Summary of Arithmetic Transformers Can Length-generalize in Both Operand Length and Count, by Hanseul Cho and Jaeyoung Cha and Srinadh Bhojanapalli and Chulhee Yun
Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count
by Hanseul Cho, Jaeyoung Cha, Srinadh Bhojanapalli, Chulhee Yun
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 abstract presents research on improving the ability of Transformers to generalize to longer sequence lengths, particularly for arithmetic tasks like multi-operand addition and multiplication. The proposed approach, which includes task-specific scratchpads and multi-level Position Coupling, enables approximately 2-3x length generalization on these tasks, outperforming previous results. The method relies on the model focusing on a fixed number of tokens per prediction step and attending to the correct position using the multi-level Position Coupling mechanism. Theoretical analysis shows that a single-layer Transformer using this approach can solve multi-operand addition up to exponential complexity in embedding dimension. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps computers better understand longer sentences by improving their ability to generalize to new lengths. Current computer models, called Transformers, struggle with this task and are especially bad at tasks like adding or multiplying multiple numbers together. The scientists came up with a way to make the model focus on specific parts of the sentence and ignore others, allowing it to work better with longer sequences. |
Keywords
* Artificial intelligence * Embedding * Generalization * Transformer
