Summary of From Random to Informed Data Selection: a Diversity-based Approach to Optimize Human Annotation and Few-shot Learning, by Alexandre Alcoforado et al.
From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning
by Alexandre Alcoforado, Thomas Palmeira Ferraz, Lucas Hideki Okamura, Israel Campos Fama, Arnold Moya Lavado, Bárbara Dias Bueno, Bruno Veloso, Anna Helena Reali Costa
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This research paper proposes an innovative approach to building initial datasets for natural language processing models through automatic and informed data selection. The current methods often rely on crowdsourcing or zero-shot learning, but these approaches have limitations. For instance, crowdsourcing can introduce annotator biases, while zero-shot learning is less effective than fully supervised counterparts. Another common method involves human annotation of random datapoints, which can be inefficient and biased towards majority classes in imbalanced datasets. To address these challenges, the authors develop a novel architecture that selects data for human annotation based on characteristics that maximize diversity and minimize quantity, ultimately improving model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in natural language processing: getting good data to train machines to understand human language. Right now, people usually rely on crowdsourcing or guesswork to get the data they need. But these methods have some major limitations. Crowdsourcing can lead to biased results, while guessing often means wasting time and resources. To make things better, this research proposes a new way to pick the most important data points for humans to label, which helps machines learn faster and more accurately. |
Keywords
* Artificial intelligence * Natural language processing * Supervised * Zero shot
