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While Large Language Models (LLMs) have shown exceptional performance in various tasks, one of their most prominent drawbacks is generating inaccurate or false information with a confident tone.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Calibration of machine learning models
Antonio Bella, Cèsar Ferri, José Hernández-Orallo, and María José Ramírez-Quintana. 2010 · 2010
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al. 2017 · 2017
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Wizard of wikipedia: Knowledge-powered conversational agents
Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2018 · 2018
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Fever: a large-scale dataset for fact extraction and verification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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The fever2. 0 shared task
James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, and Arpit Mittal. 2019 · 2019
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Adaptive sparse dropout: Learning the certainty and uncertainty in deep neural networks
Yuanyuan Chen and Zhang Yi. 2021 · 2021
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Understanding factuality in abstractive summarization with frank: A benchmark for factuality metrics
Artidoro Pagnoni, Vidhisha Balachandran, and Yulia Tsvetkov. 2021 · 2021
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Fine-tuning language models to find agreement among humans with diverse preferences
Michiel Bakker, Martin Chadwick, Hannah Sheahan, Michael Tessler, Lucy Campbell-Gillingham, Jan Balaguer, Nat McAleese, Amelia Glaese, John Aslanides, Matt Botvinick, et al. 2022 · 2022
Cited alongside, same era.
Michael Bommarito II and Daniel Martin Katz. 2022 · 2022
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Discovering latent knowledge in language models without supervision
Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. 2022 · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022 · 2022
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Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023 · 2023
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Palm-e: An embodied multimodal language model
Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al. 2023 · 2023
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
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David Dale, Elena Voita, Loïc Barrault, and Marta R Costa-jussà. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, et al. 2023 · 2023
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Llama 2: Early adopters’ utilization of meta’s new open-source pretrained model
Konstantinos I Roumeliotis, Nikolaos D Tselikas, and Dimitrios K Nasiopoulos. 2023 · 2023
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