Hallucination in Large Language Models (LLMs) is an inherent and unavoidable limitation.
Our sources:
Hallucination is Inevitable: An Innate Limitation of Large Language Models (Ziwei Xu, Sanjay Jain, Mohan Kankanhalli): https://arxiv.org/pdf/2401.11817
https://en.wikipedia.org/wiki/Hallucination_(artificial_intelligence)
The authors define hallucination as inconsistencies between an LLM's output and a computable ground truth function within a formal, simplified world. By applying learning theory results and diagonalization arguments, the paper demonstrates that LLMs, when used as general problem solvers, cannot learn all computable functions, thus inevitably producing incorrect or nonsensical information. This theoretical finding is then extrapolated to real-world LLMs, with the conclusion that hallucination cannot be entirely eliminated, even with advancements in model size, training data, or prompting techniques. The text also discusses hallucination-prone tasks (such as complex mathematical or logical reasoning), the limitations of current mitigation strategies, and the practical implications for the safe and ethical deployment of LLMs, emphasizing the need for external aids and human oversight in critical applications.
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