Summary of Alta: Compiler-based Analysis Of Transformers, by Peter Shaw et al.
ALTA: Compiler-Based Analysis of Transformers
by Peter Shaw, James Cohan, Jacob Eisenstein, Kenton Lee, Jonathan Berant, Kristina Toutanova
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new programming language called ALTA, which can be mapped to Transformer weights using a compiler. Building on prior work from RASP and Tracr, ALTA offers improved expressiveness through loops and Universal Transformers. The language is used to demonstrate how Transformers can represent length-invariant algorithms for parity and addition computations, as well as solve the SCAN benchmark for compositional generalization tasks without intermediate decoding steps. Additionally, the paper introduces tools for analyzing cases where an algorithm’s expressibility is established but fails to induce desired behavior through end-to-end training. The authors also explore using ALTA execution traces as a fine-grained supervision signal for training, enabling further experiments and theoretical analyses on learnability and modeling decisions. Key contributions include the ALTA framework specification, symbolic interpreter, and weight compiler, which are made available for further applications and insights. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new programming language called ALTA that helps computers understand how to perform certain tasks more efficiently. It’s like a special set of instructions that tells the computer what to do. The authors show how this language can be used to solve problems that were previously hard or impossible for computers to figure out on their own. They also provide tools to help people understand when and why these solutions work, and how they can be improved. Overall, the goal is to make computers more intelligent and better at solving complex problems. |
Keywords
» Artificial intelligence » Generalization » Transformer
