Large language models (LLMs) have achieved impressive performance on various
natural language generation tasks. Nonetheless, they suffer from generating
negative and harmful contents that are biased against certain demographic
groups (e.g., female), raising severe fairness concerns. As remedies, prior
works intervened the generation by removing attitude or demographic
information, inevitably degrading the generation quality and resulting in
notable \textit{fairness-fluency} trade-offs. However, it is still
under-explored to what extent the fluency \textit{has to} be affected in order
to achieve a desired level of fairness. In this work, we conduct the first
formal study from an information-theoretic perspective. We show that previous
approaches are excessive for debiasing and propose LIDAO, a general framework
to debias a (L)LM at a better fluency provably. We further robustify LIDAO in
adversarial scenarios, where a carefully-crafted prompt may stimulate LLMs
exhibiting instruction-following abilities to generate texts with fairness
issue appears only when the prompt is also taken into account. Experiments on
three LMs ranging from 0.7B to 7B parameters demonstrate the superiority of our
method.

大型语言模型中的公平性和流畅度之间的权衡问题研究，提出了一种基于信息论的框架 LIDAO，用于使语言模型更好地实现公平性而仍保持流畅度。