Data-centric AI for
Natural Language Processing (NLP)

A clear and balanced overview, capturing both the basics and more advanced topics.

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Key Insights

  • "Data Quality isn’t a mere advantage; it’s an imperative."

  • Discover the emerging concept of Data-centric AI for Generative AI and NLP.

  • Capture both the basics, such as data
    annotation or LLMs dataset quality, and more advanced topics like confident learning, prompt engineering or privacy compliance.

  • Uncover insights to improve adaptability, efficiency, and reliability in NLP models.
  • Explore the future of Data-centric AI in NLP, unlocking innovative solutions and broader applications.

Why reading this White Paper?

Explore the frontier of Data-centric AI in NLP with guidance from our leading experts. This white paper serves as a guiding light, leading readers through the fundamental strategies, practical applications, and future potentials that Data-centric AI brings in reshaping success in Generative AI and NLP projects.

Dive into a wealth of insights on Data-centric AI approaches, showcasing their ability to improve model adaptability, efficiency, and overall performance. Tailored for professionals in the business and technology fields, as well as anyone interested in navigating the intersection of data and AI, this white paper is an essential read.

Embark on an enlightening exploration that equips you with the knowledge to navigate the nuances and harness the full potential of Data-centric AI in NLP.

Content Data-centric AI for Natural Language Processing NLP Positive Thinking Company

Content

  1. Prioritizing Quality Over Quantity in the LLM Era
  2. Why Data-centric AI?
  3. What is Data-centric AI?
  4. Quickstarting NLP Projects by Annotating Unlabeled Data via Weak Supervision
  5. From Silver to Gold – Enhancing the Data Annotation Quality via Human Feedback
  6. NLPOps/LLMOps to Streamline Reproducible Model Deployments
  7. Typical Dataset Development Tasks of LLM Training and Tuning
  8. Prompt Engineering as a Core Data-Centric AI Approach
  9. Typical Data Analysis Tasks in Data-centric NLP
  10. Analysis of Linguistic Text Properties
  11. Intersection of Model-centric and Data-centric AI: Behavioral model testing & XAI
  12. Ensuring Security and Privacy