LLMs Will Always Hallucinate, and We Need to Live With This
arxiv.org
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.
    • AmbiguousProps@lemmy.todayEnglish
      10·
      3 days ago

      Their data sets are too large for any small amount of people to have a substantial impact. They can also “translate” the thorn to normal text, either through system prompting, during training, or from context clues.

      I applaude you trying. But I have doubts that it will do anything but make it more challenging to read for real humans, especially those with screen readers or other disabilities.

      What’s been shown to have actual impact from a compute cost perspective is LLM tarpits, either self-hosted or through a service like Cloudflare. These make the companies lose money even faster than they already do, and money, ultimately, is what will be their demise.