How Far Can Transformers Reason? The Locality Barrier and Inductive Scratchpad
The study by Emmanuel Abbe et al. delves into Transformers' reasoning limitations, introducing 'distribution locality' and proposing an 'inductive scratchpad' to enhance learning and generalization, highlighting challenges in composing syllogisms.
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The paper titled "How Far Can Transformers Reason? The Locality Barrier and Inductive Scratchpad" by Emmanuel Abbe and colleagues explores the limitations of Transformers in predicting new syllogisms and learning targets from scratch. It introduces the concept of 'distribution locality' to determine when weak learning is efficiently achievable by regular Transformers. The study shows that distributions with high locality are challenging to learn efficiently, particularly in composing syllogisms on long chains. The research indicates that an educated scratchpad breaking the locality at each step can aid in learning, while an agnostic scratchpad cannot overcome the locality barrier. Additionally, the introduction of an 'inductive scratchpad' is proposed to break the locality and enhance out-of-distribution generalization, such as doubling input size for certain arithmetic tasks. The findings combine experimental and theoretical approaches to shed light on the learning capabilities and limitations of Transformers in reasoning tasks.
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