Two new arXiv papers explore the theoretical underpinnings of in-context learning (ICL) in transformers. One paper demonstrates how transformers can perform in-context logistic regression by implicitly executing normalized gradient descent steps within each layer. The other paper investigates nonlinear regression, showing how attention mechanisms can construct features that enable transformers to learn from examples without weight updates. AI
影响 These papers advance the theoretical understanding of how transformers learn from prompts, potentially guiding future model development and optimization.
排序理由 Two arXiv papers provide theoretical analysis of in-context learning mechanisms in transformers.
- arXiv
- In-context learning
- Logistic regression
- Nonlinear regression
- Normalized gradient descent
- Softmax attention
- Transformer
AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →
