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Textual style expresses a diverse set of information, including interpersonal dynamics (e.g., formality) and the author's emotions or attitudes (e.g., disgust).
Content analysis: An introduction to its methodology
Klaus Krippendorff. 1980 · 1980
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The multifunctionality of discourse markers
Eduard H Hovy. 1995 · 1995
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017 · 2017
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Semeval-2017 task 4: Sentiment analysis in twitter
Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017 · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
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Dear sir or madam, may i introduce the gyafc dataset: Corpus, benchmarks and metrics for formality style transfer
Sudha Rao and Joel Tetreault. 2018 · 2018
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Semeval-2018 task 3: Irony detection in english tweets
Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2018 · 2018
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Reinforcement learning based text style transfer without parallel training corpus
Hongyu Gong, Suma Bhat, Lingfei Wu, Jinjun Xiong, and Wen mei Hwu. 2019 · 2019
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Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher. 2019 · 2019
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Tweeteval: Unified benchmark and comparative evaluation for tweet classification
Francesco Barbieri, Jose Camacho-Collados, Luis Espinosa Anke, and Leonardo Neves. 2020 · 2020
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Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2020 · 2020
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GoEmotions: A Dataset of Fine-Grained Emotions
Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko, Alan Cowen, Gaurav Nemade, and Sujith Ravi. 2020 · 2020
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Gedi: Generative discriminator guided sequence generation
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani. 2020 · 2020
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Fine-tuning language models from human preferences
Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2020 · 2020
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A general language assistant as a laboratory for alignment
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, et al. 2021 · 2021
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How helpful is inverse reinforcement learning for table-to-text generation?
Sayan Ghosh, Zheng Qi, Snigdha Chaturvedi, and Shashank Srivastava. 2021 · 2021
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Style is not a single variable: Case studies for cross-stylistic language understanding
Dongyeop Kang and Eduard Hovy. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Evaluate & evaluation on the hub: Better best practices for data and model measurements
Leandro Von Werra, Lewis Tunstall, Abhishek Thakur, Sasha Luccioni, Tristan Thrush, Aleksandra Piktus, Felix Marty, Nazneen Rajani, Victor Mustar, and Helen Ngo. 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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Analyzing the performance of gpt-3.5 and gpt-4 in grammatical error correction
Steven Coyne, Keisuke Sakaguchi, Diana Galvan-Sosa, Michael Zock, and Kentaro Inui. 2023 · 2023
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Balancing effect of training dataset distribution of multiple styles for multi-style text transfer
Debarati Das, David Ma, and Dongyeop Kang. 2023 · 2023
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Inverse reinforcement learning for text summarization
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Cross-replication reliability - an empirical approach to interpreting inter-rater reliability
Ka Wong, Praveen Paritosh, and Lora Aroyo. 2021 · 2021
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Attribute alignment: Controlling text generation from pre-trained language models
Dian Yu, Zhou Yu, and Kenji Sagae. 2021 · 2021
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LoRA: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
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Deep learning for text style transfer: A survey
Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, and Rada Mihalcea. 2022 · 2022
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Multi-attribute controlled text generation with contrastive-generator and external-discriminator
Guisheng Liu, Yi Li, Yanqing Guo, Xiangyang Luo, and Bo Wang. 2022 · 2022
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Peft: State-of-the-art parameter-efficient fine-tuning methods
Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, and Sayak Paul. 2022 · 2022
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On the impact of noises in crowd-sourced data for speech translation
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Yu Fu, Deyi Xiong, and Yue Dong. 2023 · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
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Activation addition: Steering language models without optimization
Alex Turner, Lisa Thiergart, David Udell, Gavin Leech, Ulisse Mini, and Monte MacDiarmid. 2023 · 2023
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Fine-grained human feedback gives better rewards for language model training
Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A. Smith, Mari Ostendorf, and Hannaneh Hajishirzi. 2023 · 2023
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A survey of controllable text generation using transformer-based pre-trained language models
Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, and Dawei Song. 2023 · 2023
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Style vectors for steering generative large language model
Kai Konen, Sophie Jentzsch, Diaoulé Diallo, Peer Schütt, Oliver Bensch, Roxanne El Baff, Dominik Opitz, and Tobias Hecking. 2024 · 2024
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Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
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Warm: On the benefits of weight averaged reward models
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