Papers by Nafis Irtiza Tripto
Beyond Checkmate: Exploring the Creative Choke Points for AI Generated Texts (2025.emnlp-main)
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| Challenge: | Recent work on detecting LLM-generated text (AI text) has raised concerns about potential misuse . a new study examines the nuanced distinctions between human and AI texts . |
| Approach: | They analyze human-AI text differences across body, intro, conclusion segments . human texts exhibit greater stylistic variation across segments, they show . |
| Outcome: | The findings will inform their viability and boundaries as effective creative assistants to humans. |
Catch Me If You Can? Not Yet: LLMs Still Struggle to Imitate the Implicit Writing Styles of Everyday Authors (2025.findings-emnlp)
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| Challenge: | Personal style is often subtle and implicit, making it difficult to specify through prompts yet essential for user-aligned generation. |
| Approach: | They evaluate LLMs' ability to imitate personal writing styles via in-context learning from user-authored samples. |
| Outcome: | The proposed model can imitate personal writing styles from a small number of user-authored samples. |
CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis (2025.findings-naacl)
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| Challenge: | Existing studies on LLM-LLM collaboration for open-ended tasks have focused on human-LLm interaction. |
| Approach: | They propose to generate a dataset exclusively for LLMs to explore multi-LLM collaboration scenarios . they extend their authorship-related tasks for multi-llm settings and extend their baselines . |
| Outcome: | The authors extend authorship-related tasks for multi-LLM settings and present baselines for LLM-LLMS collaboration. |
A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts (2024.acl-long)
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Nafis Irtiza Tripto, Saranya Venkatraman, Dominik Macko, Robert Moro, Ivan Srba, Adaku Uchendu, Thai Le, Dongwon Lee
| Challenge: | Using a computational approach, we discover that diminishing performance in text classification models is closely associated with the extent of deviation from the original author’s style. |
| Approach: | They propose to use large language models to determine whether a text retains original authorship when it undergoes numerous paraphrasing iterations. |
| Outcome: | The results suggest that authorship should be task-dependent . |
Authorship Obfuscation in Multilingual Machine-Generated Text Detection (2024.findings-emnlp)
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Dominik Macko, Robert Moro, Adaku Uchendu, Ivan Srba, Jason Lucas, Michiharu Yamashita, Nafis Irtiza Tripto, Dongwon Lee, Jakub Simko, Maria Bielikova
| Challenge: | Recent advances in Language Modeling have birthed Large Language Models (LLMs), which exhibit significant improvements, including the ability to generate texts easily misconstrued as humanwritten. |
| Approach: | They compare authorship obfuscation methods against machine-generated text (MGT) in 11 languages and analyze their performance against 37 well-known AO methods. |
| Outcome: | The proposed methods can cause evasion of detection in all languages, with homoglyph attacks particularly successful. |