# Reducing hallucination in structured outputs via Retrieval-Augmented Generation

**Authors:** Patrice Béchard, Orlando Marquez Ayala  
**arXiv:** [2404.08189](https://arxiv.org/abs/2404.08189)  
**Date:** 2024-04-12  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.08189v1/

## Key points

- Authors: Patrice Béchard and Orlando Marquez Ayala
- Issue: Hallucinations in Generative AI (GenAI) systems
- Proposed Solution: Retrieval-Augmented Generation (RAG)
- Reduces hallucinations in structured outputs
- Enhances generalization capabilities of large language models (LLMs)
- Improves efficiency and feasibility of deployments
- Addresses challenges in real-world applications and user acceptance
- Contributes valuable insights to AI technology development

## Summary

In their paper titled "Reducing hallucination in structured outputs via Retrieval-Augmented Generation," authors Patrice Béchard and Orlando Marquez Ayala address the issue of inaccurate or misleading information generated by Generative AI (GenAI) systems. This phenomenon, known as hallucinations, poses a significant challenge for real-world applications and user acceptance. To combat this problem, the authors propose a system that utilizes Retrieval Augmented Generation (RAG) to improve the quality of structured outputs produced by an enterprise application that creates workflows based on natural language requirements. By implementing RAG, the proposed system effectively reduces hallucinations and enhances the generalization capabilities of large language models (LLMs) in out-of-domain scenarios. The authors also demonstrate how incorporating a small yet well-trained retriever encoder can reduce the size and resource requirements of LLM-based systems. This optimization not only improves efficiency but also makes deployments more feasible for organizations seeking to implement AI-driven solutions. Overall, Béchard and Marquez Ayala's research highlights the importance of addressing hallucinations in GenAI systems and showcases how innovative approaches like RAG can significantly enhance performance and usability in practical applications. Their findings contribute valuable insights to the ongoing development and deployment of AI technologies across various industries.