Filtered to scalability · clear filter
This paper introduces Kimi K3, a large-scale, open-source AI model that achieves state-of-the-art performance on a range of tasks, including vision and coding, and is designed to be more efficient and scalable than previous models, making it a promising candidate for real-world applications.
This paper compares the performance of different retrieval-augmented generation (RAG) paradigms at varying corpus sizes, finding that BM25 outperforms others at larger scales, but not at smaller ones, and that lexical retrieval is the strongest scalable default.
This paper investigates how to scale large language models to also understand and interact with the physical world by training them on multiple types of data from scratch, allowing them to reason about both text and images. Practitioners might care because this could lead to more robust and versatile AI systems that can handle a wider range of tasks.
This paper investigates using hypernetworks for large-scale knowledge injection into language models, a technique that can improve their ability to answer factual questions. Practitioners may care because it could lead to more accurate and scalable language models for applications like customer service or question-answering systems.
This paper introduces Explorative Modeling, a new approach to training generative models that allows for end-to-end generation by exploring multiple candidate matches between model generations and data. This can lead to improved performance and efficiency in various applications.