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Computer Science > Machine Learning

arXiv:2410.21333 (cs)
[Submitted on 27 Oct 2024]

Title:Mind Your Step (by Step): Chain-of-Thought can Reduce
Performance on Tasks where Thinking Makes Humans Worse

Authors:Ryan Liu, Jiayi Geng, Addison J. Wu, Ilia Sucholutsky, Tania
Lombrozo, Thomas L. Griffiths
View a PDF of the paper titled Mind Your Step (by Step):
Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes
Humans Worse, by Ryan Liu and 5 other authors
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    Abstract:Chain-of-thought (CoT) prompting has become a widely
    used strategy for working with large language and multimodal
    models. While CoT has been shown to improve performance across
    many tasks, determining the settings in which it is effective
    remains an ongoing effort. In particular, it is still an open
    question in what settings CoT systematically reduces model
    performance. In this paper, we seek to identify the
    characteristics of tasks where CoT reduces performance by drawing
    inspiration from cognitive psychology, looking at cases where (i)
    verbal thinking or deliberation hurts performance in humans, and
    (ii) the constraints governing human performance generalize to
    language models. Three such cases are implicit statistical
    learning, visual recognition, and classifying with patterns
    containing exceptions. In extensive experiments across all three
    settings, we find that a diverse collection of state-of-the-art
    models exhibit significant drop-offs in performance (e.g., up to
    36.3% absolute accuracy for OpenAI o1-preview compared to GPT-4o)
    when using inference-time reasoning compared to zero-shot
    counterparts. We also identify three tasks that satisfy condition
    (i) but not (ii), and find that while verbal thinking reduces
    human performance in these tasks, CoT retains or increases model
    performance. Overall, our results show that while there is not an
    exact parallel between the cognitive processes of models and
    those of humans, considering cases where thinking has negative
    consequences for human performance can help us identify settings
    where it negatively impacts models. By connecting the literature
    on human deliberation with evaluations of CoT, we offer a new
    tool that can be used in understanding the impact of prompt
    choices and inference-time reasoning.

          Machine Learning (cs.LG); Artificial Intelligence (cs.AI);
Subjects: Computation and Language (cs.CL); Computers and Society
          (cs.CY)
Cite as:  arXiv:2410.21333 [cs.LG]
          (or arXiv:2410.21333v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2410.21333
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Ryan Liu [view email]
[v1] Sun, 27 Oct 2024 18:30:41 UTC (2,612 KB)
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    View a PDF of the paper titled Mind Your Step (by Step):
    Chain-of-Thought can Reduce Performance on Tasks where Thinking
    Makes Humans Worse, by Ryan Liu and 5 other authors
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