LLM/Agent-as-Data-Analyst: A Survey

• URL: http://arxiv.org/abs/2509.23988v3
• Date: Mon, 27 Oct 2025 02:52:57 GMT
• Title: LLM/Agent-as-Data-Analyst: A Survey
• Authors: Zirui Tang, Weizheng Wang, Zihang Zhou, Yang Jiao, Bangrui Xu, Boyu Niu, Dayou Zhou, Xuanhe Zhou, Guoliang Li, Yeye He, Wei Zhou, Yitong Song, Cheng Tan, Xue Yang, Chunwei Liu, Bin Wang, Conghui He, Xiaoyang Wang, Fan Wu,
• Abstract summary: Large language models (LLMs) and agent techniques have brought a fundamental shift in the functionality and development paradigm of data analysis tasks.<n>LLMs enable complex data understanding, natural language, semantic analysis functions, and autonomous pipeline orchestration.
• Score: 54.08761322298559
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) and agent techniques have brought a fundamental shift in the functionality and development paradigm of data analysis tasks (a.k.a LLM/Agent-as-Data-Analyst), demonstrating substantial impact across both academia and industry. In comparison with traditional rule or small-model based approaches, (agentic) LLMs enable complex data understanding, natural language interfaces, semantic analysis functions, and autonomous pipeline orchestration. From a modality perspective, we review LLM-based techniques for (i) structured data (e.g., NL2SQL, NL2GQL, ModelQA), (ii) semi-structured data (e.g., markup languages understanding, semi-structured table question answering), (iii) unstructured data (e.g., chart understanding, text/image document understanding), and (iv) heterogeneous data (e.g., data retrieval and modality alignment in data lakes). The technical evolution further distills four key design goals for intelligent data analysis agents, namely semantic-aware design, autonomous pipelines, tool-augmented workflows, and support for open-world tasks. Finally, we outline the remaining challenges and propose several insights and practical directions for advancing LLM/Agent-powered data analysis.

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