Summary of Implicit In-context Learning, by Zhuowei Li et al.
Implicit In-context Learning
by Zhuowei Li, Zihao Xu, Ligong Han, Yunhe Gao, Song Wen, Di Liu, Hao Wang, Dimitris N. Metaxas
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces Implicit In-context Learning (I2CL), a paradigm that reduces the computational and memory overhead of in-context learning (ICL) while maintaining minimal information loss. I2CL generates a context vector from demonstration examples, which is then injected into the model’s residual streams at inference time. This approach achieves few-shot level performance at zero-shot inference cost on nine real-world tasks across three model architectures. The paper also explores the representation of task-ids, enhancing task similarity detection and transfer learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary I2CL is a new way for large language models to learn from a few examples without using too much computer power or memory. It works by taking some example text and turning it into a special kind of vector. Then, when the model makes predictions, it uses this vector to help make better decisions. This approach is really good at doing tasks like summarizing text or answering questions, even if it’s never seen those tasks before. The paper also talks about how I2CL can be used to understand which tasks are similar and how to use that understanding to learn new things. |
Keywords
» Artificial intelligence » Few shot » Inference » Transfer learning » Zero shot
