>> Because this paper introduces their alternative method which simply runs the same query multiple times on the same LLM, without any context shared across queries. And then they run a similarity algorithm on the answers and pick the most common answer.
Years ago weather simulations started tweaking input params and running their models over and over. Discarding outliers, taking averages. It works pretty well.
Because LLM's mostly have random seeds (aka temperature) feeding them the same input and averaging the output is going to get you a better guess.
Lorenz also gives some clues (if not an outright explanation) as to why the "hallucination" problem is likely unsolvable.
If you buy into this line of thinking then it quickly becomes apparent that LLM's are more or less a dead end when it comes to AGI. Simulating isnt emulating... an LLM is as likely to become intelligent as a forecast is to control the weather.
https://en.wikipedia.org/wiki/Lorenz_system
Years ago weather simulations started tweaking input params and running their models over and over. Discarding outliers, taking averages. It works pretty well.
Because LLM's mostly have random seeds (aka temperature) feeding them the same input and averaging the output is going to get you a better guess.
Lorenz also gives some clues (if not an outright explanation) as to why the "hallucination" problem is likely unsolvable.
If you buy into this line of thinking then it quickly becomes apparent that LLM's are more or less a dead end when it comes to AGI. Simulating isnt emulating... an LLM is as likely to become intelligent as a forecast is to control the weather.