True for today’s static models during inference. Not true for self-supervised learning, not true during training or fine-tuning, of course. Ignores that LLMs might start continuous training in the future - there’s no fundamental or technical constraint that prevents LLM ‘plasticity’. And ignores that accumulating context/memories/skills/etc affects performance and might count as a valid analogy to what many people loosely call ‘neural plasticity’, which is sometimes casually mistaking knowledge for network modification.
Today, depending on which model you use. You’re making unstated assumptions. And that’s not a fundamental property of LLMs, it’s happenstance. LLMs are capable of ‘plasticity’, by design.