@inproceedings{stuhlinger-winkler-2024-tuecicl,
title = "{T}ue{CICL} at {S}em{E}val-2024 Task 8: Resource-efficient approaches for machine-generated text detection",
author = "Stuhlinger, Daniel and
Winkler, Aron",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.227",
doi = "10.18653/v1/2024.semeval-1.227",
pages = "1597--1601",
abstract = "Recent developments in the field of NLP have brought large language models (LLMs) to the forefront of both public and research attention. As the use of language generation technologies becomes more widespread, the problem arises of determining whether a given text is machine generated or not. Task 8 at SemEval 2024 consists of a shared task with this exact objective. Our approach aims at developing models and strategies that strike a good balance between performance and model size. We show that it is possible to compete with large transformer-based solutions with smaller systems.",
}
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<abstract>Recent developments in the field of NLP have brought large language models (LLMs) to the forefront of both public and research attention. As the use of language generation technologies becomes more widespread, the problem arises of determining whether a given text is machine generated or not. Task 8 at SemEval 2024 consists of a shared task with this exact objective. Our approach aims at developing models and strategies that strike a good balance between performance and model size. We show that it is possible to compete with large transformer-based solutions with smaller systems.</abstract>
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%0 Conference Proceedings
%T TueCICL at SemEval-2024 Task 8: Resource-efficient approaches for machine-generated text detection
%A Stuhlinger, Daniel
%A Winkler, Aron
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F stuhlinger-winkler-2024-tuecicl
%X Recent developments in the field of NLP have brought large language models (LLMs) to the forefront of both public and research attention. As the use of language generation technologies becomes more widespread, the problem arises of determining whether a given text is machine generated or not. Task 8 at SemEval 2024 consists of a shared task with this exact objective. Our approach aims at developing models and strategies that strike a good balance between performance and model size. We show that it is possible to compete with large transformer-based solutions with smaller systems.
%R 10.18653/v1/2024.semeval-1.227
%U https://aclanthology.org/2024.semeval-1.227
%U https://doi.org/10.18653/v1/2024.semeval-1.227
%P 1597-1601
Markdown (Informal)
[TueCICL at SemEval-2024 Task 8: Resource-efficient approaches for machine-generated text detection](https://aclanthology.org/2024.semeval-1.227) (Stuhlinger & Winkler, SemEval 2024)
ACL