  The deployment of Large Language Models (LLMs) in diverse applications
necessitates an assurance of safety without compromising the contextual
integrity of the generated content. Traditional approaches, including
safety-specific fine-tuning or adversarial testing, often yield safe outputs at
the expense of contextual meaning. This can result in a diminished capacity to
handle nuanced aspects of bias and toxicity, such as underrepresentation or
negative portrayals across various demographics. To address these challenges,
we introduce MBIAS, an LLM framework carefully instruction fine-tuned on a
custom dataset designed specifically for safety interventions. MBIAS is
designed to significantly reduce biases and toxic elements in LLM outputs while
preserving the main information. This work also details our further use of
LLMs: as annotator under human supervision and as evaluator of generated
content. Empirical analysis reveals that MBIAS achieves a reduction in bias and
toxicity by over 30\% in standard evaluations, and by more than 90\% in diverse
demographic tests, highlighting the robustness of our approach. We make the
dataset and the fine-tuned model available to the research community for
further investigation and ensure reproducibility. The code for this project can
be accessed here https://github.com/shainarazavi/MBIAS/tree/main.
  Warning: This paper contains examples that may be offensive or upsetting.
