Implementing BERT and fine-tuned RobertA to detect AI generated news by ChatGPT

2023-06-09 · Zecong Wang, Jiaxi Cheng, Chen Cui, Chenhao Yu

The abundance of information on social media has increased the necessity of accurate real-time rumour detection. Manual techniques of identifying and verifying fake news generated by AI tools are impracticable and time-consuming given the enormous volume of information generated every day. This has sparked an increase in interest in creating automated systems to find fake news on the Internet. The studies in this research demonstrate that the BERT and RobertA models with fine-tuning had the best success in detecting AI generated news. With a score of 98%, tweaked RobertA in particular showed excellent precision. In conclusion, this study has shown that neural networks can be used to identify bogus news AI generation news created by ChatGPT. The RobertA and BERT models' excellent performance indicates that these models can play a critical role in the fight against misinformation.

📄 PDF Abstract BibTeX arXiv:2306.07401

Code (0)

등록된 구현이 없습니다.

Tasks

Misinformation Rumour Detection

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음

Attention 설명 없음

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…

Weight Decay 설명 없음

Residual Connection 설명 없음

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Adam 설명 없음

LuxVeri at GenAI Detection Task 3: Cross-Domain Detection of AI-Generated Text Using Inverse Perplexity-Weighted Ensemble of Fine-Tuned Transformer Models

2025-01-21 · Md Kamrujjaman Mobin, Md Saiful Islam

This paper presents our approach for Task 3 of the GenAI content detection workshop at COLING-2025, focusing on Cross-Domain Machine-Generated Text (MGT) Detection. We propose an ensemble of fine-tuned transformer models…

OPI@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text using RoBERTa Pre-trained Language Models

2022-05-01 · LTEDI (ACL) 2022 5 · Rafał Poświata, Michał Perełkiewicz

This paper presents our winning solution for the Shared Task on Detecting Signs of Depression from Social Media Text at LT-EDI-ACL2022. The task was to create a system that, given social media posts in English, should de…

Depression Detection Language Modeling Language Modelling

multiMentalRoBERTa: A Fine-tuned Multiclass Classifier for Mental Health Disorder

2025-11-01 · K M Sajjadul Islam, John Fields, Praveen Madiraju arxiv

The early detection of mental health disorders from social media text is critical for enabling timely support, risk assessment, and referral to appropriate resources. This work introduces multiMentalRoBERTa, a fine-tuned…

Felix&Julia at SemEval-2022 Task 4: Patronizing and Condescending Language Detection

2022-07-01 · SemEval (NAACL) 2022 7 · Felix Herrmann, Julia Krebs

This paper describes the authors’ submission to the SemEval-2022 task 4: Patronizing and Condescending Language (PCL) Detection. The aim of the task is the detection and classification of PCL in an annotated dataset. Sub…

Binary Classification Classification Multi-Label Classification MUlTI-LABEL-ClASSIFICATION

LeCun at SemEval-2021 Task 6: Detecting Persuasion Techniques in Text Using Ensembled Pretrained Transformers and Data Augmentation

2021-08-01 · SEMEVAL 2021 · Dia Abujaber, Ahmed Qarqaz, Malak A. Abdullah

We developed a system for task 6 sub-task 1 for detecting propaganda in memes. An external dataset and augmentation data-set were used to extend the official competition data-set. Data augmentation techniques were applie…

Data Augmentation