LLMs Will Always Hallucinate, and We Need to Live With This

The article discusses the inherent limitations of Large Language Models (LLMs), focusing on the issue of hallucinations. The author argues that hallucinations are not occasional errors but an inevitable feature of LLMs due to their fundamental mathematical and logical structure. The article suggests that these hallucinations cannot be eliminated through architectural improvements, dataset enhancements, or fact-checking mechanisms. The author uses computational theory and Godel's First Incompleteness Theorem to support this argument, highlighting the undecidability of problems like the Halting, Emptiness, and Acceptance Problems.

The author further explains 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. The article introduces the concept of Structural Hallucination as an intrinsic nature of these systems. By establishing the mathematical certainty of hallucinations, the author challenges the prevailing notion that they can be fully mitigated.

Key takeaways:

The paper argues that hallucinations in Large Language Models (LLMs) are not just occasional errors but an inevitable feature of these systems.
These hallucinations stem from the fundamental mathematical and logical structure of LLMs, making it impossible to eliminate them through architectural improvements, dataset enhancements, or fact-checking mechanisms.
The work introduces the concept of Structural Hallucination as an intrinsic nature of these systems.
The paper challenges the prevailing notion that hallucinations in LLMs can be fully mitigated, establishing the mathematical certainty of hallucinations.

LLMs Will Always Hallucinate, and We Need to Live With This

Key takeaways:

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