  Large language models (LLMs) can elicit social bias during generations,
especially when inference with toxic prompts. Controlling the sensitive
attributes in generation encounters challenges in data distribution,
generalizability, and efficiency. Specifically, fine-tuning and retrieval
demand extensive unbiased corpus, while direct prompting requires meticulously
curated instructions for correcting the output in multiple rounds of thoughts
but poses challenges on memory and inference latency. In this work, we propose
the Expert-Guided Extinction of Toxic Tokens for Debiased Generation (EXPOSED)
to eliminate the undesired harmful outputs for LLMs without the aforementioned
requirements. EXPOSED constructs a debiasing expert based on the abundant toxic
corpus to expose and elicit the potentially dangerous tokens. It then processes
the output to the LLMs and constructs a fair distribution by suppressing and
attenuating the toxic tokens. EXPOSED is evaluated on fairness benchmarks over
three LLM families. Extensive experiments demonstrate that compared with other
baselines, the proposed EXPOSED significantly reduces the potential social bias
while balancing fairness and generation performance.
