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Google has announced the release of DataGemma, a series of open models designed to enhance the factual accuracy of large language models (LLMs). DataGemma utilizes real-world data from Data Commons through two approaches: Retrieval Interleaved Generation (RIG) and Retrieval Augmented Generation (RAG). These methods help to ground LLMs in reliable, public statistical data, thereby reducing instances of hallucinations in AI responses. The models, including the fine-tuned Gemma 2 and a 27B-sized version, are aimed at improving responsible AI development and fact-checking capabilities.
Google debuts DataGemma models that use real world data from Google-created Data Commons to reduce hallucinations in queries revolving around statistical data (@mr_bumss / VentureBeat) https://t.co/bPF0PBP1fS 📫 Subscribe: https://t.co/OyWeKSRpIM https://t.co/nJzoVJ3BJ4
Google AI Introduces DataGemma: A Set of Open Models that Utilize Data Commons through Retrieval Interleaved Generation (RIG) and Retrieval Augmented Generation (RAG) https://t.co/PsxGfkNvqU #DataGemma #AI #Reliability #DataCommons #Innovation #ai #news #llm #ml #research #ai… https://t.co/CvqENHb7fH
Google AI Introduces DataGemma: A Set of Open Models that Utilize Data Commons through Retrieval Interleaved Generation (RIG) and Retrieval Augmented Generation (RAG) https://t.co/LVVBjeaRQo
