can-large-language-models-make-the-grade
https://github.com/owenhenkel/can-large-language-models-make-the-grade
Science Score: 54.0%
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✓DOI references
Found 3 DOI reference(s) in README -
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Links to: arxiv.org, acm.org -
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○Scientific vocabulary similarity
Low similarity (4.6%) to scientific vocabulary
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Metadata Files
README.md
This repo contains the dataset and related materials associated with the paper:
Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability To Mark Short Answer Questions in K-12 Education
This dataset is available for use for non-commercial uses only, with attribution (CC-BY-NC 4.0)
Final Paper : https://dl.acm.org/doi/10.1145/3657604.3664693
Pre-Print: https://arxiv.org/abs/2405.02985
To Cite
Owen Henkel, Libby Hills, Adam Boxer, Bill Roberts, and Zach Levonian. 2024. Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability To Mark Short Answer Questions in K-12 Education. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S '24). Association for Computing Machinery, New York, NY, USA, 300–304. https://doi.org/10.1145/3657604.3664693
Owner
- Name: owen
- Login: owenhenkel
- Kind: user
- Repositories: 1
- Profile: https://github.com/owenhenkel
Citation (citation)
Owen Henkel, Libby Hills, Adam Boxer, Bill Roberts, and Zach Levonian. 2024. Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability To Mark Short Answer Questions in K-12 Education. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S '24). Association for Computing Machinery, New York, NY, USA, 300–304. https://doi.org/10.1145/3657604.3664693
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