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Found 7 papers, 5 papers with code
KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection
SemEval-2024 Task 8 is focused on multigenerator, multidomain, and multilingual black-box machine-generated text detection.
Authorship Obfuscation in Multilingual Machine-Generated Text Detection
However, it is susceptible to authorship obfuscation (AO) methods, such as paraphrasing, which can cause MGTs to evade detection.
IMGTB: A Framework for Machine-Generated Text Detection Benchmarking
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 harmful use or simply due to annotation purposes.
Disinformation Capabilities of Large Language Models
Automated disinformation generation is often listed as an important risk associated with large language models (LLMs).
A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts
In the realm of text manipulation and linguistic transformation, the question of authorship has always been a subject of fascination and philosophical inquiry.
MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark
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.
Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detection
We perform our experiments by employing a hybrid anomaly detection approach in network flow data.
