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This paper studies the performance of open-source Large Language Models (LLMs) in text classification tasks typical for political science research.
“Language models are few-shot learners.” Advances in neural information processing systems
Brown, Tom, 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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The media frames corpus: Annotations of frames across issues. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Card, Dallas, Amber Boydstun, Justin H Gross, Philip Resnik and Noah A Smith. 2015 · 2015
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“Universal language model fine-tuning for text classification.” arXiv:1801.06146
Howard, Jeremy and Sebastian Ruder. 2018 · 2018
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“TRL: Transformer Reinforcement Learning.” https://github.com/huggingface/trl
von Werra, Leandro, Younes Belkada, Lewis Tunstall, Edward Beeching, Tristan Thrush, Nathan Lambert and Shengyi Huang. 2020 · 2020
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Structured Pruning of Large Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Wang, Ziheng, Jeremy Wohlwend and Tao Lei. 2020 · 2020
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“Automated text classification of news articles: A practical guide.” Political Analysis
Barberá, Pablo, Amber E Boydstun, Suzanna Linn, Ryan McMahon and Jonathan Nagler. 2021 · 2021
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“Towards a unified view of parameter-efficient transfer learning.” arXiv:2110.04366
He, Junxian, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick and Graham Neubig. 2021 · 2021
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“Lora: Low-rank adaptation of large language models.” arXiv:2106.09685
Hu, Edward J, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang and Weizhu Chen. 2021 · 2021
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“Privacy and artificial intelligence: challenges for protecting health information in a new era.” BMC Medical Ethics
Murdoch, Blake. 2021 · 2021
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“Can Large Language Models Transform Computational Social Science?” arXiv:2305.03514
Ziems, Caleb, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang and Diyi Yang. 2023 · 2021
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“Content Moderation As a Political Issue: The Twitter Discourse Around Trump’s Ban.” Journal of Quantitative Description: Digital Media
Alizadeh, Meysam, Fabrizio Gilardi, Emma Hoes, K Jonathan Klüser, Mael Kubli and Nahema Marchal. 2022 · 2022
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“Scaling instruction-finetuned language models.” arXiv:2210.11416
Chung, Hyung Won, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma et al. 2022 · 2022
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“Training compute-optimal large language models.” arXiv:2203.15556
Hoffmann, Jordan, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark et al. 2022 · 2022
Earlier work this paper cites.
“Large language models are zero-shot reasoners.” arXiv:2205.11916
Kojima, Takeshi, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo and Yusuke Iwasawa. 2022 · 2022
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Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems
Ouyang, Long, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike and Ryan Lowe. 2022 · 2022
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“Finetuned Language Models Are Zero-Shot Learners.”
Wei, Jason, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai and Quoc V. Le. 2022 · 2022
Cited alongside, same era.
“Chain of thought prompting elicits reasoning in large language models.” arXiv:2201.11903
Wei, Jason, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le and Denny Zhou. 2022 · 2022
Cited alongside, same era.
“Turning large language models into cognitive models.” arXiv:2306.03917
Binz, Marcel and Eric Schulz. 2023 · 2023
Cited alongside, same era.
“QLoRA: Efficient Finetuning of Quantized LLMs.”
Dettmers, Tim, Artidoro Pagnoni, Ari Holtzman and Luke Zettlemoyer. 2023 · 2023
Cited alongside, same era.
Is GPT-3 a Good Data Annotator? In Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics
Ding, Bosheng, Chengwei Qin, Linlin Liu, Yew Ken Chia, Shafiq Joty, Boyang Li and Lidong Bing. 2023 · 2023
“Why open-source generative AI models are an ethical way forward for science.” Nature
Spirling, Arthur. 2023 · 2023
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“ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning.”
Törnberg, Petter. 2023 a · 2023
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Törnberg, Petter. 2023 b · 2023
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“Llama 2: Open foundation and fine-tuned chat models.” arXiv:2307.09288
Touvron, Hugo, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale et al. 2023 · 2023
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“ChatGPT: five priorities for research.” Nature
Van Dis, Eva AM, Johan Bollen, Willem Zuidema, Robert van Rooij and Claudi L Bockting. 2023 · 2023
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Frei, Johann and Frank Kramer. 2023 · 2023
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Gilardi, Fabrizio, Meysam Alizadeh and Maël Kubli. 2023 · 2023
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“OpenAssistant Conversations – Democratizing Large Language Model Alignment.”
Köpf, Andreas, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi-Rui Tam, Keith Stevens, Abdullah Barhoum, Nguyen Minh Duc, Oliver Stanley, Richárd Nagyfi, Shahul ES, Sameer Suri, David Glushkov, Arnav Dantuluri, Andrew Maguire, Christoph Schuhmann, Huu Nguyen and Alexander Mattick. 2023 · 2023
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Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators. In Proceedings of the 5th International Conference on Conversational User Interfaces
Liesenfeld, Andreas, Alianda Lopez and Mark Dingemanse. 2023 · 2023
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“Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.” ACM Computing Surveys
Liu, Pengfei, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi and Graham Neubig. 2023 · 2023
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“Harnessing the power of llms in practice: A survey on chatgpt and beyond.” arXiv:2304.13712
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“Qlora: Efficient finetuning of quantized llms.” Advances in Neural Information Processing Systems
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“The dangers of using proprietary LLMs for research.” Nature Machine Intelligence
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