KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection
This addresses the need to prevent misuse of LLMs by detecting multilingual AI-generated text, though it is an incremental improvement on existing detection methods.
The paper tackled the problem of detecting machine-generated text across multiple languages, domains, and generators, using fine-tuned LLMs with parameter-efficient methods and threshold calibration. Their system achieved competitive results, ranking fourth in the SemEval-2024 task, less than 1 percentage point behind the winner.
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.