Effectiveness of large language models in automated evaluation of argumentative essays: finetuning vs. zero-shot prompting

Author:

Wang Qiao1,Gayed John Maurice2

Affiliation:

1. Center for English Language Education, Faculty of Science and Engineering (CELESE), Waseda University, Shinjuku-ku, Japan

2. Global Education Center (GEC), Waseda University, Shinjuku-ku, Japan

Funder

Telecommunications Advancement Foundation

Japan Society for the Promotion of Science

Publisher

Informa UK Limited

Reference73 articles.

1. Using automated written corrective feedback in the writing classrooms: effects on L2 writing accuracy

2. Bengio, Y., Ducharme, R., & Vincent, P. (2000). A neural probabilistic language model. Journal of Machine Learning Research, 3, 1137–1155.

3. TOEFL11: A CORPUS OF NON-NATIVE ENGLISH

4. Bridgeman, B., Trapani, C., & Williamson, D. (2011). The question of validity of automated essay scores and differentially valued evidence. Annual Meeting of the National Council on Measurement in Education, New orleans.

5. Language models are few-shot learners;Brown T. B.;Advances in Neural Information Processing Systems,2020

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