VQV Signal
Large language models and AI agents show promise for zero-shot and few-shot data integration but face accuracy and cost issues in enterprises. The paper proposes knowledge-grounded LLMs and agents within retrieval-augmented generation to improve trustworthiness, scalability, and cost efficiency.
Large language models and AI agents show promise for zero-shot and few-shot data integration but face accuracy and cost issues in enterprises. The paper proposes knowledge-grounded LLMs and agents within retrieval-augmented generation to improve trustworthiness, scalability, and cost efficiency.
AI-assisted summary based on the listed source.
Large language models (LLMs) and AI agents have demonstrated strong potential for data integration in zero-shot and few-shot settings. However, they continue to face significant accuracy and cost challenges in enterprise environments due to a persistent knowledge gap. This paper envisions trustworthy, scalable,...
Addressing accuracy and cost challenges is critical for deploying AI-driven data integration in enterprise settings. Knowledge-grounded agentic approaches could enable more reliable and scalable integration solutions.
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VQV surfaced this signal because it is recent, relevant to AI Agents, connected to arXiv.
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