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Retriever-augmented instruction-following models are attractive alternatives to fine-tuned approaches for information-seeking tasks such as question answering (QA).
Language models are few-shot learners
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Natural questions: A benchmark for question answering research
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CoQA: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D. Manning. 2019 · 2019
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Dense passage retrieval for open-domain question answering
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Retrieval-augmented generation for knowledge-intensive NLP tasks
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 · 2020
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NeurIPS 2020 EfficientQA Competition: Systems, analyses and lessons learned
Sewon Min, Jordan Boyd-Graber, Chris Alberti, Danqi Chen, Eunsol Choi, Michael Collins, Kelvin Guu, Hannaneh Hajishirzi, Kenton Lee, Jennimaria Palomaki, Colin Raffel, Adam Roberts, Tom Kwiatkowski, Patrick Lewis, Yuxiang Wu, Heinrich Küttler, Linqing Liu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel, Sohee Yang, Minjoon Seo, Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Edouard Grave, Ikuya Yamada, Sonse Shimaoka, Masatoshi Suzuki, Shumpei Miyawaki, Shun Sato, Ryo Takahashi, Jun Suzuki, Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz, Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, and Wen-tau Yih. 2021 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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BERTScore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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Q 2 {}^{\textrm{2}} : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering
Or Honovich, Leshem Choshen, Roee Aharoni, Ella Neeman, Idan Szpektor, and Omri Abend. 2021 · 2021
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Retrieval augmentation reduces hallucination in conversation
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Combining lexical and dense retrieval for computationally efficient multi-hop question answering
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Answering complex open-domain questions with multi-hop dense retrieval
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Finetuned language models are zero-shot learners
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OPT: Open pre-trained transformer language models
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Can large language models be an alternative to human evaluations?
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The dangers of trusting stochastic parrots: Faithfulness and trust in open-domain conversational question answering
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Scaling instruction-finetuned language models
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Internet-augmented language models through few-shot prompting for open-domain question answering
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Stanford Alpaca: An instruction-following LLaMA model
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Large language models are not fair evaluators
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A critical evaluation of evaluations for long-form question answering
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