As Large Language Models become more ubiquitous across domains, it becomes important to examine their inherent limitations critically. This work argues that hallucinations in language models are not just occasional errors but an inevitable feature of these systems. We demonstrate that hallucinations stem from the fundamental mathematical and logical structure of LLMs. It is, therefore, impossible to eliminate them through architectural improvements, dataset enhancements, or fact-checking mechanisms. Our analysis draws on computational theory and Godel's First Incompleteness Theorem, which references the undecidability of problems like the Halting, Emptiness, and Acceptance Problems. We demonstrate that every stage of the LLM process-from training data compilation to fact retrieval, intent classification, and text generation-will have a non-zero probability of producing hallucinations. This work introduces the concept of Structural Hallucination as an intrinsic nature of these systems. By establishing the mathematical certainty of hallucinations, we challenge the prevailing notion that they can be fully mitigated.
It’s worth noting that humans aren’t immune to the problem either. The real solution will be to have a system that can do reasoning and have a heuristic for figuring out what’s likely a hallucination or not. The reason we’re able to do that is because we interact with the outside world, and we get feedback when our internal model diverges from it that allows us to bring it in sync.
There is a lot of hype around LLMs, and other forms of AI certainly should be getting more attention, but arguing that this tech no value is simply disingenuous. People really need to stop perseverating over the fact that this tech exists because it’s not going anywhere.
You seem to have a very US centric perspective on this tech the situation in China looks to be quite different. Meanwhile, whether you personally think the benefits are outweighed by whatever dangers you envision, the reality is that you can’t put toothpaste back in the tube at this point. LLMs will continue to be developed. The only question is how that’s going to be done and who will control this tech. I’d much rather see it developed in the open.
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It’s worth noting that humans aren’t immune to the problem either. The real solution will be to have a system that can do reasoning and have a heuristic for figuring out what’s likely a hallucination or not. The reason we’re able to do that is because we interact with the outside world, and we get feedback when our internal model diverges from it that allows us to bring it in sync.
LLMentalist is a mandatory read.
Stop making LLMs happen, we don’t need energy hungry bullshit generators for anything.
There are so many more important AIs that need attention and funding to help us with real problems.
LLMs won’t solve anything.
There is a lot of hype around LLMs, and other forms of AI certainly should be getting more attention, but arguing that this tech no value is simply disingenuous. People really need to stop perseverating over the fact that this tech exists because it’s not going anywhere.
Any benefits are by far outweighted by the cost and dangers.
Tell me more about the value when every LLM company is hemorrhaging money.
You seem to have a very US centric perspective on this tech the situation in China looks to be quite different. Meanwhile, whether you personally think the benefits are outweighed by whatever dangers you envision, the reality is that you can’t put toothpaste back in the tube at this point. LLMs will continue to be developed. The only question is how that’s going to be done and who will control this tech. I’d much rather see it developed in the open.