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Over the past decade, Artificial Intelligence (AI) has made significant advancements, leading to transformative changes across various industries, including healthcare and finance. In recent years, it has become increasingly evident that even the most advanced AI models are only as good as the data they are trained on.
The navigator then evaluates the fidelity of these instructions, filtering out low-qualitydata to train a better generator in subsequent iterations. This iterative refinement ensures continuous improvement in both the dataquality and the models’ performance. Trending: LG AIResearch Releases EXAONE 3.5:
Instead of relying on organic events, we generate this data through computer simulations or generative models. Synthetic data can augment existing datasets, create new datasets, or simulate unique scenarios. Specifically, it solves two key problems: datascarcity and privacy concerns.
Ensuring dataquality, addressing potential biases, and maintaining strict privacy and security standards for sensitive medical data are the major concerns. Data Availability and Quality : Obtaining high-quality, domain-specific datasets is crucial for training accurate and reliable DSLMs.
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