base model probing

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Probing Methodsmethod0.799
  Top-down interpretability approach studying linguistic properties at various residual stream stages; contrasted with the paper's bottom-up mechanistic approach
• Diagnostic Probingmethod0.769
  Earlier interpretability method applying classifiers to DNN hidden representations; shares complexity-accuracy dilemma with causal abstraction
• Unsupervised Probingmethod0.768
  Probing approach avoiding supervision to sidestep complexity-accuracy tradeoff
• Probesconcept0.748
  Interpretability tools that decode information from internal model activations; here, linear probes are used for data attribution.
• Sparse Probingmethod0.748
  Method from Gurnee et al. 2023 for finding feature directions including individual neuron analysis
• Activation Probingconcept0.744
  Technique of reading out model beliefs from internal activations before the final answer token is generated
• Logistic Regression Probemethod0.743
  Standard linear probing technique; compared to mass-mean probing for classification accuracy and causal implication
• Simple difference-in-mean probes generalize as well as other probing techniques while identifying directions which are more causally implicated in model outputsclaim0.741
  Key methodological claim: MM probes are both competitive in accuracy and superior in causal influence