  Social cognitive theory explains how people learn and acquire knowledge
through observing others. Recent years have witnessed the rapid development of
large language models (LLMs), which suggests their potential significance as
agents in the society. LLMs, as AI agents, can observe external information,
which shapes their cognition and behaviors. However, the extent to which
external information influences LLMs' cognition and behaviors remains unclear.
This study investigates how external statements and opinions influence LLMs'
thoughts and behaviors from a social cognitive perspective. Three experiments
were conducted to explore the effects of external information on LLMs'
memories, opinions, and social media behavioral decisions. Sociocognitive
factors, including source authority, social identity, and social role, were
analyzed to investigate their moderating effects. Results showed that external
information can significantly shape LLMs' memories, opinions, and behaviors,
with these changes mirroring human social cognitive patterns such as authority
bias, in-group bias, emotional positivity, and emotion contagion. This
underscores the challenges in developing safe and unbiased LLMs, and emphasizes
the importance of understanding the susceptibility of LLMs to external
influences.
