arXiv:2605.29940v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted for synthetic data generation, significantly reducing annotation costs. However, most existing studies treat synthesis as a set of isolated tasks and overlook a more fundamental question: whether a model can learn to synthesize by accumulating experience from past tasks and transferring it to future ones. In this work, we introduce StreamSynth, a new setting in which synthesis tasks arrive sequentially and experience from historical tasks provides informative signals for future synthesis. To a
The increasing maturity and widespread adoption of large language models for data generation necessitates more sophisticated learning paradigms, moving beyond isolated tasks to continuous adaptation.
This research introduces a novel approach for LLMs to continuously learn and improve synthetic data generation, potentially revolutionizing the efficiency and quality of AI training data.
The focus shifts from one-off synthetic data generation to a system where LLMs accumulate and transfer experience across sequential tasks, leading to more resilient and efficient AI development pipelines.
- · AI developers
- · Data-dependent industries
- · Companies with limited annotation budgets
- · Researchers in continuous learning
- · Monotonous data annotation services
- · Models reliant on static, expensive datasets
Reduced costs and increased agility in developing high-quality datasets for AI model training.
Accelerated innovation in AI by making advanced training data more accessible and dynamic.
Potentially democratizes advanced AI development by significantly lowering data barrier to entry for smaller firms or research groups.
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Read at arXiv cs.AI