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Dominik Macko

7 accepted papers

2025

Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation

ACL 2025long

The capabilities of recent large language models (LLMs) to generate high-quality content indistinguishable by humans from human-written texts raises many concerns regarding their misuse. Previous research has shown that LLMs can be effectively misused for generating disinformation news articles foll…

Cited by 0SourcePDFScholar
2025

MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts

ACL 2025long

Recent LLMs are able to generate high-quality multilingual texts, indistinguishable for humans from authentic human-written ones. Research in machine-generated text detection is however mostly focused on the English language and longer texts, such as news articles, scientific papers or student essay…

2024

A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts

ACL 2024long

In the realm of text manipulation and linguistic transformation, the question of authorship has been a subject of fascination and philosophical inquiry. Much like the Ship of Theseus paradox, which ponders whether a ship remains the same when each of its original planks is replaced, our research del…

2024

Authorship Obfuscation in Multilingual Machine-Generated Text Detection

EMNLP 2024finding

High-quality text generation capability of latest Large Language Models (LLMs) causes concerns about their misuse (e.g., in massive generation/spread of disinformation). Machine-generated text (MGT) detection is important to cope with such threats. However, it is susceptible to authorship obfuscatio…

2024

Disinformation Capabilities of Large Language Models

ACL 2024long

Automated disinformation generation is often listed as one of the risks of large language models (LLMs). The theoretical ability to flood the information space with disinformation content might have dramatic consequences for democratic societies around the world. This paper presents a comprehensive…

2024

IMGTB: A Framework for Machine-Generated Text Detection Benchmarking

ACL 2024system demonstrations

In the era of large language models generating high quality texts, it is a necessity to develop methods for detection of machine-generated text to avoid their harmful use or simply for annotation purposes. It is, however, also important to properly evaluate and compare such developed methods. Recent…

2023

MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark

EMNLP 2023long main

There is a lack of research into capabilities of recent LLMs to generate convincing text in languages other than English and into performance of detectors of machine-generated text in multilingual settings. This is also reflected in the available benchmarks which lack authentic texts in languages ot…

Cited by 0SourcecodeScholar