利用人工智能打击在线仇恨：探索大语言模型在仇恨言论检测中的挑战与机遇
Harnessing Artificial Intelligence to Combat Online Hate: Exploring the Challenges and Opportunities of Large Language Models in Hate Speech Detection
AI总结：
本研究通过文献综述与实证分析，探讨大语言模型在仇恨言论检测中的能力与限制，识别表现优异的模型及其影响因素。
摘要：
大语言模型（LLMs）在语言生成之外的众多不同应用中表现出色，例如翻译、摘要和情感分析。一个引人入胜的应用是文本分类。这在识别仇恨或有害言论的领域中尤为重要——这是一个充满挑战和伦理困境的领域。在我们的研究中，我们有两个目标：首先，提供一份围绕LLMs作为分类器的文献综述，强调它们在检测和分类仇恨或有害内容中的作用。随后，我们探讨了几种LLMs在仇恨言论分类中的效能：识别哪些LLMs在此任务中表现出色，以及它们的基本属性和训练。深入了解促成LLM在识别仇恨内容方面熟练（或缺乏熟练）的因素。通过将全面的文献综述与实证分析相结合，我们的论文旨在阐明LLMs在仇恨言论检测这一关键领域的能力和限制。
英文摘要：
Large language models (LLMs) excel in many diverse applications beyond language generation, e.g., translation, summarization, and sentiment analysis. One intriguing application is in text classification. This becomes pertinent in the realm of identifying hateful or toxic speech -- a domain fraught with challenges and ethical dilemmas. In our study, we have two objectives: firstly, to offer a literature review revolving around LLMs as classifiers, emphasizing their role in detecting and classifying hateful or toxic content. Subsequently, we explore the efficacy of several LLMs in classifying hate speech: identifying which LLMs excel in this task as well as their underlying attributes and training. Providing insight into the factors that contribute to an LLM proficiency (or lack thereof) in discerning hateful content. By combining a comprehensive literature review with an empirical analysis, our paper strives to shed light on the capabilities and constraints of LLMs in the crucial domain of hate speech detection.
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。
