{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/kinit-at-semeval-2024-task-8-fine-tuned-llms","title":"KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection","arxiv_id":"2402.13671","date":"2024-02-21","proceeding":null,"authors":["Michal Spiegel","Dominik Macko"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2402.13671v2","url_pdf":"https://arxiv.org/pdf/2402.13671v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"kinit-at-semeval-2024-task-8-fine-tuned-llms","repo_url":"https://github.com/michalspiegel/imgtb","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"kinit-at-semeval-2024-task-8-fine-tuned-llms","repo_url":"https://github.com/kinit-sk/semeval-2024-task-8-machine-text-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"language-identification","task_name":"Language Identification"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-detection","task_name":"Text Detection"},{"task_slug":"parameter-efficient-fine-tuning","task_name":"parameter-efficient fine-tuning"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2402.13671","atlas_url":"https://app.syntology.ai/?focus=2402.13671","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}