Summary of A Formal Framework For Understanding Length Generalization in Transformers, by Xinting Huang et al.
A Formal Framework for Understanding Length Generalization in Transformers
by Xinting Huang, Andy Yang, Satwik Bhattamishra, Yash Sarrof, Andreas Krebs, Hattie Zhou, Preetum Nakkiran, Michael Hahn
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A major challenge for transformers is their ability to generalize to sequences longer than those observed during training. While previous works have shown that transformers can either succeed or fail at length generalization depending on the task, a theoretical understanding of this phenomenon remains limited. This paper introduces a rigorous framework to analyze length generalization in causal transformers with learnable absolute positional encodings. The authors characterize functions that are identifiable in the limit from sufficiently long inputs with absolute positional encodings and prove the possibility of length generalization for a rich family of problems. Experimental validation shows that the theory predicts success or failure of length generalization across various tasks, including algorithmic and formal language processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are powerful tools that can process text and other data. One problem with transformers is that they often struggle to work well on very long sequences of data. In this paper, scientists try to understand why this happens and how to fix it. They create a new way to analyze transformer models and show that some types of data are better suited for length generalization than others. The results can help us predict when transformers will be able to handle long sequences of data. |
Keywords
» Artificial intelligence » Generalization » Transformer
