Summary of Alvin: Active Learning Via Interpolation, by Michalis Korakakis et al.
ALVIN: Active Learning Via INterpolation
by Michalis Korakakis, Andreas Vlachos, Adrian Weller
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers introduce Active Learning Via INterpolation (ALVIN), a novel approach to minimize annotation effort by selecting the most useful instances from a pool of unlabeled data. ALVIN addresses the issue of models relying on shortcuts for predictions by conducting intra-class interpolations between examples from under-represented and well-represented groups, creating anchors that expose the model to regions of the representation space that counteract the influence of shortcuts. This method is evaluated on six datasets encompassing sentiment analysis, natural language inference, and paraphrase detection, demonstrating improved performance compared to state-of-the-art active learning methods in both in-distribution and out-of-distribution generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ALVIN is a new way for machines to learn by choosing the most important things from a big group of things they don’t know about. Right now, these machines often make mistakes because they’re relying on easy shortcuts instead of really understanding what’s going on. ALVIN tries to fix this by creating special points in space that help the machine see things it wasn’t seeing before. This makes the machine learn better and make fewer mistakes. |
Keywords
» Artificial intelligence » Active learning » Generalization » Inference
