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Trend of Synthetic Data Adoption in LLM Training
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The rapid advancement of large language models (LLMs) is transforming the AI landscape, but it also faces a critical challenge: the growing demand for high-quality training data that outpaces the available supply. As LLMs become more sophisticated and widely adopted across industries, the need for diverse, representative datasets tailored to specific applications is skyrocketing. Synthetic data generation has emerged as a promising solution to bridge this gap, enabling the creation of realistic, privacy-preserving datasets at scale. Meanwhile, efforts are underway to address the unique challenges of low-resource languages in LLM development, aiming to promote linguistic diversity and inclusion in the AI era.

Training Data Demand Trends

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The AI training dataset market is projected to grow rapidly, from $2.39 billion in 2023 to $17.04 billion by 2032, a CAGR of 24.7%, driven by the increasing adoption of AI across industries.1 However, the performance, accuracy, and fairness of AI models heavily depend on the quality and diversity of the training data, and the supply of premium datasets is lagging behind the burgeoning demand.2
  • Collecting, annotating, and validating large-scale, high-quality training datasets requires substantial expertise, time, and resources, creating a supply-demand gap that is a key bottleneck in AI development today.3
  • To address this challenge, companies are turning to techniques like data synthesis, active learning, and weak supervision to generate high-quality training data more efficiently, while collaborative data ecosystems and marketplaces are emerging to improve access to datasets.4
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Synthetic Data Adoption

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Synthetic data is poised to play a major role in the future development of LLMs as the technology matures and adoption grows across industries:
  • By 2024, it's estimated that 60% of data used for AI training will be synthetic.1 Large tech companies like NVIDIA, IBM, and Google are investing in developing open-source synthetic data generation pipelines and methods specifically for LLM training, helping democratize access to high-quality training data.23
  • Synthetic data can improve LLM performance by generating diverse data covering edge cases and rare scenarios that are hard to find in real datasets, reducing hallucination and bias issues.41 Parameter-efficient techniques like LoRA are being combined with differential privacy to generate useful synthetic datasets while preserving the privacy of the original data.3
However, challenges remain - synthetic data quality depends on how well it captures real data patterns, and it can still reflect biases. Human oversight is still needed, and synthetic data alone is not a complete solution.4
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Microsoft's Orca and Phi

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Microsoft's Orca and Phi projects demonstrate the immense potential of synthetic data for training powerful yet efficient LLMs:
  • Orca-2 and Phi-3, despite being much smaller than typical LLMs (under 15B parameters), achieved competitive or even superior performance on complex reasoning tasks by leveraging carefully curated synthetic datasets, showing synthetic data's ability to democratize access to powerful AI.12
  • Orca's and Phi's training pipelines exemplify a hybrid approach that combines filtered web data with synthetic data generated by larger models, leveraging the internet's broad knowledge and the reasoning abilities of synthetic data. This emerging best practice efficiently trains capable small LLMs.34
  • As seen in Orca's and Phi's evaluations, synthetic data-trained models can exhibit lower toxicity and bias than purely web-trained models, likely due to the greater control over the generated data's quality and diversity.2
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AI Success with Synthetic Data

Here are some more success stories demonstrating the impact of synthetic data in AI applications across various industries: Synthetic data enabled a telemedicine company to improve its AI model for predicting fall risks in elderly patients by combining its proprietary data with external socio-demographic datasets in a privacy-preserving manner. The resulting next-generation risk prediction model explored new data synergies and allowed direct, safe monetization of data.1 A large investment bank used synthetic data to develop a personalized AI system for advising small and medium-sized corporate clients. By creating a synthetic version of its confidential client database, the bank could safely share data with an external AI consulting firm to build a predictive model, which the bank then used to tailor advice to clients' specific needs.1 Synthetic data accelerates AI enablement for organizations with limited or unusable datasets, such as those stored in thousands of PDFs or spreadsheets. Synthetic data models can play a critical role in defining overall data strategy, modernization, cloud migration, and deployment of AI-ready data pipelines in such cases.2 Synthetic datasets have been shown to produce more accurate results than models trained on real-world data in many cases, while eliminating privacy, copyright, and ethical concerns. Over 60% of data used for AI and analytics projects is expected to be synthetically generated by 2024.2 These success stories highlight the transformative potential of synthetic data in unlocking the value of sensitive or scarce datasets, driving AI innovation across sectors, and enabling new data-driven business models and personalized services.
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Supporting Low-Resource Languages

Governments can play a crucial role in supporting the development of language models for low-resource languages (LRLs) by providing funding, infrastructure, and collaborative initiatives.1 Key actions include:
  • Investing in education and training programs to build local expertise in natural language processing, machine learning, and LRL technologies.2
  • Developing policies that promote language diversity, data sharing, and responsible AI, such as mandating the use of local languages in public services and setting standards for data privacy.3
  • Partnering with private companies and NGOs to leverage their expertise and resources for LRL technology development and deployment, ensuring models are culturally appropriate and responsive to local needs.4
By taking a proactive and holistic approach, governments can help bridge the gap between high-resource and low-resource languages in the era of AI, making the benefits of language technologies accessible to all while preserving linguistic diversity.23
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Related
How can synthetic data generation benefit low-resource languages
What are the challenges of using synthetic data for low-resource languages
How do large language models perform with low-resource languages
Are there specific tools or models designed for synthetic data in low-resource languages
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