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Computer Science > Computation and Language

arXiv:2404.15758 (cs)
[Submitted on 24 Apr 2024]

Title:Let's Think Dot by Dot: Hidden Computation in Transformer
Language Models

Authors:Jacob Pfau, William Merrill, Samuel R. Bowman
View a PDF of the paper titled Let's Think Dot by Dot: Hidden
Computation in Transformer Language Models, by Jacob Pfau and 2 other
authors
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    Abstract:Chain-of-thought responses from language models improve
    performance across most benchmarks. However, it remains unclear
    to what extent these performance gains can be attributed to
    human-like task decomposition or simply the greater computation
    that additional tokens allow. We show that transformers can use
    meaningless filler tokens (e.g., '......') in place of a chain of
    thought to solve two hard algorithmic tasks they could not solve
    when responding without intermediate tokens. However, we find
    empirically that learning to use filler tokens is difficult and
    requires specific, dense supervision to converge. We also provide
    a theoretical characterization of the class of problems where
    filler tokens are useful in terms of the quantifier depth of a
    first-order formula. For problems satisfying this
    characterization, chain-of-thought tokens need not provide
    information about the intermediate computational steps involved
    in multi-token computations. In summary, our results show that
    additional tokens can provide computational benefits independent
    of token choice. The fact that intermediate tokens can act as
    filler tokens raises concerns about large language models
    engaging in unauditable, hidden computations that are
    increasingly detached from the observed chain-of-thought tokens.

Comments:    17 pages, 10 figures
Subjects:    Computation and Language (cs.CL); Artificial
             Intelligence (cs.AI)
ACM classes: I.2.6
Cite as:     arXiv:2404.15758 [cs.CL]
             (or arXiv:2404.15758v1 [cs.CL] for this version)
             https://doi.org/10.48550/arXiv.2404.15758
             Focus to learn more
             arXiv-issued DOI via DataCite

Submission history

From: Jacob Pfau [view email]
[v1] Wed, 24 Apr 2024 09:30:00 UTC (579 KB)
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