Enough of unconstrained generative AI outputs. It often leads to wrong or harmful outputs. How to make LLM safe?
Time for constraints in models.
How can constraints help?
— — No unintended outputs
— — By controlling models, we control the right outputs
— — The regularization of gen AI can be done by governments by looking over the inbuilt constraints.
— — Constraints are needed to avoid the wrong meaning of the same word.
— — For example, a prompt with Word Knife can trigger a wrong output.
— — A wrong output can trigger some wrong reactions by innocent people reading it.
— — Hence, governments must sanction AI companies to use constraints in their LLM models.
— — We right now aim only at Loss Minimization.
— — The feasible region is the entire internet, now in which gen AI finds solutions.
— The feasible region in an optimization problem is the region that are satisfied by constraints.
— Right now feasible region is the whole internet, including the dark Net.
—As no constraints are set as of now.
— — We need to decrease the feasible region to a limited region from where LLMs can find solutions.
— — In layman’s terms, we need to set boundary points to gen AI feasible search regions.
— — We need it, as all internet data can’t be fed again in LLM, we have moved ahead.
— — So we tell LLM, what is right to search in the form of reducing the feasible region from the whole internet to several cricket grounds.
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References
(PDF) Unconstrained LLMs, How Long? Safe, secure, and trusted constraint-based LLMs