Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing
  Framework
Jingling Li Zeyu Tang Xiaoyu Liu Peter Spirtes Kun Zhang Liu Leqi Yang Liu
Large language models (LLMs) can easily generate biased and discriminative
responses. As LLMs tap into consequential decision-making (e.g., hiring and
healthcare), it is of crucial importance to develop strategies to mitigate
these biases. This paper focuses on social bias, tackling the association
between demographic information and LLM outputs. We propose a causality-guided
debiasing framework that utilizes causal understandings of (1) the
data-generating process of the training corpus fed to LLMs, and (2) the
internal reasoning process of LLM inference, to guide the design of prompts for
debiasing LLM outputs through selection mechanisms. Our framework unifies
existing de-biasing prompting approaches such as inhibitive instructions and
in-context contrastive examples, and sheds light on new ways of debiasing by
encouraging bias-free reasoning. Our strong empirical performance on real-world
datasets demonstrates that our framework provides principled guidelines on
debiasing LLM outputs even with only the black-box access.