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The pile: An 800gb dataset of diverse text for language modeling

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Retrieval augmented language model pre-training

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The curious case of neural text degeneration

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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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How much knowledge can you pack into the parameters of a language model?

Adam Roberts, Colin Raffel, and Noam Shazeer. 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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Mind the gap: Assessing temporal generalization in neural language models

Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d’Autume, Tomáš Kočiský, Sebastian Ruder, Dani Yogatama, Kris Cao, Susannah Young, and Phil Blunsom. 2021 · 2021

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DExperts: Decoding-time controlled text generation with experts and anti-experts

Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, and Yejin Choi. 2021 · 2021

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Entity-based knowledge conflicts in question answering

Shayne Longpre, Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, and Sameer Singh. 2021 · 2021

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Retrieval augmentation reduces hallucination in conversation

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Toolformer: Language models can teach themselves to use tools

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Llama 2: Open foundation and fine-tuned chat models

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HistAlign: Improving context dependency in language generation by aligning with history

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AlignScore: Evaluating factual consistency with a unified alignment function