license: apache-2.0
pipeline_tag: text-ranking
library_name: transformers
base_model: google/t5-3b
MVP: Multi-view-guided Passage Reranking with Large Language Models
This repository contains the official implementation of the paper: Multi-view-guided Passage Reranking with Large Language Models.
Overview
Recent advances in large language models (LLMs) have shown impressive performance in passage reranking tasks. Despite their success, LLM-based methods still face challenges in efficiency and sensitivity to external biases.
- (i) Existing models rely mostly on autoregressive generation and sliding window strategies to rank passages, which incurs heavy computational overhead as the number of passages increases.
- (ii) External biases, such as positional or semantic bias, hinder the model’s ability to accurately represent passages and the input-order sensitivity.
To address these limitations, we propose Multi-View-guided Passage Reranking (MVP), a non-generative LLM-based reranker that encodes query–passage information into multiple views and computes relevance scores via anchor vectors in a single decoding step. An orthogonal loss encourages diversity across views. With only 220M parameters, MVP matches 7B-scale fine-tuned models while reducing inference latency by 100×, and the 3B variant achieves state-of-the-art results on both in-domain and out-of-domain benchmarks.
Setup Environment
conda env create -f mvp.yaml
conda activate mvp
How to Use
Run MVP
cd inference
bash run_evaluation.sh
Train MVP
cd train
bash train.sh
Model Checkpoints
Dataset
Evaluation Datasets
Training Datasets
- Train/Valid This dataset is derived from BEIR/MSMARCO license, and its usage is restricted to academic purposes only.
Acknowledgments
We implemented our model based on the following repository: ListT5
Citation
If you find our work useful or helpful for your research, please consider citing our paper:
@inproceedings{na2025multiviewguided,
title={Multi-view-guided Passage Reranking with Large Language Models},
author={Na, Jeongwoo and Kwon, Jun and Choi, Eunseong and Lee, Jongwuk},
booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
year={2025}
}