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In a systematic way, we investigate a widely asked question: Do LLMs really understand what they say?, which relates to the more familiar term Stochastic Parrot.
Language models are few-shot learners
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Climbing towards nlu: On meaning, form, and understanding in the age of data
Emily M Bender and Alexander Koller. 2020 · 2020
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On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021 · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2021 · 2021
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Communicating natural programs to humans and machines
Sam Acquaviva, Yewen Pu, Marta Kryven, Theodoros Sechopoulos, Catherine Wong, Gabrielle Ecanow, Maxwell Nye, Michael Tessler, and Josh Tenenbaum. 2022 · 2022
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FLUTE: Figurative language understanding through textual explanations
Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, and Smaranda Muresan. 2022 · 2022
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A survey of embodied ai: From simulators to research tasks
Jiafei Duan, Samson Yu, Hui Li Tan, Hongyuan Zhu, and Cheston Tan. 2022 · 2022
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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
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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
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The reversal curse: Llms trained on" a is b" fail to learn" b is a"
Lukas Berglund, Meg Tong, Max Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, and Owain Evans. 2023 · 2023
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Stochastic parrots or intelligent systems? a perspective on true depth of understanding in llms
Ali Borji. 2023 · 2023
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
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Hypothesis search: Inductive reasoning with language models
Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, and Noah D Goodman. 2023 · 2023
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Language models meet world models: Embodied experiences enhance language models
Jiannan Xiang, Tianhua Tao, Yi Gu, Tianmin Shu, Zirui Wang, Zichao Yang, and Zhiting Hu. 2023 · 2023
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Yudong Xu, Wenhao Li, Pashootan Vaezipoor, Scott Sanner, and Elias B Khalil. 2023 · 2023
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Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks
Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, Bin Li, Ping Luo, Tong Lu, Yu Qiao, and Jifeng Dai. 2023 · 2023
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Using large language models in psychology
Dorottya Demszky, Diyi Yang, David S Yeager, Christopher J Bryan, Margarett Clapper, Susannah Chandhok, Johannes C Eichstaedt, Cameron Hecht, Jeremy Jamieson, Meghann Johnson, et al. 2023 · 2023
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Da Silva Gameiro Henrique, Andrei Kucharavy, and Rachid Guerraoui. 2023 · 2023
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Do androids laugh at electric sheep? humor “understanding” benchmarks from the new yorker caption contest
Jack Hessel, Ana Marasovic, Jena D. Hwang, Lillian Lee, Jeff Da, Rowan Zellers, Robert Mankoff, and Yejin Choi. 2023 · 2023
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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
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The dark side of chatgpt: legal and ethical challenges from stochastic parrots and hallucination
Zihao Li. 2023 · 2023
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Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. 2023 · 2023
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Mo Yu, Jiangnan Li, Shunyu Yao, Wenjie Pang, Xiaochen Zhou, Zhou Xiao, Fandong Meng, and Jie Zhou. 2023 · 2023
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A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al. 2024 · 2024
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How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites
Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye, Zhangwei Gao, Erfei Cui, Wenwen Tong, Kongzhi Hu, Jiapeng Luo, Zheng Ma, et al. 2024 · 2024
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Can llms understand computer networks? towards a virtual system administrator
Denis Donadel, Francesco Marchiori, Luca Pajola, and Mauro Conti. 2024 · 2024
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Flocks of stochastic parrots: Differentially private prompt learning for large language models
Haonan Duan, Adam Dziedzic, Nicolas Papernot, and Franziska Boenisch. 2024 · 2024
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Anpl: Towards natural programming with interactive decomposition
Di Huang, Ziyuan Nan, Xing Hu, Pengwei Jin, Shaohui Peng, Yuanbo Wen, Rui Zhang, Zidong Du, Qi Guo, Yewen Pu, et al. 2024 · 2024
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Previously on the stories: Recap snippet identification for story reading
Jiangnan Li, Qiujing Wang, Liyan Xu, Wenjie Pang, Mo Yu, Zheng Lin, Weiping Wang, and Jie Zhou. 2024 · 2024
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Introducing meta llama 3: The most capable openly available llm to date
MetaAI. 2024 · 2024
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Large-language models facilitate discovery of the molecular signatures regulating sleep and activity
Di Peng, Liubin Zheng, Dan Liu, Cheng Han, Xin Wang, Yan Yang, Li Song, Miaoying Zhao, Yanfeng Wei, Jiayi Li, et al. 2024 · 2024
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Metaphor understanding challenge dataset for llms
Xiaoyu Tong, Rochelle Choenni, Martha Lewis, and Ekaterina Shutova. 2024 · 2024
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Speak it out: Solving symbol-related problems with symbol-to-language conversion for language models
Yile Wang, Sijie Cheng, Zixin Sun, Peng Li, and Yang Liu. 2024 · 2024
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Understanding llms’ fluid intelligence deficiency: An analysis of the arc task
Junjie Wu, Mo Yu, Lemao Liu, Dit-Yan Yeung, and Jie Zhou. 2025 · 2025
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