Curating Grounded Synthetic Data with Global Perspectives for Equitable AI

• URL: http://arxiv.org/abs/2406.10258v2
• Date: Tue, 18 Jun 2024 08:58:42 GMT
• Title: Curating Grounded Synthetic Data with Global Perspectives for Equitable AI
• Authors: Elin Törnquist, Robert Alexander Caulk,
• Abstract summary: We introduce a novel approach to creating synthetic datasets, grounded in real-world diversity and enriched through strategic diversification. We synthesize data using a comprehensive collection of news articles spanning 12 languages and originating from 125 countries, to ensure a breadth of linguistic and cultural representations. Preliminary results demonstrate substantial improvements in performance on traditional NER benchmarks, by up to 7.3%.
• Score: 0.5120567378386615
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The development of robust AI models relies heavily on the quality and variety of training data available. In fields where data scarcity is prevalent, synthetic data generation offers a vital solution. In this paper, we introduce a novel approach to creating synthetic datasets, grounded in real-world diversity and enriched through strategic diversification. We synthesize data using a comprehensive collection of news articles spanning 12 languages and originating from 125 countries, to ensure a breadth of linguistic and cultural representations. Through enforced topic diversification, translation, and summarization, the resulting dataset accurately mirrors real-world complexities and addresses the issue of underrepresentation in traditional datasets. This methodology, applied initially to Named Entity Recognition (NER), serves as a model for numerous AI disciplines where data diversification is critical for generalizability. Preliminary results demonstrate substantial improvements in performance on traditional NER benchmarks, by up to 7.3%, highlighting the effectiveness of our synthetic data in mimicking the rich, varied nuances of global data sources. This paper outlines the strategies employed for synthesizing diverse datasets and provides such a curated dataset for NER.

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