Papers by Ryuto Koike
Likelihood-based Mitigation of Evaluation Bias in Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) are widely used to evaluate natural language generation tasks as automated metrics. |
| Approach: | They propose to use LLMs to evaluate sentences with higher likelihoods and lower likelihoods to mitigate the likelihood bias. |
| Outcome: | The proposed method overrates sentences with higher likelihoods while underrating sentences with lower likelihoods. |
Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI (2026.acl-long)
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Yuxia Wang, Rui Xing, Jonibek Mansurov, Giovanni Puccetti, Zhuohan Xie, Minh Ngoc Ta, Jiahui Geng, Jinyan Su, Mervat Abassy, Saadeldine Eletter, Kareem Elozeiri, Nurkhan Laiyk, Maiya Goloburda, Tarek Mahmoud, Raj Vardhan Tomar, Alexander Aziz, Ryuto Koike, Masahiro Kaneko, Artem Shelmanov, Ekaterina Artemova, Vladislav Mikhailov, Akim Tsvigun, Alham Fikri Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
| Challenge: | Prior studies have shown that distinguishing text generated by Large Language Models from human-written text is challenging for humans and often no better than random guessing. |
| Approach: | They conduct extensive case study to determine the upper bound of human detection accuracy. |
| Outcome: | The findings challenge previous conclusions on human detection accuracy across languages and domains. |
How You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text Detection (2024.findings-emnlp)
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| Challenge: | Recent studies have presented LLM-generated-text detectors with promising performance, but they do not cover such diverse instruction patterns when creating datasets for LLM detection. |
| Approach: | They propose to use task-oriented constraints that would naturally be included in an instruction and are not related to detection-evasion to create detectors with large variances in detection performance. |
| Outcome: | The proposed detectors have a large variance in detection performance on student essay writing with task-oriented constraints, and the standard deviation is significantly larger than that on texts generated by the constraint with such a constraint. |
ExaGPT: Example-Based Machine-Generated Text Detection for Human Interpretability (2026.findings-acl)
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| Challenge: | Existing interpretable detectors are not aligned with the human decision-making process and fail to offer evidence that users easily understand. |
| Approach: | They propose an interpretable detection approach that checks whether a text is human-written or LLM-generated by checking whether it shares more similar spans with human-generated texts. |
| Outcome: | ExaGPT outperforms interpretable detectors by +37.0 points at a false positive rate of 1%. |