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Computer Science > Machine Learning

arXiv:2408.08210 (cs)
[Submitted on 15 Aug 2024]

Title:Does Reasoning Emerge? Examining the Probabilities of Causation
in Large Language Models

Authors:Javier Gonzalez, Aditya V. Nori
View a PDF of the paper titled Does Reasoning Emerge? Examining the
Probabilities of Causation in Large Language Models, by Javier Gonz\
'alez and Aditya V. Nori
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    Abstract:Recent advances in AI have been significantly driven by
    the capabilities of large language models (LLMs) to solve complex
    problems in ways that resemble human thinking. However, there is
    an ongoing debate about the extent to which LLMs are capable of
    actual reasoning. Central to this debate are two key
    probabilistic concepts that are essential for connecting causes
    to their effects: the probability of necessity (PN) and the
    probability of sufficiency (PS). This paper introduces a
    framework that is both theoretical and practical, aimed at
    assessing how effectively LLMs are able to replicate real-world
    reasoning mechanisms using these probabilistic measures. By
    viewing LLMs as abstract machines that process information
    through a natural language interface, we examine the conditions
    under which it is possible to compute suitable approximations of
    PN and PS. Our research marks an important step towards gaining a
    deeper understanding of when LLMs are capable of reasoning, as
    illustrated by a series of math examples.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2408.08210 [cs.LG]
          (or arXiv:2408.08210v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2408.08210
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Javier Gonzalez [view email]
[v1] Thu, 15 Aug 2024 15:19:11 UTC (8,071 KB)
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