Lack of grounding can lead to hallucinations — instances where the model generates incorrect or misleading information. Building responsible and trustworthy AI systems is a core focus and addressing the challenge of hallucination in LLMs is crucial to achieving this goal.

DataGemma represents a significant leap forward in addressing the challenges associated with AI hallucinations by grounding its outputs in real-world data. By combining advanced LLM capabilities with robust retrieval techniques from Data Commons, Google aims to enhance the reliability and trustworthiness of AI-generated information across various applications.

Two-Pronged Approach Retrieval-Augmented Generation (RAG): This approach retrieves relevant contextual information before generating a response, grounding the output in verified data.

RIG: This method allows for real-time data retrieval during the response generation process, enhancing factual accuracy and reducing hallucinations by verifying generated information against external sources.