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Large Language Models (LLMs) have shown great potential in Natural Language Processing (NLP) tasks.
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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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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Recipes for building an open-domain chatbot
Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M Smith, et al. 2020 · 2004
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Deep exploration via bootstrapped dqn
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy. 2016 · 2016
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017 · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel. 2017 · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017 · 2017
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Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton. 2017 · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
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Comqa: A community-sourced dataset for complex factoid question answering with paraphrase clusters
Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, and Gerhard Weikum. 2019 · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Using pre-training can improve model robustness and uncertainty
Dan Hendrycks, Kimin Lee, and Mantas Mazeika. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Calibration of pre-trained transformers
Shrey Desai and Greg Durrett. 2020 · 2020
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2020 · 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, Peter J Liu, et al. 2020 · 2020
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Knowledgeable or educated guess? revisiting language models as knowledge bases
Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun, Lingyong Yan, Meng Liao, Tong Xue, and Jin Xu. 2021 · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021
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Unsolved problems in ml safety
Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt. 2021 · 2021
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How can we know when language models know? on the calibration of language models for question answering
Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig. 2021 · 2021
Cited alongside, same era.
Improving calibration through the relationship with adversarial robustness
Yao Qin, Xuezhi Wang, Alex Beutel, and Ed Chi. 2021 · 2021
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
Cited alongside, same era.
Calibrating factual knowledge in pretrained language models
Qingxiu Dong, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. 2022 · 2022
Cited alongside, same era.
Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022 · 2022
Evaluating the logical reasoning ability of chatgpt and gpt-4
Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, Jian Liu, Qiji Zhou, and Yue Zhang. 2023 · 2023
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When not to trust language models: Investigating effectiveness and limitations of parametric and non-parametric memories
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Hannaneh Hajishirzi, and Daniel Khashabi. 2023 · 2023
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Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Potsawee Manakul, Adian Liusie, and Mark JF Gales. 2023 · 2023
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Self-contradictory hallucinations of large language models: Evaluation, detection and mitigation
Niels Mündler, Jingxuan He, Slobodan Jenko, and Martin Vechev. 2023 · 2023
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Cited alongside, same era.
Calibration meets explanation: A simple and effective approach for model confidence estimates
Dongfang Li, Baotian Hu, and Qingcai Chen. 2022 · 2022
Cited alongside, same era.
Teaching models to express their uncertainty in words
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022
Cited alongside, same era.
Reducing conversational agents’ overconfidence through linguistic calibration
Sabrina J Mielke, Arthur Szlam, Emily Dinan, and Y-Lan Boureau. 2022 · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Faviq: Fact verification from information-seeking questions
Jungsoo Park, Sewon Min, Jaewoo Kang, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022 · 2022
Cited alongside, same era.
Quantifying uncertainty in foundation models via ensembles
Meiqi Sun, Wilson Yan, Pieter Abbeel, and Igor Mordatch. 2022 · 2022
Cited alongside, same era.
Can explanations be useful for calibrating black box models?
Xi Ye and Greg Durrett. 2022 · 2022
Cited alongside, same era.
OpenAI. 2023 · 2023
Closest in time.
Is chatgpt a general-purpose natural language processing task solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023 · 2023
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Chatgpt-crawler: Find out if chatgpt really knows what it’s talking about
Aman Rangapur and Haoran Wang. 2023 · 2023
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Prompting gpt-3 to be reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, and Lijuan Wang. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou. 2023 · 2023
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Do large language models know what they don’t know?
Zhangyue Yin, Qiushi Sun, Qipeng Guo, Jiawen Wu, Xipeng Qiu, and Xuanjing Huang. 2023 · 2023
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Beyond confidence: Reliable models should also consider atypicality
Mert Yuksekgonul, Linjun Zhang, James Zou, and Carlos Guestrin. 2023 · 2023
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Jiaxin Zhang, Zhuohang Li, Kamalika Das, Bradley A Malin, and Sricharan Kumar. 2023 · 2023
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Calibrating sequence likelihood improves conditional language generation
Yao Zhao, Misha Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, and Peter J Liu. 2023 · 2023
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Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert
Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, and Dacheng Tao. 2023 · 2023
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Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity
Terry Yue Zhuo, Yujin Huang, Chunyang Chen, and Zhenchang Xing. 2023 · 2023
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Can llms express their uncertainty? an empirical evaluation of confidence elici
Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hoo. 2024 · 2024
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Calibrating structured output predictors for natural language processing
Abhyuday Jagannatha and Hong Yu. 2020 · 2092
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Are nlp models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2094
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