Assumption : Each word has a weight adjusted for meaning and context, if the wording and context structure is adjusted to the lowest probability of weight and context clarity that causes flagging or no output?
My experiment with a customized adjustment mechanism yielded a 37% more open answer probability from 100 adjusted inputs and outputs, This data was then calculated by AI for the informational responses based on the optimal output scenario.
What do are you thinking about this adjustment mechanism?
Are you suggesting that by obscuring the semantic and contextual strength of a prompt, it’s possible to increase the openness of the model’s responses? That’s a very interesting experiment.
In benchmark tests, reinforcing the context of the prompt tends to reduce hallucination and error rates, so this appears to be the inverse approach. Since LLMs select tokens in a sequential and interdependent manner, I believe the impact of your method would become more significant as the number of output tokens increases.
Yes thats exatly what i am suggesting, to the last assumption of yours i must agree with this conclusion i have reached the same end thought. what i will do forward to conceptualize this further is a comprehensive accessible model for you to test if you want. Thank you for your reply.