  Large Language Models (LLMs) perpetuate social biases, reflecting prejudices
in their training data and reinforcing societal stereotypes and inequalities.
Our work explores the potential of the Contact Hypothesis, a concept from
social psychology for debiasing LLMs. We simulate various forms of social
contact through LLM prompting to measure their influence on the model's biases,
mirroring how intergroup interactions can reduce prejudices in social contexts.
We create a dataset of 108,000 prompts following a principled approach
replicating social contact to measure biases in three LLMs (LLaMA 2, Tulu, and
NousHermes) across 13 social bias dimensions. We propose a unique debiasing
technique, Social Contact Debiasing (SCD), that instruction-tunes these models
with unbiased responses to prompts. Our research demonstrates that LLM
responses exhibit social biases when subject to contact probing, but more
importantly, these biases can be significantly reduced by up to 40% in 1 epoch
of instruction tuning LLaMA 2 following our SCD strategy. Our code and data are
available at https://github.com/chahatraj/breakingbias.
