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Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM).
Combination of multiple searches
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Selecting good expansion terms for pseudo-relevance feedback
Cao, G., Nie, J.-Y., Gao, J., and Robertson, S · 2008
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Answering complex open-domain questions with multi-hop dense retrieval
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Modeling reformulation using query distributions
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SQuAD: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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Reading Wikipedia to answer open-domain questions
Chen, D., Fisch, A., Weston, J., and Bordes, A · 2017
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Fusion in information retrieval: Sigir 2018 half-day tutorial
Kurland, O. and Culpepper, J. S · 2018
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The natural language decathlon: Multitask learning as question answering
McCann, B., Keskar, N. S., Xiong, C., and Socher, R · 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., Toutanova, K., Jones, L., Kelcey, M., Chang, M.-W., Dai, A. M., Uszkoreit, J., Le, Q., and Petrov, S · 2019
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Latent retrieval for weakly supervised open domain question answering
Lee, K., Chang, M.-W., and Toutanova, K · 2019
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Multi-hop reading comprehension through question decomposition and rescoring
Min, S., Zhong, V., Zettlemoyer, L., and Hajishirzi, H · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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Open-domain question answering goes conversational via question rewriting
Anantha, R., Vakulenko, S., Tu, Z., Longpre, S., Pulman, S., and Chappidi, S · 2020
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 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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HoVer: A dataset for many-hop fact extraction and claim verification
Jiang, Y., Bordia, S., Zhong, Z., Dognin, C., Singh, M., and Bansal, M · 2020
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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., and Yih, W.-t · 2020
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Colbert: Efficient and effective passage search via contextualized late interaction over BERT
Khattab, O. and Zaharia, M · 2020
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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P. S. H., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., and Kiela, D · 2020
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Question rewriting for open-domain conversational qa: Best practices and limitations
Del Tredici, M., Barlacchi, G., Shen, X., Cheng, W., and de Gispert, A · 2021
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Geva, M., Khashabi, D., Segal, E., Khot, T., Roth, D., and Berant, J · 2021
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Internet-augmented language models through few-shot prompting for open-domain question answering
Lazaridou, A., Gribovskaya, E., Stokowiec, W., and Grigorev, N · 2022
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Few-shot anaphora resolution in scientific protocols via mixtures of in-context experts
Le, N. T., Bai, F., and Ritter, A · 2022
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Standing on the shoulders of giant frozen language models
Levine, Y., Dalmedigos, I., Ram, O., Zeldes, Y., Jannai, D., Muhlgay, D., Osin, Y., Lieber, O., Lenz, B., Shalev-Shwartz, S., et al · 2022
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Contrastive decoding: Open-ended text generation as optimization
Li, X. L., Holtzman, A., Fried, D., Liang, P., Eisner, J., Hashimoto, T., Zettlemoyer, L., and Lewis, M · 2022
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Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., and Chen, W · 2021
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True few-shot learning with language models
Perez, E., Kiela, D., and Cho, K · 2021
Cited alongside, same era.
Retrieval augmentation reduces hallucination in conversation
Shuster, K., Poff, S., Chen, M., Kiela, D., and Weston, J · 2021
Cited alongside, same era.
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
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Dohan, D., Xu, W., Lewkowycz, A., Austin, J., Bieber, D., Lopes, R. G., Wu, Y., Michalewski, H., Saurous, R. A., Sohl-Dickstein, J., et al · 2022
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Attributed text generation via post-hoc research and revision
Gao, L., Dai, Z., Pasupat, P., Chen, A., Chaganty, A. T., Fan, Y., Zhao, V. Y., Lao, N., Lee, H., Juan, D.-C., et al · 2022
Cited alongside, same era.
Fid-light: Efficient and effective retrieval-augmented text generation
Hofstätter, S., Chen, J., Raman, K., and Zamani, H · 2022
Cited alongside, same era.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
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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 · 2022
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Measuring and narrowing the compositionality gap in language models
Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N. A., and Lewis, M · 2022
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Question rewriting? assessing its importance for conversational question answering
Raposo, G., Ribeiro, R., Martins, B., and Coheur, L · 2022
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ColBERTv2: Effective and efficient retrieval via lightweight late interaction
Santhanam, K., Khattab, O., Saad-Falcon, J., Potts, C., and Zaharia, M · 2022
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Prompting gpt-3 to be reliable
Si, C., Gan, Z., Yang, Z., Wang, S., Wang, J., Boyd-Graber, J., and Wang, L · 2022
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Recitation-augmented language models
Sun, Z., Wang, X., Tay, Y., Yang, Y., and Zhou, D · 2022
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SCAI-QReCC shared task on conversational question answering
Vakulenko, S., Kiesel, J., and Fröbe, M · 2022
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Chain of thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., and Zhou, D · 2022
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On decoding strategies for neural text generators
Wiher, G., Meister, C., and Cotterell, R · 2022
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React: Synergizing reasoning and acting in language models
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y · 2022
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Star: Bootstrapping reasoning with reasoning
Zelikman, E., Wu, Y., and Goodman, N. D · 2022
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Automatic chain of thought prompting in large language models
Zhang, Z., Zhang, A., Li, M., and Smola, A · 2022
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Romqa: A benchmark for robust, multi-evidence, multi-answer question answering
Zhong, V., Shi, W., Yih, W.-t., and Zettlemoyer, L · 2022
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