Text2SQL is Not Enough Unifying AI and Databases with TAG discuss: huggingface.co/papers/2408.14… AI systems that serve natural language questions over databases promise to unlock tremendous value. Such systems would allow users to leverage the powerful reasoning and knowledge

Want to answer NL questions over your data? Introducing Table Augmented Generation (TAG)! Joint work w/ the amazing Matei Zaharia Carlos Guestrin Joey Gonzalez Asim Biswal Sid Jha Amog Kamsetty Shu Lynn Liu 📚 Paper: arxiv.org/abs/2408.14717 🛠️ Code: github.com/tag-research/t… 🧵

Text2SQL is not enough! Introducing Table Augmented Generation (TAG), a general paradigm on how LLMs interact with databases. Unlike Text2SQL or RAG, TAG fully leverages the capabilities of both database systems and LLMs. We also introduce TAG-Bench-v1, a benchmark consisting

🚀 Just Launched: VideoArena!🎥 Discover head-to-head comparisons of video clips generated from the same prompts across top text-to-video models. Compare outputs from 7 leading models and we're adding more soon! 🔗 Check out the leaderboard: videoarena.tv #Text2Video

This is so cool! Check out this demo from Asim Biswal for asking questions over large tables, like election contributions🗳️I have so many questions to ask👀 ✨And it's powered by Text2LOTUS! NL questions are converted to LOTUS programs for optimized LLM-powered data processing!

We now have a super cool demo of Table Augmented Generation (TAG) that lets you explore important datasets (e.g., US Election Donations). Try it now! (while we still have LLM inference credits) The TAG Project combines the language reasoning and world knowledge of LLMs with

Instruct-tuned models are getting better at following instructions and ‘reasoning’ every day, but they’re shockingly poor at generating diverse responses. Diversity is crucial to many tasks like synthetic data generation. We tackle this with a new approach, BARE 🐻! (1/n)

Some of the most exciting AI apps require LLM reasoning over large datasets at test time. For these types of NL questions, RAG or Text2SQL + your favorite LLM are simply not enough. Excited to announce our new leaderboard, from the TAG team at Stanford and Berkeley, to