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With the proliferation of social media, accurate detection of hate speech has become critical to ensure safety online.
Racial bias in hate speech and abusive language detection datasets
Thomas Davidson, Debasmita Bhattacharya, and Ingmar Weber. 2019 · 1905
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Social bias frames: Reasoning about social and power implications of language
Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A Smith, and Yejin Choi. 2019b · 1911
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Hatebert: Retraining bert for abusive language detection in english
Tommaso Caselli, Valerio Basile, Jelena Mitrović, and Michael Granitzer. 2020 · 2010
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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. 2020 · 2012
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Us and them: identifying cyber hate on twitter across multiple protected characteristics
Pete Burnap and Matthew L Williams. 2016 · 2016
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A survey on hate speech detection using natural language processing
Anna Schmidt and Michael Wiegand. 2017 · 2017
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Understanding abuse: A typology of abusive language detection subtasks
Zeerak Waseem, Thomas Davidson, Dana Warmsley, and Ingmar Weber. 2017 · 2017
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2023 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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A survey on automatic detection of hate speech in text
Paula Fortuna and Sérgio Nunes. 2018 · 2018
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Custodians of the Internet: Platforms, content moderation, and the hidden decisions that shape social media
Tarleton Gillespie. 2018 · 2018
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Characterizing and detecting hateful users on twitter
Manoel Ribeiro, Pedro Calais, Yuri Santos, Virgílio Almeida, and Wagner Meira Jr. 2018 · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern. 2018 · 2018
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A just and comprehensive strategy for using nlp to address online abuse
David Jurgens, Libby Hemphill, and Eshwar Chandrasekharan. 2019 · 2019
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Towards explainable nlp: A generative explanation framework for text classification
Hui Liu, Qingyu Yin, and William Yang Wang. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Earlier work this paper cites.
Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
Cited alongside, same era.
Hate speech detection and racial bias mitigation in social media based on bert model
Marzieh Mozafari, Reza Farahbakhsh, and Noël Crespi. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
Explanations for commonsenseqa: New dataset and models
Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, and Dinesh Garg. 2021 · 2021
Cited alongside, same era.
Latent hatred: A benchmark for understanding implicit hate speech
Mai ElSherief, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi, Jordyn Seybolt, Munmun De Choudhury, and Diyi Yang. 2021 · 2021
Cited alongside, same era.
Can language models learn from explanations in context?
Andrew K Lampinen, Ishita Dasgupta, Stephanie CY Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L McClelland, Jane X Wang, and Felix Hill. 2022 · 2022
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A systematic investigation of commonsense knowledge in large language models
Xiang Lorraine Li, Adhiguna Kuncoro, Jordan Hoffmann, Cyprien de Masson d’Autume, Phil Blunsom, and Aida Nematzadeh. 2022 · 2022
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Leveraging world knowledge in implicit hate speech detection
Jessica Lin. 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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Explaining toxic text via knowledge enhanced text generation
Rohit Sridhar and Diyi Yang. 2022 · 2022
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Few-shot self-rationalization with natural language prompts
Ana Marasović, Iz Beltagy, Doug Downey, and Matthew E Peters. 2021 · 2021
Cited alongside, same era.
Hatexplain: A benchmark dataset for explainable hate speech detection
Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee. 2021 · 2021
Cited alongside, same era.
Fbert: A neural transformer for identifying offensive content
Diptanu Sarkar, Marcos Zampieri, Tharindu Ranasinghe, and Alexander Ororbia. 2021 · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
Cited alongside, same era.
Measuring association between labels and free-text rationales
Sarah Wiegreffe, Ana Marasović, and Noah A Smith. 2021b · 2021
Cited alongside, same era.
ToKen: Task decomposition and knowledge infusion for few-shot hate speech detection
Badr AlKhamissi, Faisal Ladhak, Srinivasan Iyer, Veselin Stoyanov, Zornitsa Kozareva, Xian Li, Pascale Fung, Lambert Mathias, Asli Celikyilmaz, and Mona Diab. 2022 · 2022
Cited alongside, same era.
Large language models are reasoning teachers
Namgyu Ho, Laura Schmid, and Se-Young Yun. 2022 · 2022
Cited alongside, same era.
Later among the works it cites.
Investigating the benefits of free-form rationales
Jiao Sun, Swabha Swayamdipta, Jonathan May, and Xuezhe Ma. 2022 · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
Later among the works it cites.
Knife: Distilling meta-reasoning knowledge with free-text rationales
Aaron Chan, Zhiyuan Zeng, Wyatt Lake, Brihi Joshi, Hanjie Chen, and Xiang Ren. 2023 · 2023
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Zara: Improving few-shot self-rationalization for small language models
Wei-Lin Chen, An-Zi Yen, Hen-Hsen Huang, Cheng-Kuang Wu, and Hsin-Hsi Chen. 2023 · 2023
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Minje Choi, Jiaxin Pei, Sagar Kumar, Chang Shu, and David Jurgens. 2023 · 2023
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Gpteval: Nlg evaluation using gpt-4 with better human alignment
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023 · 2023
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Explanation-based finetuning makes models more robust to spurious cues
Josh Magnus Ludan, Yixuan Meng, Tai Nguyen, Saurabh Shah, Qing Lyu, Marianna Apidianaki, and Chris Callison-Burch. 2023 · 2023
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OpenAI. 2023 · 2023
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Flask: Fine-grained language model evaluation based on alignment skill sets
Seonghyeon Ye, Doyoung Kim, Sungdong Kim, Hyeonbin Hwang, Seungone Kim, Yongrae Jo, James Thorne, Juho Kim, and Minjoon Seo. 2023 · 2023
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