In addressing the critical need for safety in Large Language Models (LLMs),
it is crucial to ensure that the outputs are not only safe but also retain
their contextual accuracy. Many existing LLMs are safe fine-tuned either with
safety demonstrations, or rely only on adversarial testing. While able to get
safe outputs, they often risk losing contextual meaning as they mitigate bias
and toxicity. In response, we present MBIAS, a LLM framework instruction
fine-tuned on a custom dataset specifically designed for safety interventions.
MBIAS aims to address the significant issues of bias and toxicity in LLMs
generations that typically manifest as underrepresentation or negative
portrayals across various demographics, including inappropriate linguistic
mentions and biased content in social media. We experiment on MBIAS for safety
interventions using various configurations, and demonstrate more than a 30\%
reduction in overall bias and toxicity while successfully retaining key
information. Additionally, a demographic analysis on an out-of-distribution
test set confirms the robustness of our approach, with reductions in bias and
toxicity exceeding 90\% across various demographics. The dataset and
instruction fine-tuned MBIAS are made available to the research community at
this https URL

为了保证 Large Language Models（LLMs）的安全性，同时保持其上下文准确性，提出了 MBIAS 框架，通过在自定义数据集上进行指令微调，优化 LLMs 生成中的偏见和毒性问题，实验结果表明，MBIAS 能够在保持关键信息的同时，将总体偏见和毒性减少 30% 以上，且在各个人口统计学数据上的测试中，偏见和毒性减少超过 90%。