Do Language Models Exhibit the Same Cognitive Biases in Problem Solving
  as Human Learners?
Andreas Opedal Alessandro Stolfo Haruki Shirakami Ying Jiao Ryan Cotterell Bernhard Schölkopf Abulhair Saparov Mrinmaya Sachan
There is increasing interest in employing large language models (LLMs) as
cognitive models. For such purposes, it is central to understand which
cognitive properties are well-modeled by LLMs, and which are not. In this work,
we study the biases of LLMs in relation to those known in children when solving
arithmetic word problems. Surveying the learning science literature, we posit
that the problem-solving process can be split into three distinct steps: text
comprehension, solution planning and solution execution. We construct tests for
each one in order to understand which parts of this process can be faithfully
modeled by current state-of-the-art LLMs. We generate a novel set of word
problems for each of these tests, using a neuro-symbolic method that enables
fine-grained control over the problem features. We find evidence that LLMs,
with and without instruction-tuning, exhibit human-like biases in both the
text-comprehension and the solution-planning steps of the solving process, but
not during the final step which relies on the problem's arithmetic expressions
(solution execution).