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Causal reasoning is fundamental to human intelligence and crucial for effective decision-making in real-world environments.
Tvqa+: Spatio-temporal grounding for video question answering
Jie Lei, Licheng Yu, Tamara L Berg, and Mohit Bansal. 2019 · 1904
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Clevrer: Collision events for video representation and reasoning
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Probabilistic reasoning in intelligent systems: networks of plausible inference
Judea Pearl. 1988 · 1988
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PaperRobot: Incremental draft generation of scientific ideas
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Causal diagrams for empirical research
Judea Pearl. 1995 · 1995
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Causation, prediction, and search
Peter Spirtes, Clark Glymour, and Richard Scheines. 2001 · 2001
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An assessment of the range and usefulness of lexical diversity measures and the potential of the measure of textual, lexical diversity (MTLD)
Philip M McCarthy. 2005 · 2005
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Causal cognition in human and nonhuman animals: A comparative, critical review
Derek C Penn and Daniel J Povinelli. 2007 · 2007
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Probabilistic logic networks: A comprehensive framework for uncertain inference
Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, and Ari Heljakka. 2008 · 2008
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Causality
Judea Pearl. 2009 · 2009
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Reasoning about goals, steps, and temporal ordering with wikihow
Li Zhang, Qing Lyu, and Chris Callison-Burch. 2020 · 2009
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Cutting the gordian knot: The moving-average type–token ratio (mattr)
Michael A Covington and Joe D McFall. 2010 · 2010
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Mtld, vocd-d, and hd-d: A validation study of sophisticated approaches to lexical diversity assessment
Philip M McCarthy and Scott Jarvis. 2010 · 2010
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Getting causes from powers
Stephen Mumford and Rani Lill Anjum. 2011 · 2011
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Sapiens: A brief history of humankind
Yuval Noah Harari. 2014 · 2014
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Causal inference in statistics, social, and biomedical sciences
Guido W Imbens and Donald B Rubin. 2015 · 2015
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Visual7w: Grounded question answering in images
Yuke Zhu, Oliver Groth, Michael S. Bernstein, and Li Fei-Fei. 2016 · 2016
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Making the V in VQA matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017 · 2017
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. 2017 · 2017
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Discovering causal signals in images
David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Scholkopf, and Léon Bottou. 2017 · 2017
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. 2017 · 2017
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The Oxford handbook of causal reasoning
Michael Waldmann. 2017 · 2017
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Fvqa: Fact-based visual question answering
Peng Wang, Qi Wu, Chunhua Shen, Anthony Dick, and Anton Van Den Hengel. 2017 · 2017
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The book of why: the new science of cause and effect
Judea Pearl and Dana Mackenzie. 2018 · 2018
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Toward driving scene understanding: A dataset for learning driver behavior and causal reasoning
Vasili Ramanishka, Yi-Ting Chen, Teruhisa Misu, and Kate Saenko. 2018 · 2018
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Review of causal discovery methods based on graphical models
Clark Glymour, Kun Zhang, and Peter Spirtes. 2019 · 2019
From representation to reasoning: Towards both evidence and commonsense reasoning for video question-answering
Jiangtong Li, Li Niu, and Liqing Zhang. 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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VALSE: A task-independent benchmark for vision and language models centered on linguistic phenomena
Letitia Parcalabescu, Michele Cafagna, Lilitta Muradjan, Anette Frank, Iacer Calixto, and Albert Gatt. 2022 · 2022
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Lexicalrichness: A small module to compute textual lexical richness
Lucas Shen. 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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Cited alongside, same era.
OK-VQA: A visual question answering benchmark requiring external knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. 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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Atomic: An atlas of machine commonsense for if-then reasoning
Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A Smith, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
From recognition to cognition: Visual commonsense reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Counterfactual vision and language learning
Ehsan Abbasnejad, Damien Teney, Amin Parvaneh, Javen Shi, and Anton van den Hengel. 2020 · 2020
Cited alongside, same era.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Cited alongside, same era.
A survey on causal discovery: theory and practice
Alessio Zanga, Elif Ozkirimli, and Fabio Stella. 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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Mme: A comprehensive evaluation benchmark for multimodal large language models
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, et al. 2023 · 2023
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Seungju Han, Junhyeok Kim, Jack Hessel, Liwei Jiang, Jiwan Chung, Yejin Son, Yejin Choi, and Youngjae Yu. 2023 · 2023
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Causal reasoning and large language models: Opening a new frontier for causality
Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan. 2023 · 2023
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Halueval: A large-scale hallucination evaluation benchmark for large language models
Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2023b · 2023
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Negative object presence evaluation (nope) to measure object hallucination in vision-language models
Holy Lovenia, Wenliang Dai, Samuel Cahyawijaya, Ziwei Ji, and Pascale Fung. 2023 · 2023
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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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Causal parrots: Large language models may talk causality but are not causal
Matej Zečević, Moritz Willig, Devendra Singh Dhami, and Kristian Kersting. 2023 · 2023
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Understanding causality with large language models: Feasibility and opportunities
Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, et al. 2023 · 2023
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Introducing the next generation of claude
Anthropic. 2024 · 2024
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Causal-hri: Causal learning for human-robot interaction
Jiaee Cheong, Nikhil Churamani, Luke Guerdan, Tabitha Edith Lee, Zhao Han, and Hatice Gunes. 2024 · 2024
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The essential role of causality in foundation world models for embodied ai
Tarun Gupta, Wenbo Gong, Chao Ma, Nick Pawlowski, Agrin Hilmkil, Meyer Scetbon, Ade Famoti, Ashley Juan Llorens, Jianfeng Gao, Stefan Bauer, et al. 2024 · 2024
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LLaVA-UHD: an lmm perceiving any aspect ratio and high-resolution images
Ruyi Xu, Yuan Yao, Zonghao Guo, Junbo Cui, Zanlin Ni, Chunjiang Ge, Tat-Seng Chua, Zhiyuan Liu, and Gao Huang. 2024 · 2024
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