Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)
arXiv:2606.05901v1 Announce Type: new 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 that the LLM did not have access to during training without resorting to expensive model fine-tuning. I
The proliferation of LLMs creates an immediate need to address their inherent hallucination risks, especially as they move into more sensitive applications.
Reducing hallucinations is critical for the reliable and trustworthy deployment of LLMs, directly impacting their commercial viability and public acceptance.
Techniques like RAG systems become more sophisticated and essential, shifting the focus of LLM development from pure model scale to robust augmentation and retrieval mechanisms.
- · RAG system developers
- · Enterprises deploying LLMs
- · Data management platforms
- · Graph database providers
- · LLM developers without strong RAG integration
- · Companies relying solely on unaugmented LLMs
Increased reliability of AI-powered information systems.
Faster adoption of LLMs in regulated and sensitive industries due to improved trustworthiness.
Enhanced competition in the AI market as effective hallucination mitigation becomes a key differentiator.
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Read at arXiv cs.CL