SafeLLM: Extraction as a Hallucination-Resistant Alternative to Rewriting in Safety-Critical Settings
arXiv:2606.12897v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to access organisational documentation, including standard operating procedures (SOPs), HR policies and institutional guidelines. However, retrieval-augmented generation (RAG) systems that rely on free-form rewriting can introduce hallucinations and unstable trade-offs between completeness and conciseness, particularly in safety- and compliance-critical settings. Objectives: To evaluate extraction as a hallucination-resistant alternative to rewriting-based RAG and compare strategies that balance
The proliferation of LLMs into critical enterprise functions is rapidly exposing their limitations, particularly concerning hallucination risks in sensitive contexts, necessitating more robust solutions.
This research flags an emerging solution to a key vulnerability of LLMs, enabling their safer deployment in high-stakes environments where accuracy and compliance are paramount.
The shift from generative rewriting to controlled extraction could significantly enhance the reliability and trustworthiness of AI systems interacting with critical organizational data.
- · Enterprises with strict compliance needs
- · LLM developers prioritizing safety and accuracy
- · Adoption of LLMs in financial and legal sectors
- · Retrieval-Augmented Generation (RAG) system providers
- · LLM solutions prioritizing creativity over factual accuracy
- · Companies with low-quality internal documentation
- · Free-form generative AI in safety-critical settings
Increased trust and adoption of RAG systems in regulated industries due to reduced hallucination risks.
Development of specialized tools and frameworks for 'extraction as context' within LLM applications, becoming a best practice.
Enhanced regulatory frameworks specifically addressing AI hallucination and accuracy in enterprise deployments, driven by successful methods like extraction.
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Read at arXiv cs.CL