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TrustAI at SemEval-2024 Task 8: A Comprehensive Analysis of Multi-domain Machine Generated Text Detection Techniques
March 26, 2024, 4:51 a.m. | Ashok Urlana, Aditya Saibewar, Bala Mallikarjunarao Garlapati, Charaka Vinayak Kumar, Ajeet Kumar Singh, Srinivasa Rao Chalamala
cs.CL updates on arXiv.org arxiv.org
Abstract: The Large Language Models (LLMs) exhibit remarkable ability to generate fluent content across a wide spectrum of user queries. However, this capability has raised concerns regarding misinformation and personal information leakage. In this paper, we present our methods for the SemEval2024 Task8, aiming to detect machine-generated text across various domains in both mono-lingual and multi-lingual contexts. Our study comprehensively analyzes various methods to detect machine-generated text, including statistical, neural, and pre-trained model approaches. We also …
abstract analysis arxiv capability concerns cs.cl detection domain generate generated however information language language models large language large language models llms machine misinformation paper personal information queries spectrum text type
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