Transformer language models have received widespread public attention, yet their
generated text is often surprising even to NLP researchers. In this survey, we discuss over …

We introduce LightGlue, a deep neural network that learns to match local features across
images. We revisit multiple design decisions of SuperGlue, the state of the art in sparse …

We introduce AudioLM, a framework for high-quality audio generation with long-term
consistency. AudioLM maps the input audio to a sequence of discrete tokens and casts …

We present a holistic approach to building a robust and useful natural language
classification system for real-world content moderation. The success of such a system relies …

Large language models (LLMs) represent a major advance in artificial intelligence and, in
particular, toward the goal of human-like artificial general intelligence. It is sometimes …

The rapid progress in conversational AI has given rise to advanced language models
capable of generating humanlike texts. Among these models, ChatGPT and Bard …

Neural Code Intelligence--leveraging deep learning to understand, generate, and optimize
code--holds immense potential for transformative impacts on the whole society. Bridging the …

In this work we present Omni-SMoLA a multimodal architecture that mixes many multi-modal
experts efficiently and achieves both high specialist and generalist performance. In contrast …

Recent advancements in large language models (LLMs) boasting billions of parameters
have generated a significant demand for efficient deployment in inference workloads. While …

The increasing size of large language models (LLMs) has introduced challenges in their
training and inference. Removing model components is perceived as a solution to tackle the …