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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…

计算与语言 · 计算机科学 2025-07-08 Chinnappa Guggilla , Budhaditya Roy , Trupti Ramdas Chavan , Abdul Rahman , Edward Bowen

Large Language Models (LLMs) are now capable of generating text that closely resembles human writing, making them powerful tools for content creation, but this growing ability has also made it harder to tell whether a piece of text was…

计算与语言 · 计算机科学 2025-10-21 Muhammad Ammar , Hadiya Murad Hadi , Usman Majeed Butt

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…

计算与语言 · 计算机科学 2023-10-04 Souradip Chakraborty , Amrit Singh Bedi , Sicheng Zhu , Bang An , Dinesh Manocha , Furong Huang

The challenge of separating AI-generated text from human-authored content is becoming more urgent as generative AI technologies like ChatGPT become more widely available. In this work, we address this issue by looking at both the detection…

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…

计算与语言 · 计算机科学 2024-02-26 Mohammad Heydari Rad , Farhan Farsi , Shayan Bali , Romina Etezadi , Mehrnoush Shamsfard

Large Language Models (LLMs) perform impressively well in various applications. However, the potential for misuse of these models in activities such as plagiarism, generating fake news, and spamming has raised concern about their…

计算与语言 · 计算机科学 2025-01-20 Vinu Sankar Sadasivan , Aounon Kumar , Sriram Balasubramanian , Wenxiao Wang , Soheil Feizi

The rapid adoption of large language models (LLMs) such as ChatGPT has blurred the line between human and AI-generated texts, raising urgent questions about academic integrity, intellectual property, and the spread of misinformation. Thus,…

计算与语言 · 计算机科学 2025-09-26 Sharanya Parimanoharan , Ruwan D. Nawarathna

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…

计算与语言 · 计算机科学 2023-10-25 Soumya Suvra Ghosal , Souradip Chakraborty , Jonas Geiping , Furong Huang , Dinesh Manocha , Amrit Singh Bedi

Large Language Models (LLMs) are gearing up to surpass human creativity. The veracity of the statement needs careful consideration. In recent developments, critical questions arise regarding the authenticity of human work and the…

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…

计算与语言 · 计算机科学 2026-01-29 Michał Gromadzki , Anna Wróblewska , Agnieszka Kaliska

Existing tools to detect text generated by a large language model (LLM) have met with certain success, but their performance can drop when dealing with texts in new domains. To tackle this issue, we train a ranking classifier called…

计算与语言 · 计算机科学 2024-10-21 You Zhou , Jie Wang

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…

计算与语言 · 计算机科学 2025-02-03 Lifu Gao , Ziwei Liu , Qi Zhang

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 potential of artificial intelligence (AI)-based large language models (LLMs) holds considerable promise in revolutionizing education, research, and practice. However, distinguishing between human-written and AI-generated text has become…

计算与语言 · 计算机科学 2023-11-14 Kadhim Hayawi , Sakib Shahriar , Sujith Samuel Mathew

Detecting AI-generated text is a difficult problem to begin with; detecting AI-generated text on social media is made even more difficult due to the short text length and informal, idiosyncratic language of the internet. It is nonetheless…

计算与语言 · 计算机科学 2025-06-17 Hillary Dawkins , Kathleen C. Fraser , Svetlana Kiritchenko

The growing use of large language models (LLMs) for text generation has led to widespread concerns about AI-generated content detection. However, an overlooked challenge is AI-polished text, where human-written content undergoes subtle…

计算与语言 · 计算机科学 2025-05-06 Shoumik Saha , Soheil Feizi

The widespread adoption of Large Language Models (LLMs) has made the detection of AI-Generated text a pressing and complex challenge. Although many detection systems report high benchmark accuracy, their reliability in real-world settings…

计算与语言 · 计算机科学 2026-04-23 Shushanta Pudasaini , Luis Miralles-Pechuán , David Lillis , Marisa Llorens Salvador

Large language models (LLMs) have demonstrated remarkable capability to generate fluent responses to a wide variety of user queries. However, this has also raised concerns about the potential misuse of such texts in journalism, education,…

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…

计算与语言 · 计算机科学 2023-08-21 Amrita Bhattacharjee , Huan Liu

ChatGPT and other general large language models (LLMs) have achieved remarkable success, but they have also raised concerns about the misuse of AI-generated texts. Existing AI-generated text detection models, such as based on BERT and…

计算与语言 · 计算机科学 2024-02-05 Rongsheng Wang , Haoming Chen , Ruizhe Zhou , Han Ma , Yaofei Duan , Yanlan Kang , Songhua Yang , Baoyu Fan , Tao Tan
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