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> 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
by chrisjj 3y ago
> 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.
- JimtheCoder 3y ago"A model has no objective. It has only output." Is creating output not an objective?
- Wicked_Engineer 3y agoObjective signifies intent, no?
- jwestbury 3y agoMaybe, somewhere, in the minds of the creator of the model. But an objective function is a core element of ML models, no? And LLMs have objective functions, so far as I'm aware.
- gremlinsinc 3y agoSure, but it doesn't need to be organization the LLMs objective. A soldier could have an objective that they don't agree with or didn't choose themselves. 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.
- neilmock 3y agoNo, it has only input. Divergence from reality (insofar as it can be centered) is a hallmark of human thought as well, so perhaps the models are simply performing.
- rcxdude 3y agoIt's reasonable to call minimising the training loss function the "objective" of the LLM, I think (said loss functions are often called objective functions, after all), though we must careful that the overloading such term in an anthropomorphic fashion. Whether "producing clear and coherent text" is a good characterisation of a "predict the next token" loss function is another question though. I would say it probably isn't, but it's a resonable hypotheses to say that the nature of the training is why the output tends to bias towards plausible but incorrect text as opposed to some variation of "I don't know".
- gharman 3y agoAgreed there is a semantic argument here. I don’t think anyone is anthropomorphizing LLMs. I’ve had to adopt “objective” with one customer instead of “task” (my preferred default term) because their industry overloads “task” with something else. Now that said, as you point out, the real objective is next token prediction not “help the user with a truth bomb.” and it just happens to have emergent accidental usefulness (I think a natural bias from the training data). 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.
- chrisjj 3y ago> I don’t think anyone is anthropomorphizing LLMs The "AI" marketeers' use of "hallucinate", "objective" and "predict" does precisely that.
- chrisjj 3y ago> It's reasonable to call minimising the training loss function the "objective" of the LLM 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.
- swexbe 3y agoObjective as in objective function.
- chrisjj 3y agoSo "LLM hallucination occurs because the model's primary objective function is to generate text that is coherent and contextually appropriate"? I'm not buying :)