Summary of Simulating Hard Attention Using Soft Attention, by Andy Yang et al.
Simulating Hard Attention Using Soft Attention
by Andy Yang, Lena Strobl, David Chiang, Dana Angluin
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Formal Languages and Automata Theory (cs.FL)
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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 Transformers using soft attention can effectively focus on specific positions by simulating hard attention, which is computationally expensive. Our study explores conditions under which this simulation is possible. We analyze various linear temporal logic variants, which were previously shown to be computable with hard attention transformers. We demonstrate how soft attention transformers can compute these formulas using unbounded positional embeddings or temperature scaling. Additionally, we show that temperature scaling enables softmax transformers to simulate a large subclass of average-hard attention transformers with the uniform-tieless property. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers can focus on specific parts by pretending they’re really focused. We looked at some special logic formulas that need this kind of focusing. We found out how soft attention transformers can do these calculations using extra information or by adjusting their temperature settings. This means that soft attention transformers can pretend to be like hard attention transformers for a lot of cases. |
Keywords
» Artificial intelligence » Attention » Softmax » Temperature
