[TJSomething]

A hallucination is often an indication that the model doesn't know something. Then, the internal signal gets dominated by noise from the seeded training weights. Efforts to eliminate hallucinations with a single model have found success by asking the same question in different ways and only taking answers that agree. Logically, you could get more durable results from multiple models on the same prompt.

[supriyo-biswas]

We had this article the other day[1] about how multiple LLMs can hallucinate about the same thing, so this is not guaranteed to remove hallucinations that are caused by poor or insufficient training data.

[1] https://news.ycombinator.com/item?id=43222027

[boxed]

I don't see why any of that makes logical sense. These models require such enormous training data that they pretty much MUST use the same training data to a very large degree. The training data itself is what they spit out. So "hallucinations" are just the training data you get out, which is the entire point of the models in the first place. There is no difference between an hallucination and a correct answer from the perspective of the math.

[neuronic]

Isn' it just statistical word pattern prediction based on training data? These models likely don't "know" something anyway and cannot verify "truth" and facts. Reasoning attempts seem to me basically just like looping until the model finds a self-satisfying equilibrium state with different output.

In that way, LLMs are more human than, say, a database or a book containing agreed-upon factual information which can be directly queried on demand.

Imagine if there was just ONE human with human limitations on the entire planet who was taught everything for a long time - how reliable do you think they are with information retrieval? Even highly trained individuals (e.g. professors) can get stuff wrong on their specific topics at times. But this is not what we expect and demand from computers.