Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) have shown remarkable capabilities in various natural language processing 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
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
Stress test evaluation for natural language inference
Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 2018 · 2018
Earlier work this paper cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Earlier work this paper cites.
Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018 · 2018
Earlier work this paper cites.
Unlearn dataset bias in natural language inference by fitting the residual
He He, Sheng Zha, and Haohan Wang. 2019 · 2019
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
R Thomas McCoy, Ellie Pavlick, and Tal Linzen. 2020 · 2019
Earlier work this paper cites.
Paws: Paraphrase adversaries from word scrambling
Yuan Zhang, Jason Baldridge, and Luheng He. 2019 · 2019
Earlier work this paper cites.
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. 2020 · 2020
Earlier work this paper cites.
Reformulating unsupervised style transfer as paraphrase generation
Kalpesh Krishna, John Wieting, and Mohit Iyyer. 2020 · 2020
Earlier work this paper cites.
Beyond accuracy: Behavioral testing of nlp models with checklist
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
Cited alongside, same era.
What do models learn from question answering datasets?
Priyanka Sen and Amir Saffari. 2020 · 2020
Cited alongside, same era.
Towards interpreting and mitigating shortcut learning behavior of nlu models
Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun, and Xia Hu. 2021 · 2021
Cited alongside, same era.
Why machine reading comprehension models learn shortcuts?
Yuxuan Lai, Chen Zhang, Yansong Feng, Quzhe Huang, and Dongyan Zhao. 2021 · 2021
Cited alongside, same era.
Mind the style of text! adversarial and backdoor attacks based on text style transfer
Fanchao Qi, Yangyi Chen, Xurui Zhang, Mukai Li, Zhiyuan Liu, and Maosong Sun. 2021 · 2021
Cited alongside, same era.
Large language models are zero-shot reasoners
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
Later among the works it cites.
Which is better? exploring prompting strategy for llm-based metrics
JoongHoon Kim, Sangmin Lee, Seung Hun Han, Saeran Park, Jiyoon Lee, Kiyoon Jeong, and Pilsung Kang. 2023 · 2023
Later among the works it cites.
Introducing chatgpt
OpenAI. 2023 · 2023
Later among the works it cites.
Large language models can be lazy learners: Analyze shortcuts in in-context learning
Ruixiang Tang, Dehan Kong, Longtao Huang, and Hui Xue. 2023 · 2023
Later among the works it cites.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Cited alongside, same era.
Glm-130b: An open bilingual pre-trained model
Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, et al. 2022 · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Cited alongside, same era.
Shortcut learning of large language models in natural language understanding
Mengnan Du, Fengxiang He, Na Zou, Dacheng Tao, and Xia Hu. 2023 · 2023
Cited alongside, same era.
Later among the works it cites.
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
Later among the works it cites.
Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms
Miao Xiong, Zhiyuan Hu, Xinyang Lu, YIFEI LI, Jie Fu, Junxian He, and Bryan Hooi. 2023 · 2023
Later among the works it cites.
Less annotating, more classifying: Addressing the data scarcity issue of supervised machine learning with deep transfer learning and bert-nli
Moritz Laurer, Wouter Van Atteveldt, Andreu Casas, and Kasper Welbers. 2024 · 2024
Closest in time.
Towards faithful explanations: Boosting rationalization with shortcuts discovery
Linan Yue, Qi Liu, Yichao Du, Li Wang, Weibo Gao, and Yanqing An. 2024 · 2024
Closest in time.
Comi: Correct and mitigate shortcut learning behavior in deep neural networks
Lili Zhao, Qi Liu, Linan Yue, Wei Chen, Liyi Chen, Ruijun Sun, and Chao Song. 2024 · 2024
Closest in time.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
Closest in time.