A Survey of LLM $\times$ DATA
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2025
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| _version_ | 1866910979497918464 |
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| author | Zhou, Xuanhe He, Junxuan Zhou, Wei Chen, Haodong Tang, Zirui Zhao, Haoyu Tong, Xin Li, Guoliang Chen, Youmin Zhou, Jun Sun, Zhaojun Hui, Binyuan Wang, Shuo He, Conghui Liu, Zhiyuan Zhou, Jingren Wu, Fan |
| author_facet | Zhou, Xuanhe He, Junxuan Zhou, Wei Chen, Haodong Tang, Zirui Zhao, Haoyu Tong, Xin Li, Guoliang Chen, Youmin Zhou, Jun Sun, Zhaojun Hui, Binyuan Wang, Shuo He, Conghui Liu, Zhiyuan Zhou, Jingren Wu, Fan |
| contents | The integration of large language model (LLM) and data management (DATA) is rapidly redefining both domains. In this survey, we comprehensively review the bidirectional relationships. On the one hand, DATA4LLM, spanning large-scale data processing, storage, and serving, feeds LLMs with high quality, diversity, and timeliness of data required for stages like pre-training, post-training, retrieval-augmented generation, and agentic workflows: (i) Data processing for LLMs includes scalable acquisition, deduplication, filtering, selection, domain mixing, and synthetic augmentation; (ii) Data Storage for LLMs focuses on efficient data and model formats, distributed and heterogeneous storage hierarchies, KV-cache management, and fault-tolerant checkpointing; (iii) Data serving for LLMs tackles challenges in RAG (e.g., knowledge post-processing), LLM inference (e.g., prompt compression, data provenance), and training strategies (e.g., data packing and shuffling). On the other hand, in LLM4DATA, LLMs are emerging as general-purpose engines for data management. We review recent advances in (i) data manipulation, including automatic data cleaning, integration, discovery; (ii) data analysis, covering reasoning over structured, semi-structured, and unstructured data, and (iii) system optimization (e.g., configuration tuning, query rewriting, anomaly diagnosis), powered by LLM techniques like retrieval-augmented prompting, task-specialized fine-tuning, and multi-agent collaboration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of LLM $\times$ DATA Zhou, Xuanhe He, Junxuan Zhou, Wei Chen, Haodong Tang, Zirui Zhao, Haoyu Tong, Xin Li, Guoliang Chen, Youmin Zhou, Jun Sun, Zhaojun Hui, Binyuan Wang, Shuo He, Conghui Liu, Zhiyuan Zhou, Jingren Wu, Fan Databases Artificial Intelligence Computation and Language Information Retrieval Machine Learning The integration of large language model (LLM) and data management (DATA) is rapidly redefining both domains. In this survey, we comprehensively review the bidirectional relationships. On the one hand, DATA4LLM, spanning large-scale data processing, storage, and serving, feeds LLMs with high quality, diversity, and timeliness of data required for stages like pre-training, post-training, retrieval-augmented generation, and agentic workflows: (i) Data processing for LLMs includes scalable acquisition, deduplication, filtering, selection, domain mixing, and synthetic augmentation; (ii) Data Storage for LLMs focuses on efficient data and model formats, distributed and heterogeneous storage hierarchies, KV-cache management, and fault-tolerant checkpointing; (iii) Data serving for LLMs tackles challenges in RAG (e.g., knowledge post-processing), LLM inference (e.g., prompt compression, data provenance), and training strategies (e.g., data packing and shuffling). On the other hand, in LLM4DATA, LLMs are emerging as general-purpose engines for data management. We review recent advances in (i) data manipulation, including automatic data cleaning, integration, discovery; (ii) data analysis, covering reasoning over structured, semi-structured, and unstructured data, and (iii) system optimization (e.g., configuration tuning, query rewriting, anomaly diagnosis), powered by LLM techniques like retrieval-augmented prompting, task-specialized fine-tuning, and multi-agent collaboration. |
| title | A Survey of LLM $\times$ DATA |
| topic | Databases Artificial Intelligence Computation and Language Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2505.18458 |