mlsanityData to 14 Oct 2026

Data Analysis in the Era of Generative AI

arXiv 2409.18475 ↗First posted 27 Sep 2024cs.AI cs.HCcited by 6

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Abstract (arXiv)

This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow by translating high-level user intentions into executable code, charts, and insights. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps. Finally, we discuss the research challenges that impede the development of these AI-based systems such as enhancing model capabilities, evaluating and benchmarking, and understanding end-user needs.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 102 of its 141 references that are in the map. First posted on arXiv 27 Sep 2024.

Builds on

102 of 141 references.

References · 102

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38 more
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50 most cited of 102 All 102 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.

  1. 2024A Survey on Large Language Model-based Agents for Statistics and Data Sciencecited by 22
  2. 2025Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Studycited by 7
  3. 2025Less Data, More Security: Advancing Cybersecurity LLMs Specialization via Resource-Efficient Domain-Adaptive Continuous Pre-training with Minimal Tokenscited by 4
  4. 2026FAMOSE: A ReAct Approach to Automated Feature Discoverycited by 4
  5. 2024From PDFs to Structured Data: Utilizing LLM Analysis in Sports Database Managementcited by 1
  6. 2025Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspectivecited by 1