Summary of Diversity Over Quantity: a Lesson From Few Shot Relation Classification, by Amir Dn Cohen et al.
Diversity Over Quantity: A Lesson From Few Shot Relation Classification
by Amir DN Cohen, Shauli Ravfogel, Shaltiel Shmidman, Yoav Goldberg
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 an approach to improve few-shot relation classification (FSRC) models by increasing diversity in relation types. While most recent advancements in NLP focus on scaling data sizes, this work highlights the importance of diversity in relation types for FSRC performance. The authors demonstrate that training on a diverse set of relations enhances a model’s ability to generalize to unseen relations, even with fixed overall dataset size. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Few-shot relation classification is a task where models need to learn about new relationships using only a few examples. This paper shows that it’s more important to have many different types of relationships rather than just collecting lots of data. The researchers found that training on diverse relationship types helps the model learn better and generalize well to new, unseen relationships. |
Keywords
* Artificial intelligence * Classification * Few shot * Nlp
