFIEDQA: Crossing Format Boundaries with a Single QA System
Khashabi, D., Min, S., Khot, T., Sabharwal, A., Tafjord, O., Clark, P., & Hajishirzi, H. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.171 · Cited by 514
Self-citationThe power of noise: Redefining retrieval for rag systems
Cuconasu, F., Trappolini, G., Siciliano, F., Filice, S., Campagnano, C., Maarek, Y., Tonellotto, N., & Silvestri, F. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657834 · Cited by 236
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
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Aremu, A., Bosselut, A., Chandu, K. R., Clinciu, M.-A., Das, D., Dhole, K., et al. (2021) · Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) · doi:10.18653/v1/2021.gem-1.10 · Cited by 159
Generation-Augmented Retrieval for Open-Domain Question Answering
Mao, Y., He, P., Liu, X., Shen, Y., Gao, J., Han, J., & Chen, W. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.316 · Cited by 146
ClarifyGPT: A Framework for Enhancing LLM-Based Code Generation via Requirements Clarification
Mu, F., Shi, L., Wang, S., Yu, Z., Zhang, B., Wang, C., Liu, S., & Wang, Q. (2024) · Proceedings of the ACM on Software Engineering · doi:10.1145/3660810 · Cited by 74
“The task of generating clarifying questions for ambiguous queries or dialogues has received much attention in information retrieval and dialogue system fields [10, 18, 20, 30, 37, 41].”
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
Gou, Z., Shao, Z., Gong, Y., Shen, Y., Yang, Y., Duan, N., & Chen, W. (2023) · arXiv · doi:10.48550/arxiv.2305.11738 · Cited by 561
“Datasets and Metrics We experiment with three datasets: AmbigNQ (Min et al., 2020), an enhanced version of Natural Question (Kwiatkowski et al., 2019) that employs multi-reference annotations to resolve ambiguity, along with TriviaQA (Joshi et al., 2017) and HotpotQA (Yang et al., 2018).”
BackgroundMethodInfluentialTomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation
Bulian, J., Buck, C., Gajewski, W., Börschinger, B., & Schuster, T. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.20 · Cited by 44
R2-D2: A Modular Baseline for Open-Domain Question Answering
Fajcik, M., Docekal, M., Ondrej, K., & Smrz, P. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.73 · Cited by 36
Generation-Augmented Retrieval for Open-domain Question Answering
Mao, Y., He, P., Liu, X., Shen, Y., Gao, J., Han, J., & Chen, W. (2020) · arXiv · doi:10.48550/arxiv.2009.08553 · Cited by 31
Chen, H.-T., Zhang, M. J. Q., & Choi, E. (2022) · arXiv · doi:10.48550/arxiv.2210.13701 · Cited by 31
Chen, H.-T., Zhang, M., & Choi, E. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.146 · Cited by 31
SciFact-Open: Towards open-domain scientific claim verification
Wadden, D., Lo, K., Kuehl, B., Cohan, A., Beltagy, I., Wang, L. L., & Hajishirzi, H. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.347 · Cited by 35
Self-citationCan Large Language Models Capture Dissenting Human Voices?
Lee, N., An, N. M., & Thorne, J. (2023) · arXiv · doi:10.48550/arxiv.2305.13788 · Cited by 22
Selectively Answering Ambiguous Questions
Cole, J., Zhang, M., Gillick, D., Eisenschlos, J., Dhingra, B., & Eisenstein, J. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.35 · Cited by 19
Query Rewriting for Retrieval-Augmented Large Language Models
Ma, X., Gong, Y., He, P., Zhao, H., & Duan, N. (2023) · arXiv · doi:10.48550/arxiv.2305.14283 · Cited by 14
“For benchmarks that provide official training and test splits (HotpotQA and AmbigNQ), we use the whole training set.”
BackgroundMethodInfluentialAnswering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework
Shao, Z., & Huang, M. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.128 · Cited by 6
Chiang, C.-H., & Lee, H.-Y. (2024) · Findings of the Association for Computational Linguistics ACL 2024 · doi:10.18653/v1/2024.findings-acl.160 · Cited by 5
Metadata
- Authors
- Min, S., Michael, J., Hajishirzi, H., & Zettlemoyer, L.
- Venue
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2020
- Pages
- 5783-5797
- Cited by
- 87
- References
- 13
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