Efficient Plagiarism Detection System Using BERT and Faiss

Core Concepts

A plagiarism detection system is proposed that efficiently retrieves and identifies different types of plagiarism in text using BERT and Faiss.

Abstract

The content describes the development of a plagiarism detection system that addresses the challenges of identifying complex forms of plagiarism, such as imitation plagiarism and creative plagiarism.
Key highlights:

The system uses BERT for text feature representation and an MLP-based classifier for plagiarism identification.
To efficiently retrieve potentially plagiarized texts, the system leverages the Faiss framework for fast similarity search.
A large-scale plagiarized text dataset is generated using GPT-3.5 to cover diverse plagiarism methods, addressing the lack of high-quality datasets in this area.
Experiments show the proposed system outperforms other models, achieving high accuracy, precision, recall, and F1 score on the generated dataset.
A user-friendly demo platform is provided to allow users to upload text libraries and perform plagiarism analysis.
The authors also discuss the limitations of the SBERT model in distinguishing between different degrees of plagiarism and provide insights into potential improvements.

Stats

The dataset contains 32,927 text pairs of the form (t1, t2, label), where t1 is the original text, t2 is the text to be detected, and label denotes the type of plagiarism of t2 on t1.

Quotes

"LLMs process a large amount of textual data in the pre-training phase, including a variety of literary works, academic papers, and web content. This enables them to learn and understand a variety of linguistic expressions, including different writing styles and language structures."
"FAISS provides crucial support in the preliminary screening of assignment submissions, employing efficient similarity search algorithms."

Key Insights Distilled From

BERT-Enhanced Retrieval Tool for Homework Plagiarism Detection System

by Jiarong Xian... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01582.pdf

Deeper Inquiries

How can the proposed system be further improved to better distinguish between different degrees of plagiarism, such as imitation plagiarism and creative plagiarism?

To enhance the system's ability to differentiate between various degrees of plagiarism, particularly imitation and creative plagiarism, several improvements can be considered:

Fine-tuning Models: Fine-tuning the existing models with additional data specifically focused on imitation and creative plagiarism could help the system better understand the nuances and differences between these types of plagiarism.

Feature Engineering: Introducing more sophisticated features that capture not only the semantic but also the syntactic and stylistic aspects of the text could aid in distinguishing between different degrees of plagiarism.

Ensemble Models: Implementing ensemble models that combine the strengths of multiple models, such as BERT with other advanced models like RoBERTa or XLNet, could potentially improve the system's overall performance in detecting different types of plagiarism.

Semantic Analysis: Incorporating more advanced semantic analysis techniques that go beyond simple word embeddings to capture deeper contextual and structural similarities between texts could be beneficial.

Prompt Design: Refining the prompts used for data generation to specifically target imitation and creative plagiarism scenarios could provide the system with more diverse and relevant training data.

How can the generated dataset be expanded or diversified to better represent real-world plagiarism scenarios in various academic and professional domains?

Expanding and diversifying the generated dataset to better reflect real-world plagiarism scenarios across different domains can be achieved through the following strategies:

Domain-specific Data: Collecting and incorporating text data from various academic disciplines and professional fields to ensure the dataset covers a wide range of topics and writing styles.

Multilingual Data: Including texts in multiple languages to address plagiarism detection challenges in multilingual environments and cater to a more diverse user base.

Varied Plagiarism Techniques: Introducing a broader range of plagiarism techniques beyond the ones currently covered, such as paraphrasing, citation manipulation, and content spinning, to make the dataset more comprehensive.

Real Case Studies: Incorporating real case studies of documented plagiarism incidents from academic institutions and professional settings to provide a more realistic and practical dataset for training and evaluation.

Ethical Considerations: Ensuring that the dataset is ethically sourced and annotated, taking into account privacy and legal considerations when using real-world examples of plagiarism.

What other techniques or models could be explored to enhance the plagiarism detection capabilities beyond the current BERT-based approach?

To further enhance plagiarism detection capabilities beyond the current BERT-based approach, the following techniques and models could be explored:

Graph-based Methods: Utilizing graph-based algorithms to represent text similarity and relationships, which can capture more complex patterns and structures in the data for improved detection accuracy.

Neural Network Architectures: Exploring advanced neural network architectures like Transformer-XL, GPT-4, or T5, which may offer enhanced capabilities in understanding and analyzing textual data for plagiarism detection.

Semi-supervised Learning: Implementing semi-supervised learning techniques to leverage both labeled and unlabeled data, potentially improving the model's performance by utilizing a larger pool of training samples.

Cross-lingual Models: Investigating cross-lingual models that can handle plagiarism detection in multiple languages simultaneously, catering to a global audience and diverse text sources.

Adversarial Training: Incorporating adversarial training methods to make the model more robust against adversarial attacks and ensure its effectiveness in detecting sophisticated forms of plagiarism.

By exploring these alternative techniques and models, the plagiarism detection system can be further refined and optimized to achieve higher accuracy and reliability in identifying various forms of plagiarism across different contexts.