Cite Them Right 11th edition - Harvard

Ruan, Q., Kuznetsov, I. und Gurevych, I. (2024) „Are large language models good classifiers? A study on edit intent classification in scientific document revisions“, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, ((2024), Seite 15049–15067), S. , Seite 15049-. doi:10.18653/v1/2024.emnlp-main.839.

Chicago Manual of Style 17th edition (full note)

Ruan, Qian, Ilia Kuznetsov, und Iryna Gurevych. „Are large language models good classifiers? A study on edit intent classification in scientific document revisions“. Electronic. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Nr. (2024), Seite 15049–15067 ([2024?]): , Seite 15049-. https://doi.org/10.18653/v1/2024.emnlp-main.839.

American Psychological Association 7th edition

Ruan, Q., Kuznetsov, I., & Gurevych, I. (ca. 2024). Are large language models good classifiers? A study on edit intent classification in scientific document revisions [Electronic]. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, (2024), Seite 15049–15067, , Seite 15049-. https://doi.org/10.18653/v1/2024.emnlp-main.839

Modern Language Association 9th edition

Ruan, Q., I. Kuznetsov, und I. Gurevych. „Are large language models good classifiers? A study on edit intent classification in scientific document revisions“. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, electronic, Nr. (2024), Seite 15049–15067, 2024, S. , Seite 15049-, https://doi.org/10.18653/v1/2024.emnlp-main.839.

ISO-690 (author-date, Deutsch)

RUAN, Qian, Ilia KUZNETSOV und Iryna GUREVYCH, 2024. Are large language models good classifiers? A study on edit intent classification in scientific document revisions. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024. Nr. (2024), Seite 15049–15067, S. , Seite 15049-. DOI 10.18653/v1/2024.emnlp-main.839

Achtung: Diese Zitate sind unter Umständen nicht zu 100% korrekt.