This research delves into the current literature on bias in Natural Language
Processing Models and the techniques proposed to mitigate the problem of bias,
including why it is important to tackle bias in the first place. Additionally,
these techniques are further analysed in the light of newly developed models
that tower in size over past editions. To achieve those aims, the authors of
this paper conducted their research on GPT3 by OpenAI, the largest NLP model
available to consumers today. With 175 billion parameters in contrast to BERTs
340 million, GPT3 is the perfect model to test the common pitfalls of NLP
models. Tests were conducted through the development of an Applicant Tracking
System using GPT3. For the sake of feasibility and time constraints, the tests
primarily focused on gender bias, rather than all or multiple types of bias.
Finally, current mitigation techniques are considered and tested to measure
their degree of functionality.

本研究探讨了自然语言处理模型中偏见及其避免技术的现有文献，包括为何首先解决偏见问题；此外，文中分析了这些技术在比过去更大的新型模型下的表现。为了实现这些目标，本文作者使用目前可由消费者使用的最大 NLP 模型 GPT3 进行了研究。通过使用 GPT3 开发申请人跟踪系统进行测试，其中主要关注了性别偏见而不是所有或多种类型的偏见。最终，考虑和测试了当前的减轻技术以测试其功能程度。