📢 New paper alert! Introducing STIC (Self-Training on Image Comprehension) that enhances the understanding and reasoning capabilities of LVLMs through self-generated data 🌟 📄 Read the paper: arxiv.org/abs/2405.19716 🔗 Project page: stic-lvlm.github.io 💻 GitHub Repo:

Starting my internship with Microsoft Research AI Frontiers Team in Redmond today! Excited for the next 3 months here. Hmu if you're around in the greater Seattle area, would love to chat about data-efficient and robust learning!

🚀 Exciting News! 🚀 Join Baharan Mirzasoleiman and me for a 2-hour tutorial on Data-Efficient Learning! Learn the principles behind data curation: the secret sauce powering today’s AI revolution! ⚡️ See you at 1pm on Monday CEST in Hall A8! 🙌 🔗 More details: sjoshi804.github.io/data-efficient…

🚀 Exciting news! ConTextual (con-textual.github.io) is headed to Vienna for #ICML2024! 🎉 📊 Leaderboard updates: GPT4o-mini is now 2nd, just 1% behind GPT4o🥳 Claude-3.5-Sonnet takes 3rd, outperforming Claude-3-Opus by 19% 😲

📢 Happening in 15 minutes (1pm) in Hall A8! Hurry, before all the good seats are taken 😂

Come see our poster #715 on CodeIt today at #ICML2024 13.30-15.00 Halle C. We approach ARC by self-improving LLMs with prioritized hindsight replay. Blaze(j) Manczak 🇵🇱🇱🇺🇪🇺 Auke Wiggers Corrado Rainone David Zhang Michaël Defferrard Taco Cohen

Excited to announce the release of Eureka, an open-source framework for evaluating and understanding large foundation models! 🌟 Eureka offers: 🔍In-depth analysis of 12 cutting-edge models 🧠 Multimodal & language capability testing beyond single-score reporting and rankings 📈

Introducing BenchAgents: a framework for automated benchmark creation, using multiple LLM agents that interact with each other and with developers to generate diverse, high-quality, and challenging benchmarks w/ Varun Chandrasekaran Neel Joshi Besmira Nushi 💙💛 Vidhisha Balachandran Microsoft Research 🧵1/8

1/ I'll be at #NeurIPS2024 presenting our work SmallToLarge (S2L): Data-efficient Fine-tuning of LLMs! 🚀 What’s S2L? It’s a scalable data selection method that trains a small proxy model to guide fine-tuning for larger models, reducing costs while preserving performance. 👇