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Humans often express their communicative intents indirectly or non-literally, which requires their interlocutors -- human or AI -- to understand beyond the literal meaning of words.
Logic and conversation
Herbert P Grice. 1975 · 1975
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Why do people use figurative language?
Richard M Roberts and Roger J Kreuz. 1994 · 1994
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Obligatory processing of literal and nonliteral meanings in verbal irony
Shelly Dews and Ellen Winner. 1999 · 1999
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Understanding figurative language: From metaphor to idioms
Sam Glucksberg and Matthew S McGlone. 2001 · 2001
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Implicature
Wayne A Davis and Wayne A Davis. 2016 · 2016
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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
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A knowledge-grounded neural conversation model
Marjan Ghazvininejad, Chris Brockett, Ming-Wei Chang, Bill Dolan, Jianfeng Gao, Wen-tau Yih, and Michel Galley. 2018 · 2018
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Revisiting the evaluation of theory of mind through question answering
Matthew Le, Y-Lan Boureau, and Maximilian Nickel. 2019 · 2019
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Towards empathetic open-domain conversation models: A new benchmark and dataset
Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2019 · 2019
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Hierarchy response learning for neural conversation generation
Bo Zhang and Xiaoming Zhang. 2019 · 2019
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All that’s ‘human’is not gold: Evaluating human evaluation of generated text
Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, and Noah A Smith. 2021 · 2021
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The perils of using mechanical turk to evaluate open-ended text generation
Marzena Karpinska, Nader Akoury, and Mohit Iyyer. 2021 · 2021
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Flute: Figurative language understanding through textual explanations
Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, and Smaranda Muresan. 2022b · 2022
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EmpHi: Generating empathetic responses with human-like intents
Mao Yan Chen, Siheng Li, and Yujiu Yang. 2022 · 2022
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Yuling Gu, Yao Fu, Valentina Pyatkin, Ian Magnusson, Bhavana Dalvi Mishra, and Peter Clark. 2022b · 2022
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A fine-grained comparison of pragmatic language understanding in humans and language models
Jennifer Hu, Sammy Floyd, Olessia Jouravlev, Evelina Fedorenko, and Edward Gibson. 2022 · 2022
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Testing the ability of language models to interpret figurative language
Emmy Liu, Chenxuan Cui, Kenneth Zheng, and Graham Neubig. 2022a · 2022
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Multi-lingual and multi-cultural figurative language understanding
Anubha Kabra, Emmy Liu, Simran Khanuja, Alham Fikri Aji, Genta Winata, Samuel Cahyawijaya, Anuoluwapo Aremu, Perez Ogayo, and Graham Neubig. 2023 · 2023
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FANToM: A benchmark for stress-testing machine theory of mind in interactions
Hyunwoo Kim, Melanie Sclar, Xuhui Zhou, Ronan Bras, Gunhee Kim, Yejin Choi, and Maarten Sap. 2023a · 2023
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The goldilocks of pragmatic understanding: Fine-tuning strategy matters for implicature resolution by llms
Laura Eline Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, and Edward Grefenstette. 2023 · 2023
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Clever hans or neural theory of mind? stress testing social reasoning in large language models
Natalie Shapira, Mosh Levy, Seyed Hossein Alavi, Xuhui Zhou, Yejin Choi, Yoav Goldberg, Maarten Sap, and Vered Shwartz. 2023 · 2023
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
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Testing the ability of language models to interpret figurative language
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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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Xuhui Zhou, Hao Zhu, Akhila Yerukola, Thomas Davidson, Jena D. Hwang, Swabha Swayamdipta, and Maarten Sap. 2023b · 2022
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Large language models are biased to overestimate profoundness
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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 generative ai paradox:" what it can create, it may not understand"
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