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

arXiv: https://arxiv.org/abs/2405.03963 · HTML: https://mlsanity.com/paper/2405.03963/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 7 May 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs](https://mlsanity.com/paper/2312.05934/) (2023)
- [TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation](https://mlsanity.com/paper/1810.02720/) (2018)
- [TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation](https://mlsanity.com/paper/1804.09769/) (2018)
- [Natural Language to Structured Query Generation via Meta-Learning](https://mlsanity.com/paper/1803.02400/) (2018)
- [Semantic Parsing with Syntax- and Table-Aware SQL Generation](https://mlsanity.com/paper/1804.08338/) (2018)
- [Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT](https://mlsanity.com/paper/2404.09296/) (2024)
- [Hallucination-minimized Data-to-answer Framework for Financial Decision-makers](https://mlsanity.com/paper/2311.07592/) (2023)
- [Bidirectional Attention for SQL Generation](https://mlsanity.com/paper/1801.00076/) (2017)

## Built on by (most cited first, 1 of 1)

- [Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Triples Modeling](https://mlsanity.com/paper/2409.11283/) (2024)

## Abstract (arXiv)

Large language models (LLMs) with retrieval augmented-generation (RAG) have been the optimal choice for scalable generative AI solutions in the recent past. Although RAG implemented with AI agents (agentic-RAG) has been recently popularized, its suffers from unstable cost and unreliable performances for Enterprise-level data-practices. Most existing 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 prompting for question-answering capabilities from Enterprise-data tables. The source tables here are highly fluctuating and large in size and the proposed framework enables structured responses in under 10 seconds per query. Additionally, we propose a five metric scoring module that detects and reports hallucinations in the LLM responses. Our proposed system and scoring metrics achieve >90% confidence scores across hundreds of user queries in the sustainability, financial health and social media domains. Extensions to the proposed extreme RAG architectures can enable heterogeneous source querying using LLMs.