aiqbench analysis
This research demonstrates that incorporating copy tasks into training data enables transformer models to master analogical reasoning by learning to attend to informative elements and generalize across heterogeneous alphabets. The study further reveals that head-level attention weights causally influence model outputs, allowing for the steering of specific analogy tasks while approximating underlying computational algorithms. These findings offer critical insights into improving the generalization capabilities of large models toward human-like reasoning patterns.
Transformer See, Transformer Do: Copying as an Intermediate Step in Learning Analogical Reasoning
arXiv:2604.06501v2 Announce Type: replace-cross 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 copy tasks in the training data. Furthermore, generalization to new alphabets improves when models are trained with more heterogeneous datasets. For the best training run, our 3-layer encoder-decoder model performs on par with frontier models on our letter-string analogy datasets. The MLC approach also enables some generalization to compositions of trained transformations, but not to completely novel transformations. To understand how the model solves the analogies, we identify an algorithm for one analogy task that approximates the model's computations. We verify this using interpretability analyses and show that head-level attention weights causally affect model output, allowing steering between different analogy tasks. We discuss the implications of our findings for generalization capabilities of larger models and the parallels to human analogical reasoning.
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 copy tasks in the training data. Furthermore, generalization to new alphabets improves when models are trained with more heterogeneous datasets. For the best training run, our 3-layer encoder-decoder model performs on par with frontier models on our letter-string analogy datasets. The MLC approach also enables some generalization to compositions of trained transformations, but not to completely novel transformations. To understand how the model solves the analogies, we identify an algorithm for one analogy task that approximates the model's computations. We verify this using interpretability analyses and show that head-level attention weights causally affect model output, allowing steering between different analogy tasks. We discuss the implications of our findings for generalization capabilities of larger models and the parallels to human analogical reasoning.
Machine Learning (cs.LG); Computation and Language (cs.CL)
arXiv:2604.06501 [cs.LG]
(or arXiv:2604.06501v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2604.06501 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 298 words
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