Summary of Im-context: In-context Learning For Imbalanced Regression Tasks, by Ismail Nejjar et al.
IM-Context: In-Context Learning for Imbalanced Regression Tasks
by Ismail Nejjar, Faez Ahmed, Olga Fink
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes an alternative to traditional deep imbalanced regression models, which often fail to generalize effectively in regions with highly imbalanced label distributions. The method, called in-context learning, conditions the model given a prompt sequence and a new query input, without requiring any parameter updates. This paradigm shift is shown to be effective in reducing bias within regions of high imbalance. Empirical evaluations across various real-world datasets demonstrate that in-context learning outperforms existing methods in scenarios with high levels of imbalance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions when we have lots of data, but some groups are really rare. Right now, our models get stuck and can’t learn from these rare examples. The new idea is to teach the model by giving it a few examples that are similar to what it’s trying to predict. This way, the model learns to make better predictions for those hard-to-reach groups. It works really well on real-world data sets. |
Keywords
* Artificial intelligence * Prompt * Regression
