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This panel discussion explores the implications of large language models on data management research and practices.

Retrieval-augmented Generation (RAG): What is There for Data Management Researchers? A discussion on research from a panel at LLM+Vector Data Workshop @ IEEE ICDE 2025

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Panel discussion reveals the role of large language models in data management, suggesting new research directions.

This panel discussion explores the implications of large language models on data management research and practices.
Reviewed various LLM applications across different domains.
Discussed automation in processes like data analysis and manipulation.
Explained limitations of LLMs including reasoning constraints and factual verification issues.
LLMs improve efficiency in data science and engineering tasks.
Issues identified include constraints in multi-step reasoning and lack of factual verification.
Discussion highlights the potential for evolving data management practices with LLM integration.

Khan et al. (2026) studied this question.

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View Full Paper

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Authors

Panel discussion reveals the role of large language models in data management, suggesting new research directions.

This panel discussion explores the implications of large language models on data management research and practices.
Reviewed various LLM applications across different domains.
Discussed automation in processes like data analysis and manipulation.
Explained limitations of LLMs including reasoning constraints and factual verification issues.
LLMs improve efficiency in data science and engineering tasks.
Issues identified include constraints in multi-step reasoning and lack of factual verification.
Discussion highlights the potential for evolving data management practices with LLM integration.

Khan et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark