🚨 New LLM Reasoning Paper 🚨 Q. How can LLMs self-improve their reasoning ability? ⇒ Introducing Self-Explore⛰️🧭, a training method specifically designed to help LLMs avoid reasoning pits by learning from their own outputs! [1/N]

Announcing our latest addition to the OLMo family, OLMo 1.7!🎉Our team's efforts to improve data quality, training procedures and model architecture have led to a leap in performance. See how OLMo 1.7 stacks up against its peers and peek into the technical details on the blog:

🚀 We're excited to share the technical report of Pegasus-1, our 17B-parameter VLM, setting new benchmarks in video understanding. It surpasses larger models like Gemini Pro and Ultra in video conversation, QA, summarization, and temporal understanding. bit.ly/pegasus-1-tech…

#NLProc Introducing 🔥Prometheus 2, an open-source LM specialized on evaluating other language models. ✅Supports both direct assessment & pairwise ranking. ✅ Improved evaluation capabilities compared to its predecessor. ✅Can assess based on user-defined evaluation criteria.

🚨 New LLM personalization/alignment paper 🚨 🤔 How can we obtain personalizable LLMs without explicitly re-training reward models/LLMs for each user? ✔ We introduce a new zero-shot alignment method to control LLM responses via the system message 🚀

🤔How can we systematically assess an LM's proficiency in a specific capability without using summary measures like helpfulness or simple proxy tasks like multiple-choice QA? Introducing the ✨BiGGen Bench, a benchmark that directly evaluates nine core capabilities of LMs.

🚨 New paper 🚨 How Large Language Models Acquire Factual Knowledge During Pretraining? I’m thrilled to announce the release of my new paper! 🎉 This research explores how LLMs acquire and retain factual knowledge during pretraining. Here are some key insights:

🤔 Humans excel at generalizing planning into extrapolated data or rapidly adapting with limited train data. How is it possible for language models? Introducing 🧠Cognitive Map for Language Models, a framework achieving Optimal Planning via Verbally Representing the World Model🌍

📢 Excited to share our latest paper on the reasoning capabilities of LLMs! Our research dives into how these models recall and utilize factual knowledge during solving complex questions. [🧵1 / 10] arxiv.org/abs/2406.19502

We are hosting wonderful NLP colleagues at KAIST on their way to ACL Bangkok! 🤩 On-site registration is closed, but the talks will be broadcast on Zoom. Please join us! Date/Time: Aug 10, 2024, 10:05-12:30 KST (UCT+9) Parallel Session 1: Advanced Language Models and AI

Molmo, our first open multimodal language model, is here; we've equipped our OLMo with eyes! 👀 ✨ Molmo: Raising the bar and outperforming the latest Llama 3.2 models. 🚀 Molmo-72B: Competes head-to-head with leading proprietary models. 🔥 MolmoE-1B: Ultra-efficient,

❓Do LLMs maintain the capability of knowledge acquisition throughout pretraining? If not, what is driving force behind it? ❗Our findings reveal that decreasing knowledge entropy hinders knowledge acquisition and retention as pretraining progresses. 📄arxiv.org/abs/2410.01380

🚀 First step to unlocking Generalist Robots! Introducing 🤖LAPA🤖, a new SOTA open-sourced 7B VLA pretrained without using action labels. 💪SOTA VLA trained with Open X (outperforming OpenVLA on cross and multi embodiment) 😯LAPA enables learning from human videos, unlocking

Excited to introduce 𝐋𝐀𝐏𝐀: the first unsupervised pretraining method for Vision-Language-Action models. Outperforms SOTA models trained with ground-truth actions 30x more efficient than conventional VLA pretraining 📝: arxiv.org/abs/2410.11758 🧵 1/9

🚨 New paper alert! 🚨 Isn’t it wasteful to repeat lengthy & complex agent prompts every time? Introducing "Generative Context Distillation"—a new lightweight method to internalize prompt. 🦾 Powerful performance 💵 Efficient inference "without the need for a prompt📜" [1/7]

🎉Thanks for attending the #NeurIPS2024 Workshop on #AdaptiveFoundationModels! 🚀Speakers: Russ Salakhutdinov Sander Dieleman Kate Saenko Matthias Bethge/Vishaal Udandarao Minjoon Seo Bing Liu Tianqi Chen 🔥Organizers: Paul Vicol Mengye Ren Renjie Liao Naila Murray Wei-Chiu Ma Beidi Chen 🧵Recap!

Presenting ✨Knowledg Entropy✨ at #ICLR2025 today in Oral 5C(Garnet 216-218) at 10:30AM and in Poster 6(#251) from 3:00PM We investigated how changes in a model's tendency to integrate its parametric knowledge during pretraining affect knowledge acquisition and forgetting

🙁 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.

[1/6] Ever wondered why Direct Preference Optimization is so effective for aligning LLMs? 🤔 Our new paper dives deep into the theory behind DPO's success, through the lens of information gain. Paper: "Differential Information: An Information-Theoretic Perspective on Preference

🚨 Episode 3 of The AI Agent Takeover is happening June 5 at 1PM KST! We’re sitting down with our very own Sean Ren | Sahara AI 🔆 and Minjoon Seo, CEO & Co-Founder of Config Intelligence to dive into the next wave of physical AI and how robots can learn from human demonstrations.