Pre-trained Large Language Models (LLMs) have significantly advanced natural
language processing capabilities but are susceptible to biases present in their
training data, leading to unfair outcomes in various applications. While
numerous strategies have been proposed to mitigate bias, they often require
extensive computational resources and may compromise model performance. In this
work, we introduce AXOLOTL, a novel post-processing framework, which operates
agnostically across tasks and models, leveraging public APIs to interact with
LLMs without direct access to internal parameters. Through a three-step process
resembling zero-shot learning, AXOLOTL identifies biases, proposes resolutions,
and guides the model to self-debias its outputs. This approach minimizes
computational costs and preserves model performance, making AXOLOTL a promising
tool for debiasing LLM outputs with broad applicability and ease of use.

通过 Axolotl，这一新颖的后处理框架，在不需要直接访问模型内部参数的情况下，与 LLMs 进行交互，通过类似零样本学习的三步过程识别偏见、提出解决方案并引导模型进行自我去偏差，从而降低计算成本并保持模型性能，为广泛应用和易于使用的 LLMs 去偏差工具提供了希望。