Are Large Language Models the New Interface for Data Pipelines?
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910480901079040 |
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| author | Junior, Sylvio Barbon Ceravolo, Paolo Groppe, Sven Jarrar, Mustafa Maghool, Samira Sèdes, Florence Sahri, Soror Van Keulen, Maurice |
| author_facet | Junior, Sylvio Barbon Ceravolo, Paolo Groppe, Sven Jarrar, Mustafa Maghool, Samira Sèdes, Florence Sahri, Soror Van Keulen, Maurice |
| contents | A Language Model is a term that encompasses various types of models designed to understand and generate human communication. Large Language Models (LLMs) have gained significant attention due to their ability to process text with human-like fluency and coherence, making them valuable for a wide range of data-related tasks fashioned as pipelines. The capabilities of LLMs in natural language understanding and generation, combined with their scalability, versatility, and state-of-the-art performance, enable innovative applications across various AI-related fields, including eXplainable Artificial Intelligence (XAI), Automated Machine Learning (AutoML), and Knowledge Graphs (KG). Furthermore, we believe these models can extract valuable insights and make data-driven decisions at scale, a practice commonly referred to as Big Data Analytics (BDA). In this position paper, we provide some discussions in the direction of unlocking synergies among these technologies, which can lead to more powerful and intelligent AI solutions, driving improvements in data pipelines across a wide range of applications and domains integrating humans, computers, and knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06596 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Are Large Language Models the New Interface for Data Pipelines? Junior, Sylvio Barbon Ceravolo, Paolo Groppe, Sven Jarrar, Mustafa Maghool, Samira Sèdes, Florence Sahri, Soror Van Keulen, Maurice Computation and Language Artificial Intelligence Databases A Language Model is a term that encompasses various types of models designed to understand and generate human communication. Large Language Models (LLMs) have gained significant attention due to their ability to process text with human-like fluency and coherence, making them valuable for a wide range of data-related tasks fashioned as pipelines. The capabilities of LLMs in natural language understanding and generation, combined with their scalability, versatility, and state-of-the-art performance, enable innovative applications across various AI-related fields, including eXplainable Artificial Intelligence (XAI), Automated Machine Learning (AutoML), and Knowledge Graphs (KG). Furthermore, we believe these models can extract valuable insights and make data-driven decisions at scale, a practice commonly referred to as Big Data Analytics (BDA). In this position paper, we provide some discussions in the direction of unlocking synergies among these technologies, which can lead to more powerful and intelligent AI solutions, driving improvements in data pipelines across a wide range of applications and domains integrating humans, computers, and knowledge. |
| title | Are Large Language Models the New Interface for Data Pipelines? |
| topic | Computation and Language Artificial Intelligence Databases |
| url | https://arxiv.org/abs/2406.06596 |