Less is More: Adaptive Coverage for Synthetic Training Data
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908465202462720 |
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| author | Tavakkol, Sasan Springer, Max Bateni, Mohammadhossein Bulut, Neslihan Cohen-Addad, Vincent Hajiaghayi, MohammadTaghi |
| author_facet | Tavakkol, Sasan Springer, Max Bateni, Mohammadhossein Bulut, Neslihan Cohen-Addad, Vincent Hajiaghayi, MohammadTaghi |
| contents | Synthetic training data generation with Large Language Models (LLMs) like Google's Gemma and OpenAI's GPT offer a promising solution to the challenge of obtaining large, labeled datasets for training classifiers. When rapid model deployment is critical, such as in classifying emerging social media trends or combating new forms of online abuse tied to current events, the ability to generate training data is invaluable. While prior research has examined the comparability of synthetic data to human-labeled data, this study introduces a novel sampling algorithm, based on the maximum coverage problem, to select a representative subset from a synthetically generated dataset. Our results demonstrate that training a classifier on this contextually sampled subset achieves superior performance compared to training on the entire dataset. This "less is more" approach not only improves model accuracy but also reduces the volume of data required, leading to potentially more efficient model fine-tuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14508 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Less is More: Adaptive Coverage for Synthetic Training Data Tavakkol, Sasan Springer, Max Bateni, Mohammadhossein Bulut, Neslihan Cohen-Addad, Vincent Hajiaghayi, MohammadTaghi Machine Learning Synthetic training data generation with Large Language Models (LLMs) like Google's Gemma and OpenAI's GPT offer a promising solution to the challenge of obtaining large, labeled datasets for training classifiers. When rapid model deployment is critical, such as in classifying emerging social media trends or combating new forms of online abuse tied to current events, the ability to generate training data is invaluable. While prior research has examined the comparability of synthetic data to human-labeled data, this study introduces a novel sampling algorithm, based on the maximum coverage problem, to select a representative subset from a synthetically generated dataset. Our results demonstrate that training a classifier on this contextually sampled subset achieves superior performance compared to training on the entire dataset. This "less is more" approach not only improves model accuracy but also reduces the volume of data required, leading to potentially more efficient model fine-tuning. |
| title | Less is More: Adaptive Coverage for Synthetic Training Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.14508 |