1.6k citations · 11.3k across the 73 of their papers we have counts for
6 papers · 1 filter
What Algorithms can Transformers Learn? A Study in Length Generalization
Hattie Zhou, Arwen Bradley, Etai Littwin +5
Large language models exhibit surprising emergent generalization properties, yet also struggle on many simple reasoning tasks such as arithmetic and parity. This raises the questio…
Adaptivity and Modularity for Efficient Generalization Over Task Complexity
Samira Abnar, Omid Saremi, Laurent Dinh +8
Can transformers generalize efficiently on problems that require dealing with examples with different levels of difficulty? We introduce a new task tailored to assess generalizatio…
When can transformers reason with abstract symbols?
Enric Boix-Adsera, Omid Saremi, Emmanuel Abbe +3
We investigate the capabilities of transformer models on relational reasoning tasks. In these tasks, models are trained on a set of strings encoding abstract relations, and are the…
Boolformer: Symbolic Regression of Logic Functions with Transformers
Stéphane d'Ascoli, Arthur Renard, Vassilis Papadopoulos +3
We introduce Boolformer, a Transformer-based model trained to perform end-to-end symbolic regression of Boolean functions. First, we show that it can predict compact formulas for c…
Transformers learn through gradual rank increase
Enric Boix-Adsera, Etai Littwin, Emmanuel Abbe +2
We identify incremental learning dynamics in transformers, where the difference between trained and initial weights progressively increases in rank. We rigorously prove this occurs…
Generalization on the Unseen, Logic Reasoning and Degree Curriculum
Emmanuel Abbe, Samy Bengio, Aryo Lotfi +1
This paper considers the learning of logical (Boolean) functions with a focus on the generalization on the unseen (GOTU) setting, a strong case of out-of-distribution generalizatio…