Prompt-based and Fine-tuned GPT Models for Context-Dependent and -Independent Deductive Coding in Social Annotation

Author:

Hou Chenyu1ORCID,Zhu Gaoxia1ORCID,Zheng Juan2ORCID,Zhang Lishan3ORCID,Huang Xiaoshan4ORCID,Zhong Tianlong1ORCID,Li Shan2ORCID,Du Hanxiang5ORCID,Ker Chin Lee6ORCID

Affiliation:

1. Nanyang Technological University, Singapore

2. Lehigh University, USA

3. Central China Normal University, China

4. McGill University, Canada

5. Western Washington University, USA

6. National Institute of Education (NIE), Nanyang Technological University, Singapore

Funder

NTU Edex Teaching and Learning Grants

Publisher

ACM

Reference56 articles.

1. Building Community in Asynchronous Online Higher Education Courses Through Collaborative Annotation

2. Understanding topic duration in Twitter learning communities using data mining

3. Yejin Bang Samuel Cahyawijaya Nayeon Lee Wenliang Dai Dan Su Bryan Wilie Holy Lovenia Ziwei Ji Tiezheng Yu Willy Chung Quyet V. Do Yan Xu and Pascale Fung. 2023. A Multitask Multilingual Multimodal Evaluation of ChatGPT on Reasoning Hallucination and Interactivity. https://doi.org/10.48550/arXiv.2302.04023

4. Perusall (www.perusall.com): Read, connect, discuss!

5. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems 33, (2020), 1877–1901.

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