Papers by Michiharu Yamashita
MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark (2023.emnlp-main)
Copied to clipboard
Dominik Macko, Robert Moro, Adaku Uchendu, Jason Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, Maria Bielikova
| Challenge: | MULTITuDE benchmarks lack authentic and machine-generated text in languages other than English . defining characteristic of new generation of LLMs is increased quality of text . |
| Approach: | They propose a benchmarking dataset for multilingual machine-generated text detection that compares detectors with authentic and machine-generated texts in 11 languages. |
| Outcome: | The proposed dataset compares detectors with zero-shot and fine-tuned detectors in 11 languages. |
Unmasking Fake Careers: Detecting Machine-Generated Career Trajectories via Multi-layer Heterogeneous Graphs (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) generate convincing career trajectories in fake resumes . a novel heterogeneous, hierarchical multi-layer graph framework is proposed to model career entities and their relations in a unified global graph built from genuine resumes. |
| Approach: | They propose a novel heterogeneous, hierarchical multi-layer graph framework that models career entities and their relations in a unified global graph built from genuine resumes. |
| Outcome: | The proposed framework outperforms state-of-the-art models by 5.8-85.0% relative to baselines. |
Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent ubiquity and disruptive impacts of large language models have raised concerns about their potential to be misused. |
| Approach: | They propose a strategy that leverages LLMs' generative and emergent reasoning capabilities to counter human-written and LLM-generated disinformation. |
| Outcome: | The proposed strategy synthesizes authentic and deceptive LLM-generated content through paraphrase-based and perturbation-based prefix-style prompts, respectively. |
Authorship Obfuscation in Multilingual Machine-Generated Text Detection (2024.findings-emnlp)
Copied to clipboard
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. |