Existing debiasing techniques are typically training-based or require access
to the model's internals and output distributions, so they are inaccessible to
end-users looking to adapt LLM outputs for their particular needs. In this
study, we examine whether structured prompting techniques can offer
opportunities for fair text generation. We evaluate a comprehensive
end-user-focused iterative framework of debiasing that applies System 2
thinking processes for prompts to induce logical, reflective, and critical text
generation, with single, multi-step, instruction, and role-based variants. By
systematically evaluating many LLMs across many datasets and different
prompting strategies, we show that the more complex System 2-based Implicative
Prompts significantly improve over other techniques demonstrating lower mean
bias in the outputs with competitive performance on the downstream tasks. Our
work offers research directions for the design and the potential of
end-user-focused evaluative frameworks for LLM use.

通过系统评估多种 LLM 模型和不同提示策略的方法，我们展示出基于第二系统推理提示的复杂方法在减少偏见的平均输出中表现更好，对下游任务具有竞争性能，这为以最终用户为焦点的评估框架在 LLM 使用方面提供了研究方向和潜力。