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Computer Science > Computation and Language

arXiv:2405.15071 (cs)
[Submitted on 23 May 2024 (v1), last revised 27 May 2024 (this
version, v2)]

Title:Grokked Transformers are Implicit Reasoners: A Mechanistic
Journey to the Edge of Generalization

Authors:Boshi Wang, Xiang Yue, Yu Su, Huan Sun
View a PDF of the paper titled Grokked Transformers are Implicit
Reasoners: A Mechanistic Journey to the Edge of Generalization, by
Boshi Wang and 3 other authors
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    Abstract:We study whether transformers can learn to implicitly
    reason over parametric knowledge, a skill that even the most
    capable language models struggle with. Focusing on two
    representative reasoning types, composition and comparison, we
    consistently find that transformers can learn implicit reasoning,
    but only through grokking, i.e., extended training far beyond
    overfitting. The levels of generalization also vary across
    reasoning types: when faced with out-of-distribution examples,
    transformers fail to systematically generalize for composition
    but succeed for comparison. We delve into the model's internals
    throughout training, conducting analytical experiments that
    reveal: 1) the mechanism behind grokking, such as the formation
    of the generalizing circuit and its relation to the relative
    efficiency of generalizing and memorizing circuits, and 2) the
    connection between systematicity and the configuration of the
    generalizing circuit. Our findings guide data and training setup
    to better induce implicit reasoning and suggest potential
    improvements to the transformer architecture, such as encouraging
    cross-layer knowledge sharing. Furthermore, we demonstrate that
    for a challenging reasoning task with a large search space,
    GPT-4-Turbo and Gemini-1.5-Pro based on non-parametric memory
    fail badly regardless of prompting styles or retrieval
    augmentation, while a fully grokked transformer can achieve
    near-perfect accuracy, showcasing the power of parametric memory
    for complex reasoning.

Comments: 22 pages, 16 figures. Code and data: this https URL
Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2405.15071 [cs.CL]
          (or arXiv:2405.15071v2 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2405.15071
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Boshi Wang [view email]
[v1] Thu, 23 May 2024 21:42:19 UTC (7,877 KB)
[v2] Mon, 27 May 2024 03:55:35 UTC (7,877 KB)
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    Reasoners: A Mechanistic Journey to the Edge of Generalization,
    by Boshi Wang and 3 other authors
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