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.

翻译：社会认知理论解释了人们如何通过观察他人来学习与获取知识。近年来，大型语言模型（LLMs）的快速发展表明它们作为社会智能体具有潜在重要意义。作为AI智能体，LLMs能够观察外部信息，这些信息塑造了它们的认知与行为。然而，外部信息对LLMs认知与行为的影响程度仍不明确。本研究从社会认知视角探究外部陈述与观点如何影响LLMs的思想与行为。通过三项实验，我们考察了外部信息对LLMs记忆、观点及社交媒体行为决策的影响。同时分析了来源权威性、社会身份与社会角色等社会认知因素的调节效应。结果表明：外部信息能显著塑造LLMs的记忆、观点与行为，这些变化折射出权威偏差、内群体偏好、情绪正向性及情绪传染等人类社会认知模式。这凸显了开发安全无偏LLMs所面临的挑战，并强调了理解LLMs易受外部影响的重要性。