An AI leaderboard suggests the newest reasoning models used in chatbots are producing less accurate results because of higher hallucination rates. Experts say the problem is bigger than that
These anomalies occur because of the learning of the models. They don’t have them when newly released because they have been trained on “clean” data.
As the resolution of vectoring increases, the speed at which the data becomes corrupted increases.
Most “hallucinations” are not really hallucinations. What happens is people put in multiple prompts changing definitions put forward by the model and then the original data gets downranked, so when a question is asked, it repeats the false data the user put in. Then they put in the screenshot at the end, not showing all the garbage they put in.
Now remember models usually discard this information for a new session, as any new information has to go through a model approval process comparing it with the clean database originally mined.
Yeah, people don’t really realize that the reason these models are “free” is that we’re part of the learning process, and is a critical step towards AGI. I don’t think that the current generation of neural networks, not just GPTs in general, would be capable of such feat, especially as current neural networks are just very simplified models of neurons that can be represented with a simple matrix multiplication.
But this is expected.
These anomalies occur because of the learning of the models. They don’t have them when newly released because they have been trained on “clean” data.
As the resolution of vectoring increases, the speed at which the data becomes corrupted increases.
Most “hallucinations” are not really hallucinations. What happens is people put in multiple prompts changing definitions put forward by the model and then the original data gets downranked, so when a question is asked, it repeats the false data the user put in. Then they put in the screenshot at the end, not showing all the garbage they put in.
Now remember models usually discard this information for a new session, as any new information has to go through a model approval process comparing it with the clean database originally mined.
Yeah, people don’t really realize that the reason these models are “free” is that we’re part of the learning process, and is a critical step towards AGI. I don’t think that the current generation of neural networks, not just GPTs in general, would be capable of such feat, especially as current neural networks are just very simplified models of neurons that can be represented with a simple matrix multiplication.