Category: FAANG

Large language models (LLMs) could be valuable personal AI agents across various domains, provided they can precisely follow user instructions. However, recent studies have shown significant limitations in LLMs’ instruction-following capabilities, raising concerns about their reliability in high-stakes applications. Accurately estimating LLMs’ uncertainty in adhering to instructions is critical to mitigating deployment risks. We present, …

Read more “Do LLMs Estimate Uncertainty Well in Instruction-Following?”

By Cheng Xie, Bryan Shultz, and Christine Xu In a previous blog post, we described how Netflix uses eBPF to capture TCP flow logs at scale for enhanced network insights. In this post, we delve deeper into how Netflix solved a core problem: accurately attributing flow IP addresses to workload identities. A Brief Recap FlowExporter is …

Read more “How Netflix Accurately Attributes eBPF Flow Logs”

Progress in natural language processing enables more intuitive ways of interacting with technology. For example, many of Apple’s products and services, including Siri and search, use natural language understanding and generation to enable a fluent and seamless interface experience for users. Natural language is a rapidly moving area of machine learning research, and includes work …

Read more “Apple Workshop on Natural Language Understanding 2024”

Large Language Models (LLMs) have transformed natural language processing, but face significant challenges in widespread deployment due to their high runtime cost. In this paper, we introduce SeedLM, a novel post-training compression method that uses seeds of a pseudo-random generator to encode and compress model weights. Specifically, for each block of weights, we find a …

Read more “SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators”

We’re exploring the frontiers of AGI, prioritizing technical safety, proactive risk assessment, and collaboration with the AI community.

Many robotics tasks, such as path planning or trajectory optimization, are formulated as optimal control problems (OCPs). The key to obtaining high performance lies in the design of the OCP’s objective function. In practice, the objective function consists of a set of individual components that must be carefully modeled and traded off such that the …

Read more “Interpreting and Improving Optimal Control Problems With Directional Corrections”