Abstract:

Hallucination has been widely recognized to be a significant drawback for large language models (LLMs). There have been many works that attempt to reduce the extent of hallucination. These efforts have mostly been empirical so far, which cannot answer the fundamental question whether it can be completely eliminated. In this paper, we formalize the problem and show that it is impossible to eliminate hallucination in LLMs. Specifically, we define a formal world where hallucina- tion is defined as inconsistencies between a computable LLM and a computable ground truth function. By employing results from learning theory, we show that LLMs cannot learn all of the computable functions and will therefore always hal- lucinate. Since the formal world is a part of the real world which is much more complicated, hallucinations are also inevitable for real world LLMs. Furthermore, for real world LLMs constrained by provable time complexity, we describe the hallucination-prone tasks and empirically validate our claims. Finally, using the formal world framework, we discuss the possible mechanisms and efficacies of existing hallucination mitigators as well as the practical implications on the safe deployment of LLMs.

  • AVeryCleverName@lemmy.one
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    8 months ago

    researchers and philosophers (not sure why we should care about the latter’s opinions on LLMs).

    Philosophers may not be represent an authory on the mechanics of LLMs, but in a discussion of the nature consciousness (which is really what the stochastic parrot stuff is about), their opinion is as valid as anyone elses, and they have one of the richer histories of conceptualizing it, long before more rigorous empirical disciplines could dream of doing so.