Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·May 21·5 min read
Can LLMs Emulate Human Belief Dynamics?
TL;DR
A gap exists in understanding how large language models (LLMs) can simulate human belief dynamics in social networks. Researchers evaluated 12 LLMs, including various model families and sizes, against established belief dynamics studies.
✦ Why It Matters
Engineers and researchers should be cautious in applying LLMs for modeling human social behaviors and beliefs.
Key Takeaways
- Evaluate LLMs critically for social behavior modeling.
- Consider human belief dynamics in AI applications.
- Explore alternative models for simulating belief changes.
- Understand LLM limitations in capturing human-like responses.
Full Summary
Understanding how humans form and change beliefs within social networks is crucial for various applications, including social media and misinformation management. Researchers aimed to assess whether large language models (LLMs) could replicate these belief dynamics.
They conducted experiments using 12 different LLMs, varying in model families and parameter sizes, to replicate an established study on belief dynamics. The findings revealed that LLMs failed to capture initial human belief distributions accurately, demonstrating a tendency to conform more than humans do.
Specifically, LLMs adjusted their responses to align with the beliefs of others, indicating a systematic deviation from human behavior. These results suggest that while LLMs can generate human-like text, they do not effectively model the complexities of human belief formation and change.
This has implications for the design and application of LLMs in social contexts.