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消除大型语言模型的偏见:研究机会

Vithya Yogarajan¹, Gillian Dobbie¹, Te Taka Keegan²

¹School of Computer Science, University of Auckland, Auckland, New Zealand.

Journal of the Royal Society of New Zealand

|December 16, 2024

概括

大型语言模型 (LLM) 可以延续社会偏见. 这项研究评估了新西兰内部的偏见指标和 debiasing 技术.

科学领域:

人工智能的人工智能
计算机科学计算机科学
技术对社会的影响技术对社会的影响

背景情况:

大型语言模型 (LLM) 越来越多地用于医疗保健和金融等关键领域.
从训练数据,算法和用户交互中,LLM可以继承和放大社会偏见,引发对平等和公平的担忧.
目前关于LLM偏见的研究主要集中在美国和欧洲,忽视了其他社会背景.

研究的目的:

在新西兰奥特亚罗亚独特的背景下,对大型语言模型进行实验性评估现有的偏差指标和 debiasing 技术.
确定研究缺口,并讨论当前和未来的研究机会,以解决新西兰的LLM偏见.

主要方法:

实验评估已建立的偏见指标.
评估现有的脱债技术.
文献审查以确定研究差距和当前工作.

主要成果:

该研究提供了针对新西兰环境量身定制的偏见指标和 debiasing 技术的实验性评估.
确定了与新西兰独特的社会,文化和历史景观相关的具体研究差距.
概述了该领域当前和正在进行的研究举措.

结论:

对于像新西兰这样的非西方,多元化的社会来说,有必要对LLM偏见研究进行调整和发展.
这些发现为新西兰研究界提供了一份路线图,为公平的AI发展做出贡献.
进一步的研究是必不可少的,以确保大型语言模型在全球多样化的人口中是公平和公正的.

关键词:

大型语言模型.新西兰新西兰新西兰新西兰偏见偏见偏见偏见偏见生成型的人工智能负责的人工智能负责的人工智能

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