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Found 5 papers, 3 papers with code
HelpSteer2-Preference: Complementing Ratings with Preferences
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.
Nemotron-4 340B Technical Report
We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward.
HelpSteer2: Open-source dataset for training top-performing reward models
Using a powerful internal base model trained on HelpSteer2, we are able to achieve the SOTA score (92. 0%) on Reward-Bench's primary dataset, outperforming currently listed open and proprietary models, as of June 12th, 2024.
NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment
Aligning Large Language Models (LLMs) with human values and preferences is essential for making them helpful and safe.
HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM
To alleviate this problem, we collect HelpSteer, a multi-attribute helpfulness dataset annotated for the various aspects that make responses helpful.
