  Mitigating bias in language models (LMs) has become a critical problem due to
the widespread deployment of LMs. Numerous approaches revolve around data
pre-processing and fine-tuning of language models, tasks that can be both
time-consuming and computationally demanding. Consequently, there is a growing
interest in machine unlearning techniques given their capacity to induce the
forgetting of undesired behaviors of the existing pre-trained or fine-tuned
models with lower computational cost. In this work, we explore two unlearning
methods, (1) Partitioned Contrastive Gradient Unlearning (PCGU) applied on
decoder models and (2) Negation via Task Vector, to reduce social biases in
state-of-the-art and open-source LMs such as LLaMA-2 and OPT. We also implement
distributed PCGU for large models. It is empirically shown, through
quantitative and qualitative analyses, that negation via Task Vector method
outperforms PCGU in debiasing with minimum deterioration in performance and
perplexity of the models. On LLaMA-27B, negation via Task Vector reduces the
bias score by 11.8%
