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罗马乌尔都语中的仇恨言论分类：参数高效微调与提示工程的比较研究Hate Speech Classification In Roman Urdu: A Comparative Study On Parameter Efficient Fine-Tuning And Prompt Engineering
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以下内容为 AI 自动翻译，英文原文附后供参考。
摘要
最近的一项研究探讨了在罗马乌尔都语中进行仇恨言论分类的创新技术，这种语言在自然语言处理方面缺乏广泛资源。研究重点比较了包括提示调优、参数高效微调和提示工程在内的各种方法，以确定它们在仇恨言论分类中的有效性。采用了四种实验方法来评估模型的性能，突出了在低资源语言工作中的挑战和潜在解决方案。
A recent study has explored innovative techniques for hate speech classification in Roman Urdu, a language that lacks extensive resources for natural language processing. The research focuses on comparing various methods, including prompt tuning, parameter-efficient fine-tuning, and prompt engineering, to determine their effectiveness in classifying hate speech. Four experimental approaches were employed to assess the models' performance, highlighting the challenges and potential solutions in working with low-resource languages.
要点
该研究强调了为像罗马乌尔都语这样的低资源语言开发有效的仇恨言论分类工具的重要性。
The study emphasizes the importance of developing effective hate speech classification tools for low-resource languages like Roman Urdu.
不同技术的比较分析表明，参数高效微调和提示工程可以显著提升模型性能。
Comparative analysis of different techniques reveals that parameter-efficient fine-tuning and prompt engineering can significantly enhance model performance.
研究结果为自然语言处理领域提供了在有限数据场景下处理的见解。
The findings contribute to the broader field of natural language processing by providing insights into handling limited data scenarios.
为何重要
这项研究的重要性在于它解决了在多样语言环境中日益严重的仇恨言论问题，特别是在代表性不足的语言中。它有助于开发更具包容性的人工智能工具，更好地服务于多语言社区。
This research is significant as it addresses the growing concern of hate speech in diverse linguistic contexts, particularly in underrepresented languages. It contributes to the development of more inclusive AI tools that can better serve multilingual communities.
产品经理
对于产品经理而言，这项研究突显了开发面向低资源语言的人工智能解决方案的潜力，开辟了新市场并增强了在不同地区的用户参与度。
For product managers, this study highlights the potential for developing AI-driven solutions that cater to low-resource languages, opening new markets and enhancing user engagement in diverse regions.
开发者
开发人员和工程师可以利用研究结果来改善他们的仇恨言论检测模型，特别是在低资源环境中，通过采用参数高效微调和提示工程的技术。
Developers and engineers can leverage the findings to improve their models for hate speech detection, particularly in low-resource settings, by adopting the techniques of parameter-efficient fine-tuning and prompt engineering.
学生与求职者
在人工智能领域的学生和求职者可以从这项研究中获得宝贵的见解，因为它展示了机器学习技术在解决诸如在代表性不足语言中进行仇恨言论分类等现实问题中的实际应用。
Students and job-seekers in the AI field can gain valuable insights from this research, as it showcases practical applications of machine learning techniques in addressing real-world issues like hate speech classification in underrepresented languages.
覆盖来源
来源标题与摘录保留原文，以便核对与追溯。
arXiv cs.AI
Hate Speech Classification In Roman Urdu: A Comparative Study On Parameter Efficient Fine-Tuning And Prompt EngineeringThe study investigates effective techniques for hate speech classification in Roman Urdu, a low-resource language, by comparing methods such as prompt tuning, parameter-efficient fine-tuning, and prompt engineering. Four experimental approaches were tested to evaluate model performance in classifying hate speech with limited data.