| license: apache-2.0 | |
| library_name: transformers | |
| pipeline_tag: text-ranking | |
| # MVP: Multi-view-guided Passage Reranking with Large Language Models | |
| This repository contains the official implementation for the EMNLP 2025 paper: | |
| [**Multi-view-guided Passage Reranking with Large Language Models**](https://huggingface.co/papers/2509.07485) | |
| by Jeongwoo Na*, Jun Kwon*, Eunseong Choi, Jongwuk Lee (* : equal contribution) | |
| ## Overview | |
| MVP (Multi-View-guided Passage Reranking) is a non-generative LLM-based reranking method designed to overcome the efficiency and bias sensitivity challenges of existing LLM-based rerankers. It encodes query-passage information into diverse view embeddings, ensuring accurate representation without external biases. The model then combines query-aware passage embeddings to produce distinct anchor vectors, which are used to directly compute relevance scores in a single decoding step. An orthogonal loss encourages diversity across these views. | |
| With just 220M parameters, MVP matches the performance of much larger 7B-scale fine-tuned models while achieving a 100x reduction in inference latency. The 3B-parameter variant of MVP achieves state-of-the-art performance on both in-domain and out-of-domain benchmarks. | |
| ## How to Use | |
| ### Setup Environment | |
| ``` | |
| conda env create -f mvp.yaml | |
| conda activate mvp | |
| ``` | |
| ### Run MVP | |
| ``` | |
| cd inference | |
| bash run_evaluation.sh | |
| ``` | |
| ## Model Checkpoints | |
| - [`MVP-base`](https://huggingface.co/Jun421/MVP-base) | |
| - [`MVP-3b`](https://huggingface.co/Jun421/MVP-3b) | |
| ## Datasets | |
| ### Evaluation Datasets | |
| - [BM25-Top100](https://huggingface.co/datasets/Soyoung97/beir-eval-bm25-top100) (`Soyoung97/beir-eval-bm25-top100`) | |
| ### Training Datasets | |
| - [Train/Valid](https://huggingface.co/datasets/Jun421/MVP-train) (`Jun421/MVP-train`) | |
| This dataset is derived from the BEIR/MSMARCO license, and its usage is restricted to **academic purposes** only. | |
| ## Acknowledgments | |
| The implementation of this model is based on the [ListT5](https://github.com/soyoung97/ListT5) repository. |