> LLM hallucination occurs because the model's primary objective
Stopped reading here. A model has no objective. It has only output.
The only objective is on the part of "AI" marketeers and it is there that the euphemism "hallucination" originates.
Stopped reading here. A model has no objective. It has only output.
The only objective is on the part of "AI" marketeers and it is there that the euphemism "hallucination" originates.
They (chatgpt et al) do move closer to “clear and coherent” by adding extra layers such as a beam search on LLM outputs. Good to remember that ChatGPT et al are products, not bare metal LLMs.
The "AI" marketeers' use of "hallucinate", "objective" and "predict" does precisely that.
This article isn't doing that, though. What it is calling the model objective is "producing clear and coherent text".
> Whether "producing clear and coherent text" is a good characterisation of a "predict the next token" loss function is another question though.
I agree. But lets us be clear that prediction too is a misnomer. Simply generating a word coherent with prior words is not prediction.
Is creating output not an objective?
But an objective function is a core element of ML models, no? And LLMs have objective functions, so far as I'm aware.
In star Trek there's the prime directive, the enterprise crew follows it because it's a core requirement of the federation.
Just because the LLM has an objective to answer queries for example does not mean it chose it. or has the intent itself as sapient being would.
I'm not buying :)