A simple but effective approach to improve long-context understanding Previous studies have mainly explored two major directions: input reduction and window extension. Input reduction reduces the length of the input context — for example, by directly truncating the input — before feeding to downstream LLMs. RAG extends this direction by breaking the input into chunks […]
Acknowledgements Special thanks to Michael Reneer for his critical contributions in setting up Parfait. Direct contributors to work on Parfait repositories include Galen Andrew, Isha Arkatkar, Sean Augenstein, Kallista Bonawitz, Amlan Chakraborty, Zachary Charles, Stanislav Chiknavaryan, DeWitt Clinton, Taylor Cramer, Katharine Daly, Stefan Dierauf, Randy Dodgen, Hubert Eichner, Nova Fallen, Ken Franko, Zachary Garrett, Emily
Humans possess a remarkable ability to localize sound sources and perceive the surrounding environment through auditory cues alone. This sensory ability, known as spatial hearing, plays a critical role in numerous everyday tasks, including identifying speakers in crowded conversations and navigating complex environments. Hence, emulating a coherent sense of space via listening devices like headphones
Introduction to intellectual property Intellectual property is our ownership of ideas and things we have created such as art, music or even films, but most often they are things we have trademarked from business that we previously or currently own. These are all legally binding and protect you if someone tried to steal these ideas
As artificial intelligence (AI) continues to advance, the landscape is becoming increasingly competitive and ethically fraught. Companies like Anthropic, which have missions centered on developing “safe AI,” face unique challenges in an ecosystem where speed, innovation, and unconstrained power are often prioritized over safety and ethical considerations. In this post, we explore whether such companies
Seeing as transformers and MPNNs are not the only ML approaches for the structural analysis of graphs, we also compared the analytical capabilities of a wide variety of other GNN- and transformer-based architectures. For GNNs, we compared both transformers and MPNNs to models like graph convolutional networks (GCNs) and graph isomorphism networks (GINs). Additionally, we
We made strong headway in ML foundations, with extensive work on algorithms, efficiency, data and privacy. We improved ML efficiency through pioneering techniques that reduce the inference times of LLMs, which were implemented across Google products and adopted throughout the industry. Our research on cascades presents a method for leveraging smaller models for “easy” outputs
What is your take on blog post “Why AI Needs Large Numerical Models (LNMs) for Mathematical Mastery“? Thought about large numerical and mathematics models for a few seconds.Confirming Additional BreakthroughsOK, I’m confirming if LNMs/LMMs need more than Transformer models to match LLM performance, and noting the user’s comprehensive response. Yes. While the Transformer architecture provided
The availability and structure of mathematical training data, combined with the unique characteristics of mathematics itself, suggest that training a Large Numerical Model (LNM) is feasible and may require less data than training a general-purpose LLM. Here’s a detailed look: Availability of Mathematical Training Data Structure of Mathematics and Data Efficiency Mathematics’ highly structured nature
