SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection
원문 초록
We present the results and the main findings of SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three subtasks. Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. This subtask has two tracks: a monolingual track focused solely on English texts and a multilingual track. Subtask B is to detect the exact source of a text, discerning whether it is written by a human or generated by a specific LLM. Subtask C aims to identify the changing point within a text, at which the authorship transitions from human to machine. The task attracted a large number of participants: subtask A monolingual (126), subtask A multilingual (59), subtask B (70), and subtask C (30). In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For all subtasks, the best systems used LLMs.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
SKDU at De-Factify 4.0: Natural Language Features for AI-Generated Text-Detection
LuxVeri at GenAI Detection Task 1: Inverse Perplexity Weighted Ensemble for Robust Detection of AI-Generated Text across English and Multilingual Contexts
Inverse Scene Text Removal
EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors
PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
토론
이 논문과 연결된 포럼 글이 아직 없습니다.