ERATTA: Extreme RAG for Table To Answers with Large Language Models

arXiv:2405.03963v1 Announce Type: cross
Abstract: Large language models (LLMs) with residual augmented-generation (RAG) have been the optimal choice for scalable generative AI solutions in the recent past. However, the choice of use-cases that incorporate RAG with LLMs have been either generic or extremely domain specific, thereby questioning the scalability and generalizability of RAG-LLM approaches. In this work, we propose a unique LLM-based system where multiple LLMs can be invoked to enable data authentication, user query routing, data retrieval and custom …

abstract ai solutions arxiv cases cs.ai cs.lg domain generative generative ai solutions however language language models large language large language models llms rag residual scalability scalable solutions table type