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Computer Science > Computation and Language

arXiv:2401.11817 (cs)
[Submitted on 22 Jan 2024]

Title:Hallucination is Inevitable: An Innate Limitation of Large
Language Models

Authors:Ziwei Xu, Sanjay Jain, Mohan Kankanhalli
Download a PDF of the paper titled Hallucination is Inevitable: An
Innate Limitation of Large Language Models, by Ziwei Xu and 2 other
authors
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    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 hallucination
    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 hallucinate. 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.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:2401.11817 [cs.CL]
          (or arXiv:2401.11817v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2401.11817
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Ziwei Xu [view email]
[v1] Mon, 22 Jan 2024 10:26:14 UTC (291 KB)
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