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Computer Science > Computation and Language

arXiv:2311.18805 (cs)
[Submitted on 30 Nov 2023]

Title:Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle
Unnatural Scrambled Text

Authors:Qi Cao, Takeshi Kojima, Yutaka Matsuo, Yusuke Iwasawa
Download a PDF of the paper titled Unnatural Error Correction: GPT-4
Can Almost Perfectly Handle Unnatural Scrambled Text, by Qi Cao and 3
other authors
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    Abstract:While Large Language Models (LLMs) have achieved
    remarkable performance in many tasks, much about their inner
    workings remains unclear. In this study, we present novel
    experimental insights into the resilience of LLMs, particularly
    GPT-4, when subjected to extensive character-level permutations.
    To investigate this, we first propose the Scrambled Bench, a
    suite designed to measure the capacity of LLMs to handle
    scrambled input, in terms of both recovering scrambled sentences
    and answering questions given scrambled context. The experimental
    results indicate that most powerful LLMs demonstrate the
    capability akin to typoglycemia, a phenomenon where humans can
    understand the meaning of words even when the letters within
    those words are scrambled, as long as the first and last letters
    remain in place. More surprisingly, we found that only GPT-4
    nearly flawlessly processes inputs with unnatural errors, even
    under the extreme condition, a task that poses significant
    challenges for other LLMs and often even for humans.
    Specifically, GPT-4 can almost perfectly reconstruct the original
    sentences from scrambled ones, decreasing the edit distance by
    95%, even when all letters within each word are entirely
    scrambled. It is counter-intuitive that LLMs can exhibit such
    resilience despite severe disruption to input tokenization caused
    by scrambled text.

Comments: EMNLP 2023 (with an additional analysis section in
          appendix)
Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2311.18805 [cs.CL]
          (or arXiv:2311.18805v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2311.18805
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Qi Cao [view email]
[v1] Thu, 30 Nov 2023 18:51:38 UTC (246 KB)
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