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🎉 Excited to share that our paper "How Does Vision-Language Adaptation Impact the Safety of Vision Language Models?" has been accepted to #ICLR2025! 🖼 Vision-Language Adaptation empowers LLMs to process visual information—but how does it impact their safety? 🛡 And what about

Glad to share that BiGGen Bench has been accepted at NAACL HLT 2025! See you at New Mexico🤗

📢 Interested in evaluating the quality of CoT reasoning steps, but don't know where to start? Here is a new survey for you! arxiv.org/abs/2502.12289 (1/3)

🏆Glad to share that our BiGGen Bench paper has received the best paper award at NAACL HLT 2025! x.com/naaclmeeting/s… 📅 Ballroom A, Session I: Thursday May 1st, 16:00-17:30 (MDT) 📅 Session M (Plenary Session): Friday May 2nd, 15:30-16:30 (MDT) 📅 Virtual Conference: Tuesday

🙁 LLMs are overconfident even when they are dead wrong. 🧐 What about reasoning models? Can they actually tell us “My answer is only 60% likely to be correct”? ❗Our paper suggests that they can! Through extensive analysis, we investigate what enables this emergent ability.

Within the RAG pipeline, the retriever often acts as the bottleneck! Instead of training a better embedding model, we explore using a reasoning model both as the retriever&generator. To do this, we add MCTS to the generative retrieval pipeline. Check out Chaeeun Kim's post!

A humanoid robot policy trained solely on synthetic data generated by a world model. Research Scientist Joel Jang presents NVIDIA's DreamGen pipeline: ⦿ Post-train the world model Cosmos-Predict2 with a small set of real teleoperation demos. ⦿ Prompt the world model to