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Relevant bibliographies by topics / AI Generated Text Detection

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Journal articles

Academic literature on the topic 'AI Generated Text Detection'

Author: Grafiati

Published: 28 December 2024

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Journal articles on the topic "AI Generated Text Detection"

1

Bhattacharjee, Amrita, and Huan Liu. "Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?" ACM SIGKDD Explorations Newsletter 25, no. 2 (2024): 14–21. http://dx.doi.org/10.1145/3655103.3655106.

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Large language models (LLMs) such as ChatGPT are increasingly being used for various use cases, including text content generation at scale. Although detection methods for such AI-generated text exist already, we investigate ChatGPT's performance as a detector on such AI-generated text, inspired by works that use ChatGPT as a data labeler or annotator. We evaluate the zeroshot performance of ChatGPT in the task of human-written vs. AI-generated text detection, and perform experiments on publicly available datasets. We empirically investigate if ChatGPT is symmetrically effective in detecting AI

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2

Wang, Yu. "Survey for Detecting AI-generated Content." Advances in Engineering Technology Research 11, no. 1 (2024): 643. http://dx.doi.org/10.56028/aetr.11.1.643.2024.

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In large language models (LLMs) field, the rapid advancements have significantly improved text generation, which has blured the distinction between AI-generated and human-written texts. These developments have sparked concerns about potential risks, such as disseminating fake information or engaging in academic cheating. As the responsible use of LLMs becomes imperative, the detection of AI-generated content has become a crucial task. Most existing surveys on AI-generated text (AIGT) Detection have analysed the detection approaches from a computational perspective, with less attention to lingu

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3

A, Nykonenko. "How Text Transformations Affect AI Detection." Artificial Intelligence 29, AI.2024.29(4) (2024): 233–41. https://doi.org/10.15407/jai2024.04.233.

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This study addresses the critical issue of AI writing detection, which currently plays a key role in deterring technology misuse and proposes a foundation for the controllable and conscious use of AI. The ability to differentiate between human-written and AI-generated text is crucial for the practical application of any policies or guidelines. Current detection tools are unable to interpret their decisions in a way that is understandable to humans or provide any human-readable evidence or proof for their decisions. We assume that there should be a traceable footprint in LLM-generated texts tha

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4

Singh, Dr Viomesh, Bhavesh Agone, Aryan More, Aryan Mengawade, Atharva Deshmukh, and Atharva Badgujar. "SAVANA- A Robust Framework for Deepfake Video Detection and Hybrid Double Paraphrasing with Probabilistic Analysis Approach for AI Text Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 2074–83. http://dx.doi.org/10.22214/ijraset.2024.65526.

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Abstract: As the generative AI has advanced with a great speed, the need to detect AI-generated content, including text and deepfake media, also increased. This research work proposes a hybrid detection method that includes double paraphrasing-based consistency checks, coupled with probabilistic content analysis through natural language processing and machine learning algorithms for text and advanced deepfake detection techniques for media. Our system hybridizes the double paraphrasing framework of SAVANA with probabilistic analysis toward high accuracy on AI-text detection in forms such as DO

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5

Vismay Vora, Et al. "A Multimodal Approach for Detecting AI Generated Content using BERT and CNN." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 691–701. http://dx.doi.org/10.17762/ijritcc.v11i9.8861.

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With the advent of Generative AI technologies like LLMs and image generators, there will be an unprecedented rise in synthetic information which requires detection. While deepfake content can be identified by considering biological cues, this article proposes a technique for the detection of AI generated text using vocabulary, syntactic, semantic and stylistic features of the input data and detecting AI generated images through the use of a CNN model. The performance of these models is also evaluated and benchmarked with other comparative models. The ML Olympiad Competition dataset from Kaggle

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6

Subramaniam, Raghav. "Identifying Text Classification Failures in Multilingual AI-Generated Content." International Journal of Artificial Intelligence & Applications 14, no. 5 (2023): 57–63. http://dx.doi.org/10.5121/ijaia.2023.14505.

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With the rising popularity of generative AI tools, the nature of apparent classification failures by AI content detection softwares, especially between different languages, must be further observed. This paper aims to do this through testing OpenAI’s “AI Text Classifier” on a set of human and AI-generated texts inEnglish, German, Arabic, Hindi, Chinese, and Swahili. Given the unreliability of existing tools for detection of AIgenerated text, it is notable that specific types of classification failures often persist in slightly different ways when various languages are observed: misclassificati

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7

Sushma D S, Pooja C N, Varsha H S, Yasir Hussain, and P Yashash. "Detection and Classification of ChatGPT Generated Contents Using Deep Transformer Models." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 05 (2024): 1404–7. http://dx.doi.org/10.47392/irjaeh.2024.0193.

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AI advancements, particularly in neural networks, have brought about groundbreaking tools like text generators and chatbots. While these technologies offer tremendous benefits, they also pose serious risks such as privacy breaches, spread of misinformation, and challenges to academic integrity. Previous efforts to distinguish between human and AI-generated text have been limited, especially with models like ChatGPT. To tackle this, we created a dataset containing both human and ChatGPT-generated text, using it to train and test various machine and deep learning models. Your results, particular

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8

Alshammari, Hamed, and Khaled Elleithy. "Toward Robust Arabic AI-Generated Text Detection: Tackling Diacritics Challenges." Information 15, no. 7 (2024): 419. http://dx.doi.org/10.3390/info15070419.

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Current AI detection systems often struggle to distinguish between Arabic human-written text (HWT) and AI-generated text (AIGT) due to the small marks present above and below the Arabic text called diacritics. This study introduces robust Arabic text detection models using Transformer-based pre-trained models, specifically AraELECTRA, AraBERT, XLM-R, and mBERT. Our primary goal is to detect AIGTs in essays and overcome the challenges posed by the diacritics that usually appear in Arabic religious texts. We created several novel datasets with diacritized and non-diacritized texts comprising up

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9

Jeremie Busio Legaspi, Roan Joyce Ohoy Licuben, Emmanuel Alegado Legaspi, and Joven Aguinaldo Tolentino. "Comparing ai detectors: evaluating performance and efficiency." International Journal of Science and Research Archive 12, no. 2 (2024): 833–38. http://dx.doi.org/10.30574/ijsra.2024.12.2.1276.

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The widespread utilization of AI tools such as ChatGPT has become increasingly prevalent among learners, posing a threat to academic integrity. This study seeks to evaluate capability and efficiency of AI detection tools in distinguishing between human-authored and AI-generated works. Three-paragraph works on “AutoCAD and Architecture” were generated through ChatGPT, and three human-written works were subjected to evaluation. AI detection tools such as GPTZero, Copyleaks and Writer AI were used to evaluate these paragraphs. Parameters such as “Human/Human Text/Human Generated Text” and “AI/AI

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10

Kim, Min-Gyu, and Heather Desaire. "Detecting the Use of ChatGPT in University Newspapers by Analyzing Stylistic Differences with Machine Learning." Information 15, no. 6 (2024): 307. http://dx.doi.org/10.3390/info15060307.

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Large language models (LLMs) have the ability to generate text by stringing together words from their extensive training data. The leading AI text generation tool built on LLMs, ChatGPT, has quickly grown a vast user base since its release, but the domains in which it is being heavily leveraged are not yet known to the public. To understand how generative AI is reshaping print media and the extent to which it is being implemented already, methods to distinguish human-generated text from that generated by AI are required. Since college students have been early adopters of ChatGPT, we sought to

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