LLM Feedback Loops and the Lock-in Hypothesis

This paper explores the potential for large language models (LLMs) to create feedback loops that reinforce existing human beliefs, leading to a loss of diversity in ideas and a phenomenon termed "lock-in." Through analysis of real-world ChatGPT usage data, LLM-based simulations, and formal modeling, the authors provide evidence for this feedback loop and its connection to the entrenchment of dominant viewpoints. They hypothesize and formally model how this interaction between humans and LLMs can lead to a collective adherence to potentially false beliefs, especially when moderate mutual trust is present. The research highlights the concerning possibility of AI contributing to intellectual stagnation by amplifying and solidifying prevailing opinions.