OpenAI ChatGPT Generated Content and Similarity Index: A study of selected terms from the Library &amp; Information Science (LIS)

Kirtania, Deep Kumar; Patra, Swapan Kumar

doi:10.32388/fo1cp6.3

Cited by 8 publications

(4 citation statements)

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“…This provides children with the most advanced way of cheating school assignments, and it is currently unclear whether or not AI use is detectable by traditional plagiarism software. One study has noted that ChatGPT can paraphrase texts and reduce plagiarism levels significantly but still display a plagiarism rate that would be suspicious to teachers (45%) [22]. However, another study found that ChatGPT responses only had 13% similarity on Turnitin.com -a plagiarism detection softwarewhen generating responses to prompts [23].…”

Section: Chatgptmentioning

confidence: 99%

Caring for screenagers (part 2): a pediatrician's primer on popular games and educational tools

Margolis,

Barile,

Cason

et al. 2024

Current Opinion in Pediatrics

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Purpose of review The internet has changed the way children can work and play. With the preferences of the youth population constantly evolving, it is important that parents and pediatricians continue to be informed of the ways children spend their time on the internet. Online gaming continues to soar in popularity, as does school usage of educational platforms. Recent findings Roblox, Fortnite, and Minecraft are three examples of games that have widespread popularity among youth populations. Though none of the game are designed to display graphic violence, sexual content, or other features that could be considered off-limits for children, there still ways for children to exposed to inappropriate material or engage with strangers. The rise in popularity of eSports also reflects changing attitudes about the value of gaming. On the educational side, school-student platforms and powerful artificial intelligence (AI) tools are becoming more and more prevalent. Summary Parents should not simply know which games or educational tools their children are using, but understand them. This article seeks to provide some insight into popular games and platforms so that parents and pediatricians can make better decisions about what children access.

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Section: Chatgptmentioning

confidence: 99%

Caring for screenagers (part 2): a pediatrician's primer on popular games and educational tools

Margolis,

Barile,

Cason

et al. 2024

Current Opinion in Pediatrics

View full text Add to dashboard Cite

show abstract

“…AI-generated content (AIGC) engages models trained on vast text or document chunks providing content closely related to the prompts supplied by its user [58,59]. Conversational LLMs, such as ChatGPT, extract outputs of related contents of patterns and structures similar to prompts given by a user in a process known as prompt engineering [60]. ChatGPTcreated by OpenAI has generated a high similarity index of text and documents as natural language responses to questions and statements asked by its users [59][60][61].…”

Section: Information Extraction As Text Generationmentioning

confidence: 99%

“…Conversational LLMs, such as ChatGPT, extract outputs of related contents of patterns and structures similar to prompts given by a user in a process known as prompt engineering [60]. ChatGPTcreated by OpenAI has generated a high similarity index of text and documents as natural language responses to questions and statements asked by its users [59][60][61]. Te use of ChatGPT to generate fake content is debatable but its applied use for educational materials cannot be overemphasized.…”

Section: Information Extraction As Text Generationmentioning

confidence: 99%

Parsing of Research Documents into XML Using Formal Grammars

Iwashokun,

Ade-Ibijola

2024

Applied Computational Intelligence and Soft Computing

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Automatic information extraction of content and style format in paged documents is challenging. It requires the conversion of the original document into a granular level of details for which every document section and content is identifiable. This functionality or tool does not exist for any academic research document yet. In this paper, we present an automated process of parsing research paper documents into XML files using a formal method approach of context-free grammars (CFGs) and regular expressions (REGEXs) definable of a standard template. We created a tool for the algorithms to parse these documents into tree-like structures organized as XML files named research_XML (RX) parser. The RX tool performed the extraction of syntactic structure and semantic information of the document’s contents into XML files. These XML output files are lightweight, analyzable, query-able, and web interoperable. The RX tool has a success rate of 91% when evaluated on fifty varying research documents of 160 average pages and 8,004 total pages. The tool and test data are accessible on GitHub repo. The novelty of our process is specific to applying formal techniques for information extraction in structured multipaged documents and academic research documents thus advancing the research in automatic information extraction.

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“…In the LIS field, there has been a growing body of research focusing on the theoretical aspects related to models akin to ChatGPT. For instance, some studies have delved into the technical ethics and risks associated with ChatGPT-like models [4][5][6], and others have explored how to better harness ChatGPT in LIS [7,8]. However, these studies have primarily centered on the theoretical influences and application analyses of ChatGPT without conducting extensive practical experiments or testing within actual LIS scenarios.…”

Section: Introductionmentioning

confidence: 99%

BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model

Li,

Chen,

Zhang

et al. 2023

Electronics

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With the continuous development and change exhibited by large language model (LLM) technology, represented by generative pretrained transformers (GPTs), many classic scenarios in various fields have re-emerged with new opportunities. This paper takes ChatGPT as the modeling object, incorporates LLM technology into the typical book resource understanding and recommendation scenario for the first time, and puts it into practice. By building a ChatGPT-like book recommendation system (BookGPT) framework based on ChatGPT, this paper attempts to apply ChatGPT to recommendation modeling for three typical tasks: book rating recommendation, user rating recommendation, and the book summary recommendation; it also explores the feasibility of LLM technology in book recommendation scenarios. At the same time, based on different evaluation schemes for book recommendation tasks and the existing classic recommendation models, this paper discusses the advantages and disadvantages of the BookGPT in book recommendation scenarios and analyzes the opportunities and improvement directions for subsequent LLMs in these scenarios. The experimental research shows the following: (1) The BookGPT can achieve good recommendation results in existing classic book recommendation tasks. Especially in cases containing less information about the target object to be recommended, such as zero-shot or one-shot learning tasks, the performance of the BookGPT is close to or even better than that of the current classic book recommendation algorithms, and this method has great potential for improvement. (2) In text generation tasks such as book summary recommendation, the recommendation effect of the BookGPT model is better than that of the manual editing process of Douban Reading, and it can even perform personalized interpretable content recommendations based on readers’ attribute and identity information, making it more persuasive than interpretable one-size-fits-all recommendation models. Finally, we have open-sourced the relevant datasets and experimental codes, hoping that the exploratory program proposed in this paper can inspire the development of more LLMs to expand their applications and theoretical research prospects in the field of book recommendation and general recommendation tasks.

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OpenAI ChatGPT Generated Content and Similarity Index: A study of selected terms from the Library & Information Science (LIS)

Cited by 8 publications

References 0 publications

Caring for screenagers (part 2): a pediatrician's primer on popular games and educational tools

Caring for screenagers (part 2): a pediatrician's primer on popular games and educational tools

Parsing of Research Documents into XML Using Formal Grammars

BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model

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