Transformer See, Transformer Do: Copying as an Intermediate Step in Learning Analogical Reasoning
Philipp Hellwig, Willem Zuidema, Claire E. Stevenson, Martha Lewis · Apr 7, 2026 · Citations: 0
Data freshness
Extraction: FreshCheck recency before relying on this page for active eval decisions. Use stale pages as context and verify against current hub results.
Metadata refreshed
Apr 7, 2026, 10:15 PM
RecentExtraction refreshed
Apr 10, 2026, 7:24 AM
FreshExtraction source
Persisted extraction
Confidence 0.15
Abstract
Analogical reasoning is a hallmark of human intelligence, enabling us to solve new problems by transferring knowledge from one situation to another. Yet, developing artificial intelligence systems capable of robust human-like analogical reasoning has proven difficult. In this work, we train transformers using Meta-Learning for Compositionality (MLC) on an analogical reasoning task (letter-string analogies) and assess their generalization capabilities. We find that letter-string analogies become learnable when guiding the models to attend to the most informative problem elements induced by including copying tasks in the training data. Furthermore, generalization to new alphabets becomes better when models are trained with more heterogeneous datasets, where our 3-layer encoder-decoder model outperforms most frontier models. The MLC approach also enables some generalization to compositions of trained transformations, but not to completely novel transformations. To understand how the model operates, we identify an algorithm that approximates the model's computations. We verify this using interpretability analyses and show that the model can be steered precisely according to expectations derived from the algorithm. Finally, we discuss implications of our findings for generalization capabilities of larger models and parallels to human analogical reasoning.