Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)

📰 ArXiv cs.AI

Learn to reduce hallucinations in complex question answering using simple graph-based retrieval-augmented generation, improving LLM performance and reliability

advanced Published 5 Jun 2026

Action Steps

Implement a retrieval-augmented generation (RAG) system to reduce hallucinations in LLMs
Use a simple graph-based approach to represent knowledge and relationships
Train the RAG system on a dataset with complex questions and answers
Evaluate the system's performance using metrics such as accuracy and F1-score
Fine-tune the system to optimize its performance on specific question answering tasks

Who Needs to Know This

NLP engineers and researchers can benefit from this technique to improve the accuracy of their question answering systems, while product managers can use this to enhance the reliability of their AI-powered products

Key Insight

💡 Simple graph-based retrieval-augmented generation can effectively reduce hallucinations in complex question answering, making LLMs more reliable and accurate

Key Takeaways

Learn to reduce hallucinations in complex question answering using simple graph-based retrieval-augmented generation, improving LLM performance and reliability

Full Article

Title: Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)

Abstract:
arXiv:2606.05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing. Despite these advances, LLMs and LLM-based systems remain prone to a variety of failure modes. Retrieval-augmented generation (RAG) systems have emerged as a common deployment scenario seeking to both avoid the well known risk of the LLM "hallucinating" information, and to enable reasoning and question answering over proprietary information t

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