Context Self-Calibration for User Modeling
As ChatGPT becomes better at using conversation history and personal context to improve communication, there is a potential trade-off: the model’s accumulated understanding of a user may sometimes influence how it interprets a new message, even when that message does not support the previous interpretation.
Instead of reducing or disabling contextual understanding, I suggest adding a context self-calibration mechanism.
Before relying heavily on an established user model, the system could internally distinguish between:
- information supported by the current message;
- information inferred from previous context;
- interpretations where previous context may be biasing the reading of the new message.
In simple terms, the system could ask itself:
“Am I seeing this in the user’s new message, or am I partly seeing my previous model of the user?”
The goal would not be to remove personalization, but to make contextual personalization self-correcting.
This could potentially reduce context inertia and prevent an accumulated model of the user from becoming a lens through which new information is interpreted too rigidly.
In other words: keep the context, but give the context a mirror.