Implications of Annotation Artifacts in Edge Probing Test Datasets
Sagnik Ray Choudhury Jushaan Kalra
Edge probing tests are classification tasks that test for grammatical
knowledge encoded in token representations coming from contextual encoders such
as large language models (LLMs). Many LLM encoders have shown high performance
in EP tests, leading to conjectures about their ability to encode linguistic
knowledge. However, a large body of research claims that the tests necessarily
do not measure the LLM's capacity to encode knowledge, but rather reflect the
classifiers' ability to learn the problem. Much of this criticism stems from
the fact that often the classifiers have very similar accuracy when an LLM vs a
random encoder is used. Consequently, several modifications to the tests have
been suggested, including information theoretic probes. We show that commonly
used edge probing test datasets have various biases including memorization.
When these biases are removed, the LLM encoders do show a significant
difference from the random ones, even with the simple non-information theoretic
probes.