  Large Language Models (LLMs) have advanced various Natural Language
Processing (NLP) tasks, such as text generation and translation, among others.
However, these models often generate text that can perpetuate biases. Existing
approaches to mitigate these biases usually compromise knowledge retention.
This study explores whether LLMs can produce safe, unbiased outputs without
sacrificing knowledge or comprehension. We introduce the Safe and Responsible
Large Language Model (\textbf{SR}$_{\text{LLM}}$), which has been instruction
fine-tuned atop an inherently safe fine-tuned LLM to reduce biases in generated
texts. We developed a specialized dataset with examples of unsafe and
corresponding safe variations to train \textbf{SR}$_{\text{LLM}}$ to identify
and correct biased text. Experiments on our specialized dataset and
out-of-distribution test sets reveal that \textbf{SR}$_{\text{LLM}}$
effectively reduces biases while preserving knowledge integrity. This
performance surpasses that of traditional fine-tuning of smaller language
models and base LLMs that merely reply on prompting techniques. Our findings
indicate that instruction fine-tuning is an effective strategy for minimizing
bias in LLMs while retaining knowledge. The code and dataset are accessible at
\href{https://github.com/shainarazavi/Safe-Responsible-LLM}{SR-LLM}.
