This research paper proposes a new framework called ALTA for analyzing and understanding the capabilities of Transformer models. ALTA introduces a new programming language that allows researchers to express algorithms symbolically and then compile these programs into Transformer weights. The authors demonstrate how this framework can be used to prove that Transformers can represent algorithms that exhibit compositional generalization, such as computing parity and addition. The paper also introduces techniques for analyzing the learnability of these algorithms, including a novel method using intermediate supervision from program execution traces. This work contributes to the ongoing discussion about the theoretical limits and practical capabilities of Transformer models.

paper - http://arxiv.org/abs/2410.18077v1

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