Large language models (LLMs) are powerful tools, but they can sometimes generate incorrect or nonsensical information, often called "hallucinations." These inaccuracies pose a significant problem, especially when dealing with sensitive areas like data privacy. In a new research paper, Meta introduces "Ingest-And-Ground," a novel approach to combatting hallucinations in LLMs focused on privacy applications. Imagine an AI assistant tasked with ensuring a social media platform adheres to complex privacy regulations. Without proper grounding, the AI could easily misinterpret the rules or even fabricate non-existent ones, leading to serious compliance issues. Meta's solution combines two powerful techniques. First, they continually pre-train an LLM (like Llama 3.1) on a massive dataset of privacy-related documents. This allows the model to absorb the nuances of privacy regulations and best practices. Second, they employ a Retrieval-Augmented Generation (RAG) system. Think of RAG as providing the LLM with a reliable fact-checker. When the LLM receives a question, the RAG system fetches relevant passages from a trusted knowledge base and presents them alongside the query. This grounding in factual information helps prevent the LLM from straying into hallucination territory. Meta’s experiments show promising results. Their tests revealed that the combination of continual pre-training and semantic RAG significantly improved the LLM's accuracy and reduced hallucinations. Compared to a standard LLM, their enhanced model showed a substantial increase in generating correct and relevant answers regarding privacy compliance. This research has important real-world implications. By improving the reliability of LLM responses, Meta aims to streamline processes like privacy reviews, making it easier for engineers and compliance experts to work together effectively. While the initial results are encouraging, the journey doesn't end here. The researchers at Meta plan to expand their data sources, refine the retrieval process, and tailor their system for specific niches within privacy regulations. This ongoing research is crucial for building a future where AI can be trusted to handle sensitive information accurately and responsibly.