@inproceedings{bafna-etal-2024-mast,
title = "Mast Kalandar at {S}em{E}val-2024 Task 8: On the Trail of Textual Origins: {R}o{BERT}a-{B}i{LSTM} Approach to Detect {AI}-Generated Text",
author = "Bafna, Jainit and
Mittal, Hardik and
Sethia, Suyash and
Shrivastava, Manish and
Mamidi, Radhika",
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-1.231",
doi = "10.18653/v1/2024.semeval-1.231",
pages = "1627--1633",
abstract = "Large Language Models (LLMs) have showcased impressive abilities in generating fluent responses to diverse user queries. However, concerns regarding the potential misuse ofsuch texts in journalism, educational, and academic contexts have surfaced. SemEval 2024introduces the task of Multigenerator, Multidomain, and Multilingual Black-Box MachineGenerated Text Detection, aiming to developautomated systems for identifying machinegenerated text and detecting potential misuse. In this paper, we i) propose a RoBERTaBiLSTM based classifier designed to classifytext into two categories: AI-generated or human ii) conduct a comparative study of ourmodel with baseline approaches to evaluate itseffectiveness. This paper contributes to the advancement of automatic text detection systemsin addressing the challenges posed by machinegenerated text misuse. Our architecture ranked46th on the official leaderboard with an accuracy of 80.83 among 125.",
}
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<abstract>Large Language Models (LLMs) have showcased impressive abilities in generating fluent responses to diverse user queries. However, concerns regarding the potential misuse ofsuch texts in journalism, educational, and academic contexts have surfaced. SemEval 2024introduces the task of Multigenerator, Multidomain, and Multilingual Black-Box MachineGenerated Text Detection, aiming to developautomated systems for identifying machinegenerated text and detecting potential misuse. In this paper, we i) propose a RoBERTaBiLSTM based classifier designed to classifytext into two categories: AI-generated or human ii) conduct a comparative study of ourmodel with baseline approaches to evaluate itseffectiveness. This paper contributes to the advancement of automatic text detection systemsin addressing the challenges posed by machinegenerated text misuse. Our architecture ranked46th on the official leaderboard with an accuracy of 80.83 among 125.</abstract>
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%0 Conference Proceedings
%T Mast Kalandar at SemEval-2024 Task 8: On the Trail of Textual Origins: RoBERTa-BiLSTM Approach to Detect AI-Generated Text
%A Bafna, Jainit
%A Mittal, Hardik
%A Sethia, Suyash
%A Shrivastava, Manish
%A Mamidi, Radhika
%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 bafna-etal-2024-mast
%X Large Language Models (LLMs) have showcased impressive abilities in generating fluent responses to diverse user queries. However, concerns regarding the potential misuse ofsuch texts in journalism, educational, and academic contexts have surfaced. SemEval 2024introduces the task of Multigenerator, Multidomain, and Multilingual Black-Box MachineGenerated Text Detection, aiming to developautomated systems for identifying machinegenerated text and detecting potential misuse. In this paper, we i) propose a RoBERTaBiLSTM based classifier designed to classifytext into two categories: AI-generated or human ii) conduct a comparative study of ourmodel with baseline approaches to evaluate itseffectiveness. This paper contributes to the advancement of automatic text detection systemsin addressing the challenges posed by machinegenerated text misuse. Our architecture ranked46th on the official leaderboard with an accuracy of 80.83 among 125.
%R 10.18653/v1/2024.semeval-1.231
%U https://aclanthology.org/2024.semeval-1.231
%U https://doi.org/10.18653/v1/2024.semeval-1.231
%P 1627-1633
Markdown (Informal)
[Mast Kalandar at SemEval-2024 Task 8: On the Trail of Textual Origins: RoBERTa-BiLSTM Approach to Detect AI-Generated Text](https://aclanthology.org/2024.semeval-1.231) (Bafna et al., SemEval 2024)
ACL