AIBearisharXiv – CS AI · May 127/10
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Position: Avoid Overstretching LLMs for every Enterprise Task
A new research position argues that enterprises should stop treating large language models as monolithic solutions for all tasks and instead use them primarily for structured data extraction within modular architectures. The paper contends that LLMs have inherent capacity limits for enterprise knowledge needs and proposes delegating computation and storage to specialized components like knowledge bases and symbolic systems for better reliability and cost efficiency.