Synthetic Dataset Diversity

embedding coverage (box volume)

embedding variation

In GPT agent-based experiments, repeated interactions consistently lead to belief convergence and diversity (entropy) reduction. Additionally, using Bayesian updates and trust matrices, researchers prove that when mutual trust exceeds a certain threshold, groups become overly confident in factually incorrect beliefs. In other words, the mutual feedback loop between humans and Large Language Models (LLMs) can reduce the diversity of user beliefs and lock in incorrect beliefs.

The Lock-in Hypothesis: Stagnation by Algorithm

Frontier AI systems, such as large language models (LLMs) (Zhao et al., 2023), are increasingly influencing human beliefs and values (Fisher et al., 2024; Leib et al., 2021; Costello et al., 2024). This creates a self-reinforcing feedback loop: AI systems learn values from human data at pre- and post-training stages (Conneau & Lample, 2019; Bai et al., 2022; Santurkar et al., 2023), influence human opinions through their interactions, and then reabsorb those influenced beliefs, and so on. What equilibrium will this dynamic process reach?

https://arxiv.org/html/2506.06166

Synthetic Dataset Diversity

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