Knowledge Distillation of LLMs for Automatic Scoring of Science Assessments

Author:

Latif Ehsan,Fang Luyang,Ma Ping,Zhai Xiaoming

Publisher

Springer Nature Switzerland

Reference14 articles.

1. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

2. ETS-MTS, T.: Learning progression-based and ngss-aligned formative assessment for using mathematical thinking in science, November 2023. http://ets-cls.org/mts/index.php/assessment/

3. Ghiassi, M., Olschimke, M., Moon, B., Arnaudo, P.: Automated text classification using a dynamic artificial neural network model. Expert Syst. Appl. 39(12), 10967–10976 (2012)

4. González-Calatayud, V., Prendes-Espinosa, P., Roig-Vila, R.: Artificial intelligence for student assessment: a systematic review. Appl. Sci. 11(12), 5467 (2021)

5. Hinton, G., Vinyals, O., Dean, J., et al.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)

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