996 citations · 1.6k across the 32 of their papers we have counts for
6 papers · 1 filter
HelpSteer2-Preference: Complementing Ratings with Preferences
Zhilin Wang, Alexander Bukharin, Olivier Delalleau +5
Reward models are critical for aligning models to follow instructions, and are typically trained following one of two popular paradigms: Bradley-Terry style or Regression style. Ho…
GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning
Aleksander Ficek, Jiaqi Zeng, Oleksii Kuchaiev
Parameter-Efficient Fine-Tuning (PEFT) and Retrieval-Augmented Generation (RAG) have become popular methods for adapting large language models while minimizing compute requirements…
HelpSteer2: Open-source dataset for training top-performing reward models
Zhilin Wang, Yi Dong, Olivier Delalleau +6
High-quality preference datasets are essential for training reward models that can effectively guide large language models (LLMs) in generating high-quality responses aligned with…
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…
NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment
Gerald Shen, Zhilin Wang, Olivier Delalleau +10
Aligning Large Language Models (LLMs) with human values and preferences is essential for making them helpful and safe. However, building efficient tools to perform alignment can be…
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…