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Our work addresses the critical issue of distinguishing text generated by Large Language Models (LLMs) from human-produced text, a task essential for numerous applications. Despite ongoing debate about the feasibility of such…

Computation and Language · Computer Science 2023-10-04 Souradip Chakraborty , Amrit Singh Bedi , Sicheng Zhu , Bang An , Dinesh Manocha , Furong Huang

Large Language Models (LLMs) have revolutionized the domain of natural language processing (NLP) with remarkable capabilities of generating human-like text responses. However, despite these advancements, several works in the existing…

Computation and Language · Computer Science 2023-10-25 Soumya Suvra Ghosal , Souradip Chakraborty , Jonas Geiping , Furong Huang , Dinesh Manocha , Amrit Singh Bedi

Large language models (LLMs) have advanced to a point that even humans have difficulty discerning whether a text was generated by another human, or by a computer. However, knowing whether a text was produced by human or artificial…

Computation and Language · Computer Science 2025-04-15 Kathleen C. Fraser , Hillary Dawkins , Svetlana Kiritchenko

With the recent proliferation of Large Language Models (LLMs), there has been an increasing demand for tools to detect machine-generated text. The effective detection of machine-generated text face two pertinent problems: First, they are…

Computation and Language · Computer Science 2024-04-04 Mazal Bethany , Brandon Wherry , Emet Bethany , Nishant Vishwamitra , Anthony Rios , Peyman Najafirad

The rapid development of large language models has led to an increase in AI-generated text, with students increasingly using LLM-generated content as their own work, which violates academic integrity. This paper presents an evaluation of AI…

Computation and Language · Computer Science 2026-01-08 Adilkhan Alikhanov , Aidar Amangeldi , Diar Demeubay , Dilnaz Akhmetzhan , Nurbek Moldakhmetov , Omar Polat , Galymzhan Zharas

The rapid advancement of large language models (LLMs) has made detecting AI-generated text an increasingly critical challenge. Traditional methods often fail to capture the nuanced semantic differences between human and machine-generated…

Computation and Language · Computer Science 2025-02-03 Lifu Gao , Ziwei Liu , Qi Zhang

My research investigates the use of cutting-edge hybrid deep learning models to accurately differentiate between AI-generated text and human writing. I applied a robust methodology, utilising a carefully selected dataset comprising AI and…

Computation and Language · Computer Science 2024-01-17 Abiodun Finbarrs Oketunji

The recent large language models (LLMs), e.g., ChatGPT, have been able to generate human-like and fluent responses when provided with specific instructions. While admitting the convenience brought by technological advancement, educators…

Computation and Language · Computer Science 2023-12-27 Zijie Zeng , Lele Sha , Yuheng Li , Kaixun Yang , Dragan Gašević , Guanliang Chen

Large Language Models (LLMs) possess an extraordinary capability to produce text that is not only coherent and contextually relevant but also strikingly similar to human writing. They adapt to various styles and genres, producing content…

Computation and Language · Computer Science 2025-07-08 Chinnappa Guggilla , Budhaditya Roy , Trupti Ramdas Chavan , Abdul Rahman , Edward Bowen

Large Language Models (LLMs) have revolutionized the field of Natural Language Generation (NLG) by demonstrating an impressive ability to generate human-like text. However, their widespread usage introduces challenges that necessitate…

Computation and Language · Computer Science 2024-06-28 Sara Abdali , Richard Anarfi , CJ Barberan , Jia He

The rapid progress of large language models has enabled the generation of text that closely resembles human writing, creating challenges for authenticity verification in education, publishing, and digital security. Detecting AI-generated…

Computation and Language · Computer Science 2026-01-29 Michał Gromadzki , Anna Wróblewska , Agnieszka Kaliska

The rapid development of Artificial Intelligence (AI) has led to the creation of powerful text generation models, such as large language models (LLMs), which are widely used for diverse applications. However, concerns surrounding…

Artificial Intelligence · Computer Science 2024-12-06 Fnu Neha , Deepshikha Bhati , Deepak Kumar Shukla , Angela Guercio , Ben Ward

Current techniques for detecting AI-generated text are largely confined to manual feature crafting and supervised binary classification paradigms. These methodologies typically lead to performance bottlenecks and unsatisfactory…

Computation and Language · Computer Science 2024-10-29 Xun Guo , Shan Zhang , Yongxin He , Ting Zhang , Wanquan Feng , Haibin Huang , Chongyang Ma

The recent proliferation of AI-generated content has prompted significant interest in developing reliable detection methods. This study explores techniques for identifying AI-generated text through sentence-level evaluation within hybrid…

Computation and Language · Computer Science 2024-12-30 Dima Galat

An ideal detection system for machine generated content is supposed to work well on any generator as many more advanced LLMs come into existence day by day. Existing systems often struggle with accurately identifying AI-generated content…

The rapid advancement of large language models (LLMs) has led to increasingly human-like AI-generated text, raising concerns about content authenticity, misinformation, and trustworthiness. Addressing the challenge of reliably detecting…

Large Language Models (LLMs) have demonstrated remarkable capabilities in generating text that closely resembles human writing across a wide range of styles and genres. However, such capabilities are prone to potential misuse, such as fake…

Computation and Language · Computer Science 2025-05-20 Harika Abburi , Sanmitra Bhattacharya , Edward Bowen , Nirmala Pudota

The burgeoning progress in the field of Large Language Models (LLMs) heralds significant benefits due to their unparalleled capacities. However, it is critical to acknowledge the potential misuse of these models, which could give rise to a…

Computation and Language · Computer Science 2023-08-07 Haolan Zhan , Xuanli He , Qiongkai Xu , Yuxiang Wu , Pontus Stenetorp

Nowadays, the usage of Large Language Models (LLMs) has increased, and LLMs have been used to generate texts in different languages and for different tasks. Additionally, due to the participation of remarkable companies such as Google and…

Computation and Language · Computer Science 2024-02-26 Mohammad Heydari Rad , Farhan Farsi , Shayan Bali , Romina Etezadi , Mehrnoush Shamsfard

Large language models (LLMs) have transformed human writing by enhancing grammar correction, content expansion, and stylistic refinement. However, their widespread use raises concerns about authorship, originality, and ethics, even…

Computation and Language · Computer Science 2024-10-21 Zhen Tao , Zhiyu Li , Runyu Chen , Dinghao Xi , Wei Xu
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