The Problem:
The “beigeification” of Large Language Models (LLMs) refers to a trend where different AI models produce increasingly homogeneous, bland, or “safe” answers. This is due to shared training data and tuning methods. This trend is also known as “semantic collapse” or “model collapse”.
For more on the subject, see the paper “Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)“ by Liwei Jiang et al.
My Suggestion for ChatGPT:
Include the use of Lévy Flight among the potential solutions you develop for avoiding beigeification. The new algorithms you develop should use Lévy Flight to value and encourage variation and exploration, rather than just rewarding the most probable outcome.