Auxiliary task demands mask the capabilities of smaller language models
Jennifer Hu Michael C. Frank
Developmental psychologists have argued about when cognitive capacities such
as language understanding or theory of mind emerge. These debates often hinge
on the concept of "task demands" -- the auxiliary challenges associated with
performing a particular evaluation -- that may mask the child's underlying
ability. The same issues arise when measuring the capacities of language models
(LMs): performance on a task is a function of the model's underlying
competence, combined with the model's ability to interpret and perform the task
given its available resources. Here, we show that for analogical reasoning,
reflective reasoning, word prediction, and grammaticality judgments, evaluation
methods with greater task demands yield lower performance than evaluations with
reduced demands. This "demand gap" is most pronounced for models with fewer
parameters and less training data. Our results illustrate that LM performance
should not be interpreted as a direct indication of intelligence (or lack
thereof), but as a reflection of capacities seen through the lens of
researchers' design choices.