Summary of Salutary Labeling with Zero Human Annotation, by Wenxiao Xiao et al.
Salutary Labeling with Zero Human Annotation
by Wenxiao Xiao, Hongfu Liu
First submitted to arxiv on: 27 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 The paper proposes a novel approach to active learning called salutary labeling, which automatically assigns labels to informative samples without human annotation. This method utilizes the influence function to select newly added samples and assign their most beneficial labels by maximizing their positive influence. The authors demonstrate the superior performance of this approach over traditional active learning strategies on nine benchmark datasets. Additionally, they explore practical applications of large language model fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to learn from data without needing humans to label it. Right now, we have to ask people questions and get answers for our machines to get better. But this takes time and money. The authors found a solution that lets computers decide which answers are most helpful and give them those labels. They tested their idea on many different datasets and showed it works better than the old way. It also has applications in fine-tuning large language models. |
Keywords
» Artificial intelligence » Active learning » Fine tuning » Large language model
