Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on
  Tasks where Thinking Makes Humans Worse
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.
672221bd3c54079d2cdeb460
Ryan Liu
672221bd3c54079d2cdeb461
Jiayi Geng
672221bd3c54079d2cdeb462
Addison J. Wu
672221bd3c54079d2cdeb463
Ilia Sucholutsky
672221bd3c54079d2cdeb464
Tania Lombrozo
672221bd3c54079d2cdeb465
Thomas L. Griffiths
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on
  Tasks where Thinking Makes Humans Worse
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.
Chain-of-thought prompting reduces performance in tasks such as implicit statistical learning, visual recognition, and classifying with patterns containing exceptions, while it retains or increases performance in tasks where humans benefit from verbal thinking.
chain-of-thought prompting implicit statistical learning visual recognition patterns containing exceptions inference-time reasoning zero-shot counterparts