Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text

• URL: http://arxiv.org/abs/2407.11774v1
• Date: Tue, 16 Jul 2024 14:33:01 GMT
• Title: Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text
• Authors: Seyedeh Fatemeh Ebrahimi, Karim Akhavan Azari, Amirmasoud Iravani, Arian Qazvini, Pouya Sadeghi, Zeinab Sadat Taghavi, Hossein Sameti,
• Abstract summary: MGT has emerged as a significant area of study within Natural Language Processing. In this research, we explore the effectiveness of fine-tuning a RoBERTa-base transformer, a powerful neural architecture, to address MGT detection. Our proposed system achieves an accuracy of 78.9% on the test dataset, positioning us at 57th among participants.
• Score: 2.2039952888743253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting Machine-Generated Text (MGT) has emerged as a significant area of study within Natural Language Processing. While language models generate text, they often leave discernible traces, which can be scrutinized using either traditional feature-based methods or more advanced neural language models. In this research, we explore the effectiveness of fine-tuning a RoBERTa-base transformer, a powerful neural architecture, to address MGT detection as a binary classification task. Focusing specifically on Subtask A (Monolingual-English) within the SemEval-2024 competition framework, our proposed system achieves an accuracy of 78.9% on the test dataset, positioning us at 57th among participants. Our study addresses this challenge while considering the limited hardware resources, resulting in a system that excels at identifying human-written texts but encounters challenges in accurately discerning MGTs.

Related papers

• Detecting Machine-Generated Long-Form Content with Latent-Space Variables [54.07946647012579]
  Existing zero-shot detectors primarily focus on token-level distributions, which are vulnerable to real-world domain shifts. We propose a more robust method that incorporates abstract elements, such as event transitions, as key deciding factors to detect machine versus human texts.
  arXiv  Detail & Related papers  (2024-10-04T18:42:09Z)
• Mast Kalandar at SemEval-2024 Task 8: On the Trail of Textual Origins: RoBERTa-BiLSTM Approach to Detect AI-Generated Text [7.959800630494841]
  SemEval 2024 introduces the task of Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. We propose a RoBERTa-BiLSTM based classifier designed to classify text into two categories: AI-generated or human. Our architecture ranked 46th on the official leaderboard with an accuracy of 80.83 among 125.
  arXiv  Detail & Related papers  (2024-07-03T10:22:23Z)
• SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection [68.858931667807]
  Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. Subtask B is to detect the exact source of a text, discerning whether it is written by a human or generated by a specific LLM. Subtask C aims to identify the changing point within a text, at which the authorship transitions from human to machine.
  arXiv  Detail & Related papers  (2024-04-22T13:56:07Z)
• PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text? [4.463184061618504]
  We present our submission to the SemEval-2024 Task 8 "Multigenerator, Multidomain, and Black-Box Machine-Generated Text Detection" Our approach relies on combining embeddings from the RoBERTa-base with diversity features and uses a resampled training set. Our results show that our approach is generalizable across unseen models and domains, achieving an accuracy of 0.91.
  arXiv  Detail & Related papers  (2024-04-08T13:05:02Z)
• TrustAI at SemEval-2024 Task 8: A Comprehensive Analysis of Multi-domain Machine Generated Text Detection Techniques [2.149586323955579]
  Large Language Models (LLMs) generate fluent content across a wide spectrum of user queries. This capability has raised concerns regarding misinformation and personal information leakage. We present our methods for the SemEval2024 Task8, aiming to detect machine-generated text across various domains.
  arXiv  Detail & Related papers  (2024-03-25T10:09:03Z)
• Retrieval is Accurate Generation [99.24267226311157]
  We introduce a novel method that selects context-aware phrases from a collection of supporting documents. Our model achieves the best performance and the lowest latency among several retrieval-augmented baselines.
  arXiv  Detail & Related papers  (2024-02-27T14:16:19Z)
• KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection [0.0]
  SemEval-2024 Task 8 is focused on multigenerator, multidomain, and multilingual black-box machine-generated text detection. Our submitted method achieved competitive results, ranking at the fourth place, just under 1 percentage point behind the winner.
  arXiv  Detail & Related papers  (2024-02-21T10:09:56Z)
• M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection [69.41274756177336]
  Large Language Models (LLMs) have brought an unprecedented surge in machine-generated text (MGT) across diverse channels. This raises legitimate concerns about its potential misuse and societal implications. We introduce a new benchmark based on a multilingual, multi-domain, and multi-generator corpus of MGTs -- M4GT-Bench.
  arXiv  Detail & Related papers  (2024-02-17T02:50:33Z)
• MGTBench: Benchmarking Machine-Generated Text Detection [54.81446366272403]
  This paper proposes the first benchmark framework for MGT detection against powerful large language models (LLMs) We show that a larger number of words in general leads to better performance and most detection methods can achieve similar performance with much fewer training samples. Our findings indicate that the model-based detection methods still perform well in the text attribution task.
  arXiv  Detail & Related papers  (2023-03-26T21:12:36Z)
• DIALOG-22 RuATD Generated Text Detection [0.0]
  Detectors that can distinguish between TGM-generated text and human-written ones play an important role in preventing abuse of TGM. We describe our pipeline for the two DIALOG-22 RuATD tasks: detecting generated text (binary task) and classification of which model was used to generate text.
  arXiv  Detail & Related papers  (2022-06-16T09:33:26Z)
• Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for Offensive Language Detection [55.445023584632175]
  We build an offensive language detection system, which combines multi-task learning with BERT-based models. Our model achieves 91.51% F1 score in English Sub-task A, which is comparable to the first place.
  arXiv  Detail & Related papers  (2020-04-28T11:27:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.