Summary of On Explaining with Attention Matrices, by Omar Naim and Nicholas Asher
On Explaining with Attention Matrices
by Omar Naim, Nicholas Asher
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 paper investigates the relationship between attention weights (AW) in transformer models and predicted output, challenging recent research that found AW to be explanatorily irrelevant. The authors demonstrate that previous formal arguments are incorrect and introduce an efficient attention method to isolate essential components of attention matrices. They show that this efficient attention has a causal role in predicting model output for tasks requiring contextual information, contradicting claims that AW are not relevant. Empirical experiments on four datasets support the method, highlighting various properties of efficient attention. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computer models work and what makes them good at understanding language. Researchers thought that “attention” – a key part of these models – wasn’t important for predicting their outputs. But this new study shows that’s not true. They developed a special way to look at attention that helps explain why some models are better than others. This can help us create even better language models in the future. |
Keywords
» Artificial intelligence » Attention » Transformer
