arXiv:2605.30407v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data. Existing LLM-based data curation methods primarily rely on human-designed workflows, leaving it unexamined whether LLMs can autonomously execute an end-to-end data engineering pipeline for model specialization. We formalize \textbf{Autonomous Agentic Data Engineering}, a novel task designed to evaluate LLMs as autonomous data engineers that drive model specializatio
LLMs have reached a sufficient level of capability to be considered for autonomous execution of complex, multi-step engineering tasks, prompting research into their higher-order functions.
Autonomous Agentic Data Engineering could significantly reduce the human effort and specialized expertise needed to tailor LLMs for specific applications, accelerating their deployment across industries.
The process of adapting and specializing large language models moves from human-designed workflows to potentially self-driving, LLM-orchestrated processes, greatly improving efficiency and scalability.
- · AI developers
- · Enterprises adopting AI
- · Data engineering tools/platforms
- · Manual data curation services
- · General-purpose LLM providers without specialization tools
Reduced time and cost for fine-tuning and specializing LLMs for domain-specific tasks.
Rapid proliferation of specialized AI agents across diverse industries due to lower barriers to entry for customization.
The emergence of 'AI-engineered' data, where datasets are optimized by AI for AI, potentially leading to novel data quality and ethical challenges.
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Read at arXiv cs.LG