🤩 It's happening today!
Join us at the 2nd Workshop on Foundation Models in the Wild — Hall 4, #6, Singapore EXPO!
🔥 10 amazing invited talks
🔥 12 exciting oral presentations
🔥 Cutting-edge ideas and lively discussions
🚀 Don't miss it — come say hi and explore the future of in-the-wild foundation models with us!
Say hello to Multiverse — the Everything Everywhere All At Once of generative modeling.
💥 Lossless, adaptive, and gloriously parallel
🌀 Now open-sourced: https://t.co/nygi9LSZLt
I was amazed how easily we could extract the intrinsic parallelism of even SOTA autoregressive LLMs—just by restructuring a few examples from their reasoning traces and training with Multiverse modeling. 🤯
#AI #Multiverse #LLM #Parallelism
🤩 It's happening today!
Join us at the 2nd Workshop on Foundation Models in the Wild — Hall 4, #6, Singapore EXPO!
🔥 10 amazing invited talks
🔥 12 exciting oral presentations
🔥 Cutting-edge ideas and lively discussions
🚀 Don't miss it — come say hi and explore the future of in-the-wild foundation models with us!
😀We're delighted to announce that the review stage of our 2nd FM-Wild Workshop at ICLR has successfully concluded. We extend our sincere gratitude to all authors and reviewers for their valuable contributions.
👉The accepted papers are now available at: https://t.co/eaIKWgYAEl
🚀This year, we received an impressive 140 submissions and are proud to have accepted 100 high-quality papers. We're currently scratching our heads trying to pick just 10 for oral presentations—talk about a tough call! We'll be making these difficult decisions in the coming days, so stay tuned.
👐We continue to welcome sponsorships in any form to help make this workshop even more impactful. Your support would be greatly appreciated!
We look forward to an exciting workshop and to seeing you at ICLR 2025!
📢 Announcing our new work "APE: Faster and Longer Context-Augmented Generation via Adaptive Parallel Encoding" @iclr_conf
🚀 Enabling the efficient combination of multiple contexts with negligible prefilling cost
💅 Re-using the context window of LLMs to accommodate more and longer retrieved texts
🔘 APE is targeted at scenarios where the generation is augmented with external texts for better performance.
🔘 It avoids the efficiency and performance bottleneck in sequence encoding by eliminating the on-the-fly prefilling of long sequences including multiple texts.
🔘 It pre-computes and caches the KV states of each text separately for direct loading during inference. Our training-free alignments ensure performance recovery.
Curious how? Check it out 👇
🔗 Website: https://t.co/KKxHXK2QNp
📜 Paper: https://t.co/u6eNNvPUEd
💻 Code: APE is targeted on scenarios in which the generation is augmented with external contexts for better performance.