A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning
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Knowledge-intensive language tasks (KILTs) benefit from retrieving high-quality relevant contexts from large external knowledge corpora. Learning task-specific retrievers that return relevant contexts at an appropriate level of semantic granularity, such as a document retriever, passage retriever, sentence retriever, and entity retriever, may help to achieve better performance on the end-to-end task. But a task-specific retriever usually has poor generalization ability to new domains and tasks, and it may be costly to deploy a variety of specialised retrievers in practice.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058 · see citing works
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · cited 2× · see citing works
“To alleviate the discrepancy between pre-training and fine-tuning, prompt learning has been proposed [30].”
BackgroundThe probabilistic relevance framework: BM25 and beyond
Robertson, S., & Zaragoza, H. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000019 · Cited by 3,374 · see citing works
GPT-3: Its Nature, Scope, Limits, and Consequences
Floridi, L., & Chiriatti, M. (2020) · Minds and Machines · doi:10.1007/s11023-020-09548-1 · Cited by 2,519 · see citing works
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 · see citing works
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 · see citing works
Reading 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 · see citing works
FEVER: a Large-scale Dataset for Fact Extraction and VERification
Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (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/n18-1074 · Cited by 1,343 · see citing works
Opportunistic data structures with applications
Ferragina, P., & Manzini, G. (2000) · Proceedings 41st Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.2000.892127 · Cited by 1,325 · see citing works
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Izacard, G., & Grave, E. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.74 · Cited by 1,109 · see citing works
Finetuned Language Models Are Zero-Shot Learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2021) · arXiv · doi:10.48550/arxiv.2109.01652 · Cited by 1,101 · see citing works
Zero-Shot Relation Extraction via Reading Comprehension
Levy, O., Seo, M., Choi, E., & Zettlemoyer, L. (2017) · Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) · doi:10.18653/v1/k17-1034 · Cited by 482 · cited 3× · see citing works
“Knowledge-intensive language tasks (KILTs), such as fact checking [41] and slot filling [27], have gained much attention in recent years.”
BackgroundZero-Shot Relation Extraction via Reading Comprehension
Levy, O., Seo, M., Choi, E., & Zettlemoyer, L. (2017) · arXiv · doi:10.48550/arxiv.1706.04115 · Cited by 482
KILT: a Benchmark for Knowledge Intensive Language Tasks
Petroni, F., Piktus, A., Fan, A., Lewis, P., Yazdani, M., De Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.200 · Cited by 357 · see citing works
ELI5: long form question answering
Fan, A., Jernite, Y., Perez, E., Grangier, D., Weston, J., & Auli, M. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1346 · Cited by 319 · see citing works
Robust named entity disambiguation with random walks
Guo, Z., & Barbosa, D. (2017) · Semantic Web · doi:10.3233/sw-170273 · Cited by 136 · see citing works
Rethinking search: making domain experts out of dilettantes
Metzler, D., Tay, Y., Bahri, D., & Najork, M. (2021) · ACM SIGIR Forum · doi:10.1145/3476415.3476428 · Cited by 112 · see citing works
Transformer Memory as a Differentiable Search IndexPreprint
Tay, Y., Tran, V. Q., Dehghani, M., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.06991 · Cited by 80 · see citing works
GERE: Generative Evidence Retrieval for Fact Verification
Chen, J., Zhang, R., Guo, J., Fan, Y., & Cheng, X. (2022) · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3477495.3531827 · Cited by 66 · see citing works
Self-citationCorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks
Chen, J., Zhang, R., Guo, J., Liu, Y., Fan, Y., & Cheng, X. (2022) · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · doi:10.1145/3511808.3557271 · Cited by 57 · see citing works
Self-citationA Neural Corpus Indexer for Document RetrievalPreprint
Wang, Y., Hou, Y., Wang, H., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.02743 · Cited by 47 · see citing works
DynamicRetriever: A Pre-training Model-based IR System with Neither Sparse nor Dense IndexPreprint
Zhou, Y., Yao, J., Dou, Z., Wu, L., & Wen, J.-R. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.00537 · Cited by 10 · see citing works
The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer EncodersPreprint
He, H. W., & Choi, J. D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.06939
Flagged first, then by citations
Research works printed but not linked: 21
- [1]1. Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, and Roland Vollgraf. 2019. FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP. In NAACL 2019. 54-59.
- [2]2. Michele Bevilacqua, Giuseppe Ottaviano, Patrick Lewis, Wen tau Yih, Sebastian Riedel, and Fabio Petroni. 2022. Autoregressive Search Engines: Generating Substrings as Document Identifiers. In arXiv pre-print 2204.10628.
- [8]8. Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. 2020. Autore- gressive Entity Retrieval. In International Conference on Learning Representations.
- [9]9. Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2018. Wizard of Wikipedia: Knowledge-Powered Conversational Agents. In International Conference on Learning Representations.
- [10]10. Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, and Elena Simperl. 2018. T-rex: A Large Scale Align- ment of Natural Language with Knowledge base triples. In LREC 2018.
- [14]14. Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, and Alfio Gliozzo. 2021. Robust Retrieval Augmented Generation for Zero-shot Slot Filling. In EMNLP 2021. 1939-1949.
- [15]15. Yuxian Gu, Xu Han, Zhiyuan Liu, and Minlie Huang. 2022. PPT: Pre-trained Prompt Tuning for Few-shot Learning. In ACL.
- [21]21. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. 2022. Survey of Hallucination in Natural Language Generation. arXiv preprint arXiv:2202.03629 (2022). · arXiv
- [23]23. Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense Passage Retrieval for Open- Domain Question Answering. In EMNLP 2020. 6769-6781.
- [24]24. Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT. 4171-4186.
- [25]25. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Opti- mization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.).
- [28]28. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In ACL. 7871-7880.
- [29]29. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020. Retrieval-augmented Generation for Knowledge-intensive NLP Tasks. NeurIPS 2020 33 (2020), 9459-9474.
- [31]31. Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021. GPT Understands, Too. arXiv preprint arXiv:2103.10385 (2021). · arXiv
- [32]32. Jean Maillard, Vladimir Karpukhin, Fabio Petroni, Wen-tau Yih, Barlas Oguz, Veselin Stoyanov, and Gargi Ghosh. 2021. Multi-Task Retrieval for Knowledge- Intensive Tasks. In ACL 2021. 1098-1111.
- [37]37. Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M. Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush. 2022. Multitask Prompted Training Enables Zero-Shot Task Generalization. In ICLR.
- [38]38. Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to Sequence Learning with Neural Networks. Advances in neural information processing systems 27 (2014).
- [39]39. Alon Talmor and Jonathan Berant. 2019. MultiQA: An Empirical Investiga- tion of Generalization and Transfer in Reading Comprehension. arXiv preprint arXiv:1905.13453 (2019). · arXiv
- [44]44. Ledell Wu, Fabio Petroni, Martin Josifoski, Sebastian Riedel, and Luke Zettlemoyer. 2020. Scalable Zero-shot Entity Linking with Dense Entity Retrieval. In EMNLP 2020. 6397-6407.
- [45]45. Shicheng Xu, Liang Pang, Huawei Shen, and Xueqi Cheng. 2022. Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning. arXiv preprint arXiv:2204.02725 (2022). · arXiv
- [46]46. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. In EMNLP. Association for Computational Linguistics, Brussels, Belgium, 2369-2380.
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- Authors
- Chen, J., Zhang, R., Guo, J., de Rijke, M., Liu, Y., Fan, Y., & Cheng, X.
- Affiliations
- Institute of Computing Technology (CN)
- University of Amsterdam, Amsterdam, Netherlands
- Tsinghua University (CN)
- Venue
- Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2023-07-18
- Pages
- 1448-1457
- OpenAlex
- W4367628274
- Cited by
- 32
- References
- 21
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 17 · 2023: 4
- FWCI
- 5.68
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (NSFC) (No. 62006218 and 61902381)
- Lenovo-CAS Joint Lab Youth Scientist Project
- Youth Innovation Promotion Association CAS (No. 20144310 and 2021100)
- Young Elite Scientist Sponsorship Program by CAST (No. YESS20200121)
- Hybrid Intelligence Center
- China Scholarship Council (No. 202104910234)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- prompt learning, document retrieval, passage retrieval, sentence retrieval, entity retrieval, semantic granularity, domain generalization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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