KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection
원문 초록
SemEval-2024 Task 8 is focused on multigenerator, multidomain, and multilingual black-box machine-generated text detection. Such a detection is important for preventing a potential misuse of large language models (LLMs), the newest of which are very capable in generating multilingual human-like texts. We have coped with this task in multiple ways, utilizing language identification and parameter-efficient fine-tuning of smaller LLMs for text classification. We have further used the per-language classification-threshold calibration to uniquely combine fine-tuned models predictions with statistical detection metrics to improve generalization of the system detection performance. Our submitted method achieved competitive results, ranking at the fourth place, just under 1 percentage point behind the winner.
함께 살펴볼 논문
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mSTEB: Massively Multilingual Evaluation of LLMs on Speech and Text Tasks
Token Masking Improves Transformer-Based Text Classification
Making Language Model a Hierarchical Classifier and Generator
Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttack
Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation
PRL: Prompts from Reinforcement Learning
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