Detection Method for ChatGPT-Generated Scientific Text

Khái niệm cốt lõi

AI-Catcher is a novel method to detect ChatGPT-generated scientific text, integrating MLP and CNN models to improve accuracy significantly.

Tóm tắt

The research introduces AI-Catcher, a model to distinguish between human-written and ChatGPT-generated scientific text. A new dataset, AIGTxt, enhances AI-generated text detection tools. Experiments show AI-Catcher outperforms baseline methods and commercial tools in accuracy, precision, recall, and F1-score.
Several plagiarism detection methods are reviewed along with machine-generated text detection approaches. The study emphasizes the importance of maintaining integrity in academic literature amidst AI advancements.
Key features of AI-Catcher include linguistic/statistical feature extraction by MLP, sequential pattern extraction by CNN, and fusion of hidden patterns. Results demonstrate the effectiveness of AI-Catcher in detecting ChatGPT-generated content accurately.

Thống kê

On average, AI-Catcher improved accuracy by 37.4%.
AIGTxt contains 3000 records across ten domains.
The average paragraph length in AIGTxt is approximately 194 words.
The dataset encompasses a rich vocabulary of 29,226 unique words.
Accuracy results achieved by AI-Catcher range from 2.65% to 89.19% improvement over baseline methods.

Trích dẫn

"AI-powered tools have the potential to generate scientific content indistinguishable from human-written work."
"AI-Catcher significantly improves accuracy in distinguishing between human-written and ChatGPT-generated texts."

Thông tin chi tiết chính được chắt lọc từ

Deep Learning Detection Method for Large Language Models-Generated Scientific Content

by Bushra Alhij... lúc arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.00828.pdf

Yêu cầu sâu hơn

How can AI advancements like ChatGPT impact the future of academic integrity?

AI advancements like ChatGPT can significantly impact the future of academic integrity by posing challenges related to plagiarism detection and content authenticity. These advanced language models have the capability to generate text that closely resembles human-written content, making it difficult to distinguish between AI-generated and human-created work. This poses a threat to academic integrity as it becomes easier for individuals to submit plagiarized or fabricated work without being detected by traditional methods.
Furthermore, the widespread use of AI in generating scientific content raises concerns about research credibility and reliability. If not properly regulated or monitored, there is a risk that false information could be disseminated within academic circles, leading to misinformation and undermining the trust in scholarly publications.
To address these challenges, institutions and researchers need to adapt their practices for verifying the authenticity of content generated by AI tools. New strategies for plagiarism detection specific to AI-generated text may need to be developed, along with enhanced educational programs on ethical writing practices.

How can ethical considerations should be addressed when using AI for content generation?

When utilizing AI for content generation, several ethical considerations must be taken into account:

Transparency: It is essential to disclose when content has been generated by an AI system rather than a human author.

Attribution: Proper credit should be given if an AI system has contributed significantly to creating a piece of work.

Bias: Care must be taken to ensure that biases present in training data do not perpetuate discriminatory or unethical outcomes in generated content.

Plagiarism: Steps should be taken to prevent using AI tools for producing plagiarized material intentionally.

Data Privacy: Safeguarding user data used in training these systems is crucial; privacy regulations must be adhered t

How can the development of tools like AI-Catcher influence educational practices regarding plagiarism detection?

The development of tools like AI-Catcher can revolutionize educational practices concerning plagiarism detection in several ways:

Enhanced Detection Accuracy: Tools like AICatcher leverage deep learning algorithms capable of accurately distinguishing between human-written and machine-generated text, improving overall detection accuracy.

Efficiency: Automated systems such as AICatcher streamline the process of identifying potential instances oplagiarism,dramatically reducing time spent on manual verification tasks.

3Educational Resource: By incorporating advanced technologieslikeAICatcheintoeducationalcurriculums,institutionscanbettereducatestudentsontheimportanceofacademicintegrityandpropercitationpracticesusingreal-worldexamplesandcutting-edgetools
4**Adaptability:AsAIadvances,itcanevolvetoaddressnewformsofplagiarismandcontentfabrication,enablingeducationalsystemstokeeppacewithemergingchallengesinthedigitalage