Summary of Mechanism and Emergence Of Stacked Attention Heads in Multi-layer Transformers, by Tiberiu Musat
Mechanism and Emergence of Stacked Attention Heads in Multi-Layer Transformers
by Tiberiu Musat
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 This paper introduces the retrieval problem, a fundamental reasoning task that only certain transformer models can solve with a minimum number of layers growing logarithmically with input size. The authors empirically demonstrate that large language models can tackle this challenge under different prompting formulations without fine-tuning. To gain insights into transformers’ solution mechanisms, the authors train various transformers on a minimal formulation and uncover the learned attention maps. This study reveals the importance of an implicit curriculum in successful learning, leading to the emergence of specific attention head sequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can solve a simple problem called the retrieval problem. The problem requires computers to understand what’s important in a piece of text. Researchers found that some computer models can solve this problem without needing extra training. They used different ways to ask the questions and still got accurate answers. To see how these computer models work, they trained several models on a simple version of the problem. By looking at what parts of the text the models focus on, researchers discovered that the models learn to follow specific rules. This helps us understand how computers can solve complex problems like this one. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Prompting » Transformer
