The benefits and capabilities of pre-trained language models (LLMs) in
current and future innovations are vital to any society. However, introducing
and using LLMs comes with biases and discrimination, resulting in concerns
about equality, diversity and fairness, and must be addressed. While
understanding and acknowledging bias in LLMs and developing mitigation
strategies are crucial, the generalised assumptions towards societal needs can
result in disadvantages towards under-represented societies and indigenous
populations. Furthermore, the ongoing changes to actual and proposed amendments
to regulations and laws worldwide also impact research capabilities in tackling
the bias problem. This research presents a comprehensive survey synthesising
the current trends and limitations in techniques used for identifying and
mitigating bias in LLMs, where the overview of methods for tackling bias are
grouped into metrics, benchmark datasets, and mitigation strategies. The
importance and novelty of this survey are that it explores the perspective of
under-represented societies. We argue that current practices tackling the bias
problem cannot simply be 'plugged in' to address the needs of under-represented
societies. We use examples from New Zealand to present requirements for
adopting existing techniques to under-represented societies.

预训练语言模型在当前和未来的创新中的好处和能力对任何社会都至关重要，然而，引入和使用语言模型会带来偏见和歧视，引发对平等、多样性和公平的关切，并需要解决。这项研究通过综述综合分析了识别和减轻语言模型中偏见的当前趋势和限制的技术，其中有关减轻偏见的方法的概述被分为度量指标、基准数据集和减轻策略。该调查的重要性和新颖性在于探讨了被忽视的社会的视角，我们认为当前处理偏见问题的做法不能简单地 “插入” 以解决被忽视的社会的需求。我们使用新西兰的例子提出了采用现有技术来适应被忽视的社会的要求。