From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries

Attention satisfies: A constraint-satisfaction lens on factual errors of language models

Original

Mert Yuksekgonul, Varun Chandrasekaran, Erik Jones, Suriya Gunasekar, Ranjita Naik, Hamid Palangi, Ece Kamar, and Besmira Nushi. 2024 · 1971

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Retrieval-augmented generation for knowledge-intensive nlp tasks

Original

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. 2021 · 2005

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Retrieval augmentation reduces hallucination in conversation

Original

Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021 · 2021

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Towards automated circuit discovery for mechanistic interpretability

Arthur Conmy, Augustine Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adrià Garriga-Alonso. 2023 · 2023

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Dissecting recall of factual associations in auto-regressive language models

Original

Mor Geva, Jasmijn Bastings, Katja Filippova, and Amir Globerson. 2023 · 2023

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The effect of scaling, retrieval augmentation and form on the factual consistency of language models

Lovisa Hagström, Denitsa Saynova, Tobias Norlund, Moa Johansson, and Richard Johansson. 2023 · 2023

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Retrieval-augmented generation (rag): Enhancing llms with external knowledge

IngestAI. 2023 · 2023

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Challenges and applications of large language models

Original

Jean Kaddour, Joshua Harris, Maximilian Mozes, Herbie Bradley, Roberta Raileanu, and Robert McHardy. 2023 · 2023

Cited alongside, same era.

Textbooks are all you need ii: phi-1.5

Original

Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, and Yin Tat Lee. 2023 · 2023

Cited alongside, same era.

Lost in the middle: How language models use long contexts

Original

Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023 · 2023

Cited alongside, same era.

Investigating the factual knowledge boundary of large language models with retrieval augmentation

Eragent: Enhancing retrieval-augmented language models with improved accuracy, efficiency, and personalization

Original

C. Shao, T. Kim, and Z. Gao. 2023 · 2023

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Improving the domain adaptation of retrieval augmented generation (rag) models for open domain question answering

A. Singh, M. Sachan, and K. Guu. 2023 · 2023

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Llama 2: Open foundation and fine-tuned chat models

Original

Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023

Later among the works it cites.