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Reward Model Deberta V3 Large

Developed by OpenAssistant
This reward model is trained to predict which generated answer human evaluators would prefer for a given question.
Downloads 796
Release Time : 1/15/2023

Model Overview

A human feedback-trained reward model for evaluating QA model quality or serving as a reward score in RLHF. Supports predicting human-preferred answer ranking.

Model Features

Multi-dataset training
Jointly trained on three datasets: WebGPT, summary feedback, and synthetic instructions
High-performance architecture
Utilizes DeBERTa-v3-large architecture with excellent benchmark performance
RLHF compatible
Can directly serve as the reward function in reinforcement learning human feedback processes

Model Capabilities

Answer quality evaluation
Answer pair ranking
Human preference prediction

Use Cases

QA systems
Answer quality scoring
Quality scoring for multiple AI-generated answers
Accurately predicts human evaluators' preferences
Reinforcement learning
RLHF reward signal
Provides alternative reward signals for reinforcement learning with human feedback
Accelerates model alignment process
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