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This paper studies multi-task training of retrieval-augmented generation models for knowledge-intensive tasks.
How reliable are the results of large-scale information retrieval experiments?
Zobel, J · 1998
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The philosophy of information retrieval evaluation
Voorhees, E. M · 2001
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Ms marco: A human generated machine reading comprehension dataset
Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T., et al · 2016
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Joshi, M., Choi, E., Weld, D. S., and Zettlemoyer, L · 2017
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Zero-shot relation extraction via reading comprehension
Levy, O., Seo, M., Choi, E., and Zettlemoyer, L · 2017
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Wizard of wikipedia: Knowledge-powered conversational agents
Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., and Weston, J · 2018
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T-rex: A large scale alignment of natural language with knowledge base triples
Elsahar, H., Vougiouklis, P., Remaci, A., Gravier, C., Hare, J., Laforest, F., and Simperl, E · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
Shazeer, N. and Stern, M · 2018
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Fever: a large-scale dataset for fact extraction and verification
Thorne, J., Vlachos, A., Christodoulopoulos, C., and Mittal, A · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W. W., Salakhutdinov, R., and Manning, C. D · 2018
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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
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Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L · 2019
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Realm: Retrieval-augmented language model pre-training
Guu, K., Lee, K., Tung, Z., Pasupat, P., and Chang, M.-W · 2020
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Leveraging passage retrieval with generative models for open domain question answering
Izacard, G. and Grave, E · 2020
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Dense passage retrieval for open-domain question answering
Karpukhin, V., Oğuz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., and Yih, W.-t · 2020
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Unifiedqa: Crossing format boundaries with a single qa system
Khashabi, D., Min, S., Khot, T., Sabharwal, A., Tafjord, O., Clark, P., and Hajishirzi, H · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Multi-task retrieval for knowledge-intensive tasks
Maillard, J., Karpukhin, V., Petroni, F., Yih, W.-t., Oğuz, B., Stoyanov, V., and Ghosh, G · 2021
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Large dual encoders are generalizable retrievers
Ni, J., Qu, C., Lu, J., Dai, Z., Ábrego, G. H., Ma, J., Zhao, V. Y., Luan, Y., Hall, K. B., Chang, M.-W., et al · 2021
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Hindsight: Posterior-guided training of retrievers for improved open-ended generation
Paranjape, A., Khattab, O., Potts, C., Zaharia, M., and Manning, C. D · 2021
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KILT: a benchmark for knowledge intensive language tasks
Petroni, F., Piktus, A., Fan, A., Lewis, P. S. H., Yazdani, M., Cao, N. D., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., Plachouras, V., Rocktäschel, T., and Riedel, S · 2021
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The web is your oyster–knowledge-intensive nlp against a very large web corpus
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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
Cited alongside, same era.
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., and Liu, P. J · 2020
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Evidentiality-guided generation for knowledge-intensive nlp tasks
Asai, A., Gardner, M., and Hajishirzi, H · 2021
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Trec deep learning track: Reusable test collections in the large data regime
Craswell, N., Mitra, B., Yilmaz, E., Campos, D., Voorhees, E. M., and Soboroff, I · 2021
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R2-d2: A modular baseline for open-domain question answering
Fajcik, M., Docekal, M., Ondrej, K., and Smrz, P · 2021
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Robust retrieval augmented generation for zero-shot slot filling
Glass, M., Rossiello, G., Chowdhury, M. F. M., and Gliozzo, A · 2021
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Tu wien at trec dl and podcast 2021: Simple compression for dense retrieval
Hofstätter, S., Sertkan, M., and Hanbury, A · 2021
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Paq: 65 million probably-asked questions and what you can do with them
Lewis, P., Wu, Y., Liu, L., Minervini, P., Küttler, H., Piktus, A., Stenetorp, P., and Riedel, S · 2021
Cited alongside, same era.
Piktus, A., Petroni, F., Karpukhin, V., Okhonko, D., Broscheit, S., Izacard, G., Lewis, P., Oğuz, B., Grave, E., Yih, W.-t., et al · 2021
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End-to-end training of multi-document reader and retriever for open-domain question answering
Singh, D., Reddy, S., Hamilton, W., Dyer, C., and Yogatama, D · 2021
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Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models
Thakur, N., Reimers, N., Rücklé, A., Srivastava, A., and Gurevych, I · 2021
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Re2g: Retrieve, rerank, generate
Anonymous · 2022
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Autoregressive search engines: Generating substrings as document identifiers
Bevilacqua, M., Ottaviano, G., Lewis, P., Yih, W.-t., Riedel, S., and Petroni, F · 2022
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Introducing neural bag of whole-words with colberter: Contextualized late interactions using enhanced reduction
Hofstätter, S., Khattab, O., Althammer, S., Sertkan, M., and Hanbury, A · 2022
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Scaling up models and data with t5x
Roberts, A., Chung, H. W., Levskaya, A., Mishra, G., Bradbury, J., Andor, D., Narang, S., Lester, B., Gaffney, C., Mohiuddin, A., Hawthorne, C., Lewkowycz, A., Salcianu, A., van Zee, M., Austin, J., Goodman, S., Soares, L. B., Hu, H., Tsvyashchenko, S., Chowdhery, A., Bastings, J., Bulian, J., Garcia, X., Ni, J., Chen, A., Kenealy, K., Clark, J. H., Lee, S., Garrette, D., Lee-Thorp, J., Raffel, C., Shazeer, N., Ritter, M., Bosma, M., Passos, A., Maitin-Shepard, J., Fiedel, N., Omernick, M., Saeta, B., Sepassi, R., Spiridonov, A., Newlan, J., and Gesmundo, A · 2022
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Retrieval-enhanced machine learning
Zamani, H., Diaz, F., Dehghani, M., Metzler, D., and Bendersky, M · 2022
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