Summary of Investigating the Pre-training Dynamics Of In-context Learning: Task Recognition Vs. Task Learning, by Xiaolei Wang et al.
Investigating the Pre-Training Dynamics of In-Context Learning: Task Recognition vs. Task Learning
by Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao, Ji-Rong Wen
First submitted to arxiv on: 20 Jun 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 The proposed paper investigates the relationships between task recognition (TR) and task learning (TL) in the emergence of in-context learning (ICL). It examines the pre-training dynamics of ICL, revealing that TR and TL are competitive abilities during this phase. The study finds a negative correlation between competition and ICL performance, and identifies common pre-training factors that contribute to this competition. To manage this competition, the authors propose a simple method for integrating TR and TL at inference time using adaptive ensemble learning. This approach enables smaller models to outperform larger ones with more than twice the parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In-context learning (ICL) is a type of machine learning where models learn from demonstrations. The paper explores how two abilities, task recognition (TR) and task learning (TL), work together to make ICL happen. It finds that these abilities are competitive during pre-training and shows how this competition affects the final performance of ICL. The authors then propose a new method to help these abilities work better together. |
Keywords
» Artificial intelligence » Inference » Machine learning
