✨ Introducing DataGemma, open models that enhance LLM factuality by grounding them with real-world data from Google’s Data Commons. → https://goo.gle/4dWFyKn Learn how DataGemma uses two distinct RIG and RAG approaches to: 📖 Harness the knowledge of Data Commons 🧠 Fact check numeric data against trusted sources 🛠️ Lead the way in developing responsible AI And more for the #Gemmaverse!
Grounding AI models with real-world data, as DataGemma is doing, is a brilliant step forward. In sectors where accuracy and real-time data are essential, this kind of innovation makes a significant impact. The integration of RIG and RAG approaches to fact-check and enhance AI's factuality is key to building more responsible and trustworthy AI systems. Looking forward to seeing how this technology continues to evolve and shape the future of AI!
good
DataGemma looks like a groundbreaking initiative! Grounding LLMs with real-world data through RIG and RAG approaches can massively improve factual accuracy and reliability. Integrating Google's Data Commons is a smart move, especially for fact-checking numeric data against trusted sources. Excited to see how this will advance responsible AI development. Looking forward to exploring the #Gemmaverse
DataGemma sounds like a powerful leap forward in enhancing AI reliability and factual accuracy! Grounding models with real-world data from trusted sources like Google’s Data Commons is a step in the right direction for responsible AI development. Let's see how RIG and RAG approaches will shape the future of AI-powered insights.
Interesting
Terrific stuff. Great to see Google creating ways for developers to better ground their models and reduce hallucinations. Well done!
It will definitely help developers in improving accuracy of models.
Insightful
So exciting to see this launch