96 citations · 103 across the 7 of their papers we have counts for
7 papers
APEX-Agents
Bertie Vidgen, Austin Mann, Abby Fennelly +21
We introduce the AI Productivity Index for Agents (APEX-Agents), a benchmark for assessing whether AI agents can execute long-horizon, cross-application tasks created by investment…
The AI Consumer Index (ACE)
Julien Benchek, Rohit Shetty, Benjamin Hunsberger +5
We introduce the first version of the AI Consumer Index (ACE), a benchmark for assessing whether frontier AI models can perform everyday consumer tasks. ACE contains a hidden heldo…
The AI Productivity Index (APEX)
Bertie Vidgen, Abby Fennelly, Evan Pinnix +17
We present an extended version of the AI Productivity Index (APEX-v1-extended), a benchmark for assessing whether frontier models are capable of performing economically valuable ta…
Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models
NVIDIA, :, Aaron Blakeman +198
As inference-time scaling becomes critical for enhanced reasoning capabilities, it is increasingly becoming important to build models that are efficient to infer. We introduce Nemo…
Nemotron-4 340B Technical Report
Nvidia, :, Bo Adler +80
We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward. Our models are open access under the NVIDIA Open…
Nemotron-4 15B Technical Report
Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings +24
We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens. Nemotron-4 15B demonstrates strong performance when assesse…
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations
John Giorgi, Osvald Nitski, Bo Wang +1
Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corp…