In two Transformer-based L2 speaking graders, a concept's linear recoverability in a hidden layer does not predict its influence on the predicted score, and sparse-autoencoder probing attenuates measured sensitivity.
Analysing Bias in Spoken Language Assessment Using Concept Activation Vectors,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.AI 1years
2026 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors
In two Transformer-based L2 speaking graders, a concept's linear recoverability in a hidden layer does not predict its influence on the predicted score, and sparse-autoencoder probing attenuates measured sensitivity.