Summary of Are Large Language Models the New Interface For Data Pipelines?, by Sylvio Barbon Junior et al.
Are Large Language Models the New Interface for Data Pipelines?
by Sylvio Barbon Junior, Paolo Ceravolo, Sven Groppe, Mustafa Jarrar, Samira Maghool, Florence Sèdes, Soror Sahri, Maurice Van Keulen
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper discusses Large Language Models (LLMs) that have gained attention for their ability to process text with human-like fluency and coherence. These models can be used for various data-related tasks and enable innovative applications across AI-related fields such as eXplainable Artificial Intelligence, Automated Machine Learning, and Knowledge Graphs. The capabilities of LLMs in natural language understanding and generation, combined with their scalability and state-of-the-art performance, make them valuable for Big Data Analytics and extracting valuable insights to drive data-driven decisions at scale. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can be used for various tasks such as processing text, making data-driven decisions, and improving AI solutions. The paper explores how these models can be used across different applications and domains, including those that integrate humans, computers, and knowledge. |
Keywords
» Artificial intelligence » Attention » Language understanding » Machine learning
