  Large language models(LLM) are pre-trained on extensive corpora to learn
facts and human cognition which contain human preferences. However, this
process can inadvertently lead to these models acquiring biases and stereotypes
prevalent in society. Prior research has typically tackled the issue of bias
through a one-dimensional perspective, concentrating either on locating or
mitigating it. This limited perspective has created obstacles in facilitating
research on bias to synergistically complement and progressively build upon one
another. In this study, we integrate the processes of locating and mitigating
bias within a unified framework. Initially, we use causal mediation analysis to
trace the causal effects of different components' activation within a large
language model. Building on this, we propose the LSDM (Least Square Debias
Method), a knowledge-editing based method for mitigating gender bias in
occupational pronouns, and compare it against two baselines on three gender
bias datasets and seven knowledge competency test datasets. The experimental
results indicate that the primary contributors to gender bias are the bottom
MLP modules acting on the last token of occupational pronouns and the top
attention module acting on the final word in the sentence. Furthermore, LSDM
mitigates gender bias in the model more effectively than the other baselines,
while fully preserving the model's capabilities in all other aspects.
