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Collecting diverse human opinions is costly and challenging.
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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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 1904
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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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The nature of human values
Milton Rokeach. 1973 · 1973
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The theory of planned behavior
Icek Ajzen. 1991 · 1991
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Argumentation , pages 3–25
Frans H. van Eemeren, Sally Jackson, and Scott Jacobs. 2015 · 2015
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A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016 · 2016
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Analyzing the semantic types of claims and premises in an online persuasive forum
Christopher Hidey, Elena Musi, Alyssa Hwang, Smaranda Muresan, and Kathy McKeown. 2017 · 2017
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The relation between human values and perceived situation characteristics in everyday life
Rebekka Kesberg and Johannes Keller. 2018 · 2018
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Unifying human and statistical evaluation for natural language generation
Tatsunori B. Hashimoto, Hugh Zhang, and Percy Liang. 2019 · 2019
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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The risk of racial bias in hate speech detection
Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A. Smith. 2019 · 2019
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Evaluating semantic accuracy of data-to-text generation with natural language inference
Ondřej Dušek and Zdeněk Kasner. 2020 · 2020
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Social chemistry 101: Learning to reason about social and moral norms
Maxwell Forbes, Jena D. Hwang, Vered Shwartz, Maarten Sap, and Yejin Choi. 2020 · 2020
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The state and fate of linguistic diversity and inclusion in the NLP world
Pratik Joshi, Sebastin Santy, Amar Budhiraja, Kalika Bali, and Monojit Choudhury. 2020 · 2020
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Moral stories: Situated reasoning about norms, intents, actions, and their consequences
Denis Emelin, Ronan Le Bras, Jena D. Hwang, Maxwell Forbes, and Yejin Choi. 2021 · 2021
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Detecting cross-geographic biases in toxicity modeling on social media
Sayan Ghosh, Dylan Baker, David Jurgens, and Vinodkumar Prabhakaran. 2021 · 2021
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Does BERT learn as humans perceive? understanding linguistic styles through lexica
Shirley Anugrah Hayati, Dongyeop Kang, and Lyle Ungar. 2021 · 2021
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True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
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Evaluating the evaluation of diversity in natural language generation
Guy Tevet and Jonathan Berant. 2021 · 2021
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Learning from the worst: Dynamically generated datasets to improve online hate detection
Bertie Vidgen, Tristan Thrush, Zeerak Waseem, and Douwe Kiela. 2021 · 2021
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Maximilian Wich, Christian Widmer, Gerhard Hagerer, and Georg Groh. 2021 · 2021
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Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022 . European Language Resources Association, Marseille, France
Increasing diversity while maintaining accuracy: Text data generation with large language models and human interventions
John Chung, Ece Kamar, and Saleema Amershi. 2023 · 2023
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What comes next? evaluating uncertainty in neural text generators against human production variability
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Co-writing with opinionated language models affects users’ views
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Gavin Abercrombie, Valerio Basile, Sara Tonelli, Verena Rieser, and Alexandra Uma, editors. 2022 · 2022
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
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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. 2022b · 2022
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Barbara Plank. 2022 · 2022
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Two contrasting data annotation paradigms for subjective NLP tasks
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Semantic diversity in dialogue with natural language inference
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Chain-of-thought prompting elicits reasoning in large language models
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Least-to-most prompting enables complex reasoning in large language models
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In defense of moral realism: Reply to gabennesch
Richard A. Shweder. 1990 · 2067
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