Summary of On the Fragility Of Active Learners For Text Classification, by Abhishek Ghose and Emma Thuong Nguyen
On the Fragility of Active Learners for Text Classification
by Abhishek Ghose, Emma Thuong Nguyen
First submitted to arxiv on: 23 Mar 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 Active learning techniques aim to optimize the labeling process by selecting instances that are most valuable for learning. However, they lack “prerequisite checks,” making it difficult for practitioners to choose the best-suited algorithm for a dataset. This raises questions about when and how often active learning can reliably beat random sampling, as well as whether it’s reasonable to use active learning in an “Always ON” mode. The role of prediction pipelines in AL’s success is also crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Active learning techniques help optimize the labeling process by selecting instances that are most valuable for learning. This means choosing the right technique can make a big difference. But it’s hard to know which one to choose, as there isn’t a clear way to pick an algorithm best suited for a dataset. The question is whether active learning can really help and how much it depends on things like the data, budget, and prediction pipeline. |
Keywords
* Artificial intelligence * Active learning
