🎉 Excited to share: "𝐌𝐢𝐱𝐭𝐮𝐫𝐞-𝐨𝐟-𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 (𝐌𝐨𝐓)" has been officially accepted to TMLR (March 2025) and the code is now open-sourced!. 📌 GitHub repo: 📄 Paper: How can we reduce pretraining costs for

RT @ShirleyYXWu: Even the smartest LLMs can fail at basic multiturn communication. Ask for grocery help → without asking where you live….

Thank you, @VictoriaLinML , for the write-up.

RT @xuandongzhao: 🚀 Excited to share the most inspiring work I’ve been part of this year:. "Learning to Reason without External Rewards"….

🌐 On United Nations (UN) adoption: Even the world's most prominent international bodies are embracing LLMs! . UN press releases showed a rapid initial surge (3.1% to 10.1%) in early 2023, then steadily climbing to 13.7% by Q3 2024.

Work done in collaboration w/@yaohuiz3, @m_codreanu, Jiayu Wang, @CaoHancheng, @james_y_zou.

🔍 Key findings:. - Lower education areas showed higher LLM adoption in consumer complaints.- Urban areas have higher LLM usage (18.2% vs 10.9%).- Science & tech companies lead in corporate adoption.- Younger firms (post-2015) use LLMs 3x more than older ones (pre-1980)

🚨 New research: We analyzed 1.5M+ documents to track LLM-assisted writing adoption across society from 2022-2024. The results? . 📊By late 2024, LLMs assist in writing:.- 18% of financial consumer complaints.- 24% of corporate press releases.- Up to 15% of job postings (esp. in

RT @VoyageAI: Thanks @liang_weixin We all enjoyed reading the paper! And we appreciate your paper for helping the community gain a deeper….

RT @VoyageAI: We are excited to announce that Voyage AI is officially joining @MongoDB !. Joining @MongoDB enables us to bring our cutting-….

RT @kefandong: Update: check out for our code, data, and model!.

RT @JunhongShen1: We introduce Mixture-of-Mamba, a multi-modal SSM that leverages modality-aware sparsity for efficient multi-modal pretrai….

🚀 Want 2x faster pretraining for your multi-modal LLM?. 🧵 Following up on Mixture-of-Transformers (MoT), we're excited to share Mixture-of-Mamba (MoM)!. 🔥 Why it matters: MoM applies modality-aware sparsity across image, text, and speech—making

📢 Can LLMs program themselves to run faster? 🏃⏱️ . LLM self-taught to code for next-gen AI hardware!. 1/ Programming AI accelerators is a major bottleneck in ML. Our self-improving LLM agent learns to write optimized code for new hardware, achieving 3.9x

RT @zhang677: 🔍 ML library development is crucial but requires expertise in ML algorithms & architecture-specific programming languages (AS….

RT @Zhang_Yu_hui: 🔍 Vision language models are getting better - but how do we evaluate them reliably? Introducing AutoConverter: transformi….

RT @WeijiaShi2: Introducing 𝐋𝐥𝐚𝐦𝐚𝐅𝐮𝐬𝐢𝐨𝐧: empowering Llama 🦙 with diffusion 🎨 to understand and generate text and images in arbitrary sequen….

RT @Zhang_Yu_hui: 🤔 Why are VLMs (even GPT-4V) worse at image classification than CLIP, despite using CLIP as their vision encoder?. Presen….

RT @SiyouPei: I’m open to academia & industry in 2025. My work in #XR 🥽 + #HCI 👩‍💻 enables low-friction XR experience thru #EmbodiedInterac….

Honored that @Nature has highlighted our work again in their latest piece examining #ChatGPT's transformative impact on scientific research and academia over the past two years. h/t @Nature.