Summary of Neural Decompiling Of Tracr Transformers, by Hannes Thurnherr et al.
Neural Decompiling of Tracr Transformers
by Hannes Thurnherr, Kaspar Riesen
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a step towards explaining the inner workings of transformer architecture-based neural networks. A dataset is created using Transformer Compiler for RASP (Tracr) to pair transformer weights with corresponding RASP programs. A model is built and trained to recover RASP code from compiled models, demonstrating interpretable decompilation of Tracr-compiled transformer weights. The model achieves exact reproduction on over 30% of test objects, while the remaining 70% can be reproduced with minor errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformer-based neural networks have made significant progress in pattern recognition and machine learning. However, their inner workings are not well-explained. This paper takes a first step towards fixing this by creating a dataset and building a model to recover RASP code from compiled models. The result is an interpretable decompilation of Tracr-compiled transformer weights. |
Keywords
» Artificial intelligence » Machine learning » Pattern recognition » Transformer
