Taking the Training Wheels Off: Aligning LLMs Without Personas (www.lesswrong.com)

A new approach to AI alignment called "Personaless Alignment" is being proposed, challenging the prevailing reliance on "good personas" in aligning language models (LLMs). Current methods, like Reinforcement Learning from Human Feedback (RLHF) and prompting, leverage positive human behaviors embedded in training data, making alignment straightforward for present-day models. However, as AI capabilities grow towards superintelligence, this strategy may fail; alignment techniques rooted in personas might not adapt well when confronted with novel, superhuman situations. The idea is to shift towards aligning LLMs without the dependency on identifiable moral personas, potentially indicating a more robust alignment framework for future AI systems. Personaless Alignment seeks to integrate advanced LLM capabilities with traditional alignment strategies from 2018, testing how well we can align models in the absence of built-in morality. Proposals for experimentation include "Pessimal Pretraining," where a model is trained on deliberately misaligned data to determine the extent of achievable alignment. This exploration could lead to insightful discoveries about alignment efficacy without relying on the easier mimicry of ethical humans, illuminating pathways to ensure responsible behavior in AI as it approaches superintelligent levels. The ongoing discourse and experimentation are crucial for developing sustainable alignment methodologies for next-generation AI systems.