HateGPT：释放 GPT-3.5 Turbo 以对抗 X 平台上的仇恨言论
HateGPT: Unleashing GPT-3.5 Turbo to Combat Hate Speech on X
AI总结：
本研究利用 GPT-3.5 Turbo 通过提示方法对 X 平台上的英语推文进行仇恨与攻击性言论分类，在三个运行中取得 0.756、0.751 和 0.754 的 Macro-F1 分数，验证了模型的高性能与稳定性。
摘要：
Twitter 和 Facebook 等社交媒体平台的广泛使用，使得各个年龄段的人们都能分享自己的想法和经历，从而导致用户生成内容的大量积累。然而，在带来益处的同时，这些平台也面临着管理仇恨言论和攻击性内容的挑战，这些内容可能破坏理性讨论并威胁民主价值观。因此，对于自动化检测和缓解此类内容的需求日益增长，尤其是考虑到对话的复杂性，可能需要在多种语言（包括 Hinglish、德语-英语和孟加拉语等混合语言）中进行上下文分析。我们参与了英语任务，需要将英语推文分为两类：仇恨与攻击性言论以及非仇恨攻击性言论。在这项工作中，我们通过提示（prompting）实验了最先进的大型语言模型（如 GPT-3.5 Turbo），以将推文分类为仇恨与攻击性言论或非仇恨攻击性言论。在本研究中，我们使用 Macro-F1 分数评估了分类模型在三个不同运行（run）中的性能。Macro-F1 分数平衡了所有类别的精确率和召回率，被用作模型评估的主要指标。获得的分数分别为：运行 1 为 0.756，运行 2 为 0.751，运行 3 为 0.754，表明模型具有高性能且各运行之间方差极小。结果表明，该模型在精确率和召回率方面持续表现良好，其中运行 1 显示出最高性能。这些发现凸显了该模型在不同运行中的稳健性和可靠性。
英文摘要：
The widespread use of social media platforms like Twitter and Facebook has enabled people of all ages to share their thoughts and experiences, leading to an immense accumulation of user-generated content. However, alongside the benefits, these platforms also face the challenge of managing hate speech and offensive content, which can undermine rational discourse and threaten democratic values. As a result, there is a growing need for automated methods to detect and mitigate such content, especially given the complexity of conversations that may require contextual analysis across multiple languages, including code-mixed languages like Hinglish, German-English, and Bangla. We participated in the English task where we have to classify English tweets into two categories namely Hate and Offensive and Non Hate-Offensive. In this work, we experiment with state-of-the-art large language models like GPT-3.5 Turbo via prompting to classify tweets into Hate and Offensive or Non Hate-Offensive. In this study, we evaluate the performance of a classification model using Macro-F1 scores across three distinct runs. The Macro-F1 score, which balances precision and recall across all classes, is used as the primary metric for model evaluation. The scores obtained are 0.756 for run 1, 0.751 for run 2, and 0.754 for run 3, indicating a high level of performance with minimal variance among the runs. The results suggest that the model consistently performs well in terms of precision and recall, with run 1 showing the highest performance. These findings highlight the robustness and reliability of the model across different runs.
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。
