Researchers have introduced ShiftAddLLM, a method to accelerate pretrained large language models (LLMs) by reducing memory and energy usage by 80%. Another study focuses on enhancing LLMs' ability to acquire new knowledge and effectively utilize information from the middle part of the context.
Accelerating Pretrained LLMs through Post-Training Shift-and-Add Reparameterization: Enhancing Efficiency in AI Models #AI #AItechnology #artificialintelligence #llm #machinelearning #ShiftAddLLM https://t.co/8DcQIqydSu https://t.co/6BvaQ8mdbd
This. Generative LLMs for their own sake are an entertaining parlour trick. https://t.co/V3cvaOirWF
ShiftAddLLM: Accelerating Pretrained LLMs through Post-Training Shift-and-Add Reparameterization: Creating Efficient Multiplication-Free Models https://t.co/qSgsu6j2xl #ShiftAddLLM #LanguageModels #AIefficiency #AutomationOpportunities #PracticalAI #ai #news #llm #ml #researc… https://t.co/UGEoOuVPwH
Consistent "Middle" Enhancement in LLMs Proposes an approach to tune an LLM to effectively utilize information from the middle part of the context. It first proposes a training-efficient method to extend LLMs to longer context lengths (e.g., 4K -> 256K). It uses a truncated… https://t.co/4c8BTVPgUy
Self-Tuning with LLMs Claims to improve an LLM’s ability to effectively acquire new knowledge from raw documents through self-teaching. Three steps involved: Step 1: Self-teaching augments documents with a set of knowledge-intensive tasks focusing on memorization,… https://t.co/kSa3SlocYM
[LG] ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization H You, Y Guo, Y Fu, W Zhou… [Georgia Institute of Technology] (2024) https://t.co/jaKnom7ilb - The paper proposes ShiftAddLLM, a method to accelerate pretrained large… https://t.co/NArCYHd8ep
ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization 80% memory and energy reductions over the original LLMs https://t.co/ktCdUyDnkd https://t.co/4u9iXnaSiP