Experimental Contexts Can Facilitate Robust Semantic Property Inference
  in Language Models, but Inconsistently
Kanishka Misra Allyson Ettinger Kyle Mahowald
Recent zero-shot evaluations have highlighted important limitations in the
abilities of language models (LMs) to perform meaning extraction. However, it
is now well known that LMs can demonstrate radical improvements in the presence
of experimental contexts such as in-context examples and instructions. How well
does this translate to previously studied meaning-sensitive tasks? We present a
case-study on the extent to which experimental contexts can improve LMs'
robustness in performing property inheritance -- predicting semantic properties
of novel concepts, a task that they have been previously shown to fail on. Upon
carefully controlling the nature of the in-context examples and the
instructions, our work reveals that they can indeed lead to non-trivial
property inheritance behavior in LMs. However, this ability is inconsistent:
with a minimal reformulation of the task, some LMs were found to pick up on
shallow, non-semantic heuristics from their inputs, suggesting that the
computational principles of semantic property inference are yet to be mastered
by LMs.