TrustAI在SemEval-2024任务8中的多领域机器生成文本检测技术综合分析
TrustAI at SemEval-2024 Task 8: A Comprehensive Analysis of Multi-domain Machine Generated Text Detection Techniques
AI总结：
本文提出TrustAI团队在SemEval-2024任务8中，通过综合分析统计、神经和预训练模型方法，在单语和多语语境下检测多领域机器生成文本，在测试集上分别取得86.9%和83.7%的准确率。
摘要：
大型语言模型（LLMs）在广泛的用户查询中展现出生成流畅内容的卓越能力。然而，这种能力引发了对错误信息和个人信息泄露的担忧。本文介绍了我们针对SemEval-2024 Task8的方法，旨在单语和多语语境下检测跨多个领域的机器生成文本。我们的研究全面分析了检测机器生成文本的各种方法，包括统计方法、神经方法和预训练模型方法。我们还详细描述了实验设置，并进行了深入的误差分析以评估这些方法的有效性。我们的方法在subtask-A mono的测试集上获得了86.9%的准确率，在subtask-B上获得了83.7%的准确率。此外，我们还强调了未来研究中需要考虑的挑战和关键因素。
英文摘要：
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 detail our experimental setup and perform a in-depth error analysis to evaluate the effectiveness of these methods. Our methods obtain an accuracy of 86.9\% on the test set of subtask-A mono and 83.7\% for subtask-B. Furthermore, we also highlight the challenges and essential factors for consideration in future studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
