Out-of-Distribution (OOD) Generalization

Machine learning generalization when training and test distributions differ; linked to causal invariance.

Neighborhood — ranked by edge-count

paper

claim

EI and normalized EI could serve as a unified metric for out-of-distribution generalization.
supports
Conjecture that maximizing EI yields causal representations invariant to distribution shifts.

concept

Out-of-Distribution Probe Generalization
related_to
The capacity of a probe trained on one true/false dataset to accurately classify statements from topically and structurally different datasets

hypothesis

cosine ≥ 0.65 · no typed edge

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

Generalizationconcept0.789
Ability to apply learned solutions to novel circumstances.
CoT improves in-distribution but may harm out-of-distribution generalizationclaim0.781
Interpretation of scope generalization results
Scope generalization: CoT boosts 2-digit in-distribution but worsens 3-4 digit OODfinding0.773
CoT increases dr for OOD operands.
Generalisationconcept0.773
Ability to respond appropriately to novel situations based on past regularities; fundamental to learning and intelligence.
generalization (abstraction)concept0.770
Abstracting from specific memories (e.g., specific leaves) to general lessons (food).
scope generalizationconcept0.744
Generalization from 2-digit to 3-4 digit arithmetic; limited by mismatch dr.
task generalizationconcept0.741
The ability to generalize across tasks; lacking in latent methods.
SAE features generalize to images despite training only on text, indicating out-of-distribution robustness.claim0.722
A promising property for interpretability analysis off-distribution.