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.

翻译：预训练语言模型（LLM）在当今及未来创新中的优势与能力对任何社会都至关重要。然而，引入和使用LLM会伴随偏见与歧视，从而引发对平等、多样性和公平性的担忧，这一问题必须得到解决。尽管理解并承认LLM中的偏见以及制定缓解策略至关重要，但对社会需求的普遍化假设可能导致对代表性不足的社会群体和原住民的劣势。此外，全球范围内法律法规实际及拟议修订的持续变化也影响着应对偏见问题的研究能力。本研究通过全面综述，综合分析了当前用于识别和缓解LLM偏见的技术趋势与局限性，将应对偏见的方法分为度量标准、基准数据集和缓解策略三类。本综述的重要性和创新之处在于探讨了代表性不足社会群体的视角。我们认为，当前应对偏见问题的实践不能简单"套用"以满足代表性不足群体的需求。我们以新西兰为例，提出了将现有技术适配至代表性不足社会群体的要求。