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Data analytics has become a key driver of commercial success in recent years. The ability to turn large data sets into actionable insights can mean the difference between a successful campaign and missed opportunities. Flipping the paradigm: Using AI to enhance dataquality What if we could change the way we think about dataquality?
Datascarcity in low-resource languages can be mitigated using word-to-word translations from high-resource languages. However, bilingual lexicons typically need more overlap with task data, leading to inadequate translation coverage. This approach faces challenges with domain specificity and performance compared to native data.
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.
Introduction The field of natural language processing (NLP) and language models has experienced a remarkable transformation in recent years, propelled by the advent of powerful large language models (LLMs) like GPT-4, PaLM, and Llama. Issues such as datascarcity, bias, and noise can significantly impact model performance.
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