Summary of Towards Understanding the Word Sensitivity Of Attention Layers: a Study Via Random Features, by Simone Bombari and Marco Mondelli
Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features
by Simone Bombari, Marco Mondelli
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 have achieved exceptional success in NLP tasks, but understanding why attention layers are suitable for these tasks requires a deeper analysis. Our research focuses on the key property of word sensitivity (WS), which is crucial for capturing contextual meaning in long sentences. We demonstrate that attention layers exhibit high WS, whereas standard random features show low WS that decays with sentence length. This difference enables attention-based models to learn and generalize better than traditional methods. Specifically, we show that attention layers can differentiate between two sentences differing by only one word, whereas random features cannot. Our theoretical findings are validated through experiments on the BERT-Base embeddings of the imdb review dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Have you ever wondered why transformers work so well for natural language processing tasks? One key reason is that they can capture subtle meanings in sentences by paying attention to specific words. We studied how attention works in these models and found that it’s much better at learning from long sentences than traditional methods. This means that attention-based models can learn to tell apart two sentences that differ only by one word! Our research shows why this is the case and how it leads to better generalization performance. |
Keywords
* Artificial intelligence * Attention * Bert * Generalization * Natural language processing * Nlp
