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Pairrm

Developed by llm-blender
PairRM is an efficient pairwise reward model for comparing and ranking output candidates from large language models, supporting various applications such as RLHF and Best-N sampling.
Downloads 6,004
Release Time : 11/6/2023

Model Overview

PairRM takes an instruction and a pair of output candidates, scoring each to measure relative quality. It can be used to rank candidate outputs, enhance decoding, and align instruction-tuned LLMs through RLHF methods.

Model Features

Pairwise Comparison
Compares a pair of candidates side-by-side to identify subtle differences and improve evaluation accuracy.
Efficient Model
Based on the 0.4B-parameter deberta-v3-large, it offers fast inference with low resource consumption.
Multi-Dataset Training
Trained on six human preference datasets, covering diverse scenarios.
Versatile Applications
Supports ranking, Best-N sampling, RLHF, and other application scenarios.

Model Capabilities

Text Generation Evaluation
Output Candidate Ranking
RLHF Support
Decoding Enhancement

Use Cases

LLM Evaluation
Candidate Output Ranking
Ranks multiple LLM-generated candidate outputs to select the optimal result.
Improves output quality, aligning closely with human preferences.
LLM Training
RLHF Alignment
Guides LLM reinforcement learning through PairRM's scoring.
Enhances alignment between LLMs and human preferences.
Best-N Sampling
Generates multiple candidates and uses PairRM to select the best output.
Consistently improves generation quality and avoids low-quality outputs.
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