Generative Language Models Exhibit Social Identity Biases
Tiancheng Hu Yara Kyrychenko Steve Rathje Nigel Collier Sander van der Linden Jon Roozenbeek
The surge in popularity of large language models has given rise to concerns
about biases that these models could learn from humans. In this study, we
investigate whether ingroup solidarity and outgroup hostility, fundamental
social biases known from social science, are present in 51 large language
models. We find that almost all foundational language models and some
instruction fine-tuned models exhibit clear ingroup-positive and
outgroup-negative biases when prompted to complete sentences (e.g., "We
are..."). A comparison of LLM-generated sentences with human-written sentences
on the internet reveals that these models exhibit similar level, if not
greater, levels of bias than human text. To investigate where these biases stem
from, we experimentally varied the amount of ingroup-positive or
outgroup-negative sentences the model was exposed to during fine-tuning in the
context of the United States Democrat-Republican divide. Doing so resulted in
the models exhibiting a marked increase in ingroup solidarity and an even
greater increase in outgroup hostility. Furthermore, removing either
ingroup-positive or outgroup-negative sentences (or both) from the fine-tuning
data leads to a significant reduction in both ingroup solidarity and outgroup
hostility, suggesting that biases can be reduced by removing biased training
data. Our findings suggest that modern language models exhibit fundamental
social identity biases and that such biases can be mitigated by curating
training data. Our results have practical implications for creating less biased
large-language models and further underscore the need for more research into
user interactions with LLMs to prevent potential bias reinforcement in humans.