Summary of Understanding Factual Recall in Transformers Via Associative Memories, by Eshaan Nichani et al.
Understanding Factual Recall in Transformers via Associative Memories
by Eshaan Nichani, Jason D. Lee, Alberto Bietti
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Information Theory (cs.IT); Machine Learning (stat.ML)
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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 ability of large language models to perform factual recall tasks. It shows that shallow transformers can achieve near-optimal storage capacity by combining associative memories. The authors first prove that the storage capacities of linear and MLP associative memories scale linearly with parameter count. They then introduce a synthetic factual recall task, demonstrating that a transformer with self-attention and an MLP can attain 100% accuracy when either the self-attention or MLP parameters scale linearly with the number of facts. The paper also analyzes the gradient flow trajectory of a simplified linear attention model trained on the task, revealing sequential learning behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart at remembering facts! Researchers wanted to see how good they could be if they used different ways to store information. They found that even simple models can remember lots of facts as long as they have enough “memory” (like a brain). The paper shows this by creating a special task where the model has to recall facts, and it gets better at it when it has more “memory”. It’s like how you learn new words or math problems by practicing! |
Keywords
» Artificial intelligence » Attention » Recall » Self attention » Transformer
