Summary of Enhancing In-context Learning Via Implicit Demonstration Augmentation, by Xiaoling Zhou et al.
Enhancing In-Context Learning via Implicit Demonstration Augmentation
by Xiaoling Zhou, Wei Ye, Yidong Wang, Chaoya Jiang, Zhemg Lee, Rui Xie, Shikun Zhang
First submitted to arxiv on: 27 Jun 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 A new approach to improving the effectiveness of in-context learning (ICL) for large pre-trained language models (PLMs) is proposed. ICL enables PLMs to make predictions without updating parameters, but its performance heavily relies on the quality and quantity of demonstrations. The authors tackle this challenge by enriching representations of demonstrations using their deep feature distribution and revealing a novel logit calibration mechanism integrated with statistical properties. This leads to a simple yet efficient method that improves accuracy across diverse PLMs and tasks, reduces performance variance among varying demonstrations, permutations, and templates, and addresses imbalanced class distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can make predictions without being updated, thanks to in-context learning (ICL). But this works best when the training data is good. The authors of a new paper find a way to make ICL work better by changing how they look at the training data. They show that this helps improve accuracy and makes the results more consistent. It also helps with problems where some classes have much fewer examples than others. |
