You use formal verification for logic and rags for source data.
In other words - say you have a model that is semi-smart, often makes mistakes in logic, but sometimes gives valid answers. You use it to “brainstorm” physical equations and then use formal provers to weed out the correct answer.
Even if the llm is correct 0.001% of the time, it’s still better than the current algorithms which are essentially brute forcing.
I’m still confused as to the value of training on tweets though in that scenario?
If you need to effectively provide this whole secondary dataset to have better answers, what value do the tweets add to training other than perhaps sentiment analysis or response stylization?
In other words - say you have a model that is semi-smart, often makes mistakes in logic, but sometimes gives valid answers. You use it to “brainstorm” physical equations and then use formal provers to weed out the correct answer.
Even if the llm is correct 0.001% of the time, it’s still better than the current algorithms which are essentially brute forcing.