# PyTerrier-GenRank: The PyTerrier Plugin for Reranking with Large Language Models

arXiv: https://arxiv.org/abs/2412.05339 · HTML: https://mlsanity.com/paper/2412.05339/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 6 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [Overview of the TREC 2019 deep learning track](https://mlsanity.com/paper/2003.07820/) (2020)
- [Multi-Stage Document Ranking with BERT](https://mlsanity.com/paper/1910.14424/) (2019)
- [RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!](https://mlsanity.com/paper/2312.02724/) (2023)
- [RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models](https://mlsanity.com/paper/2309.15088/) (2023)
- [LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking](https://mlsanity.com/paper/2311.02089/) (2023)
- [Top-Down Partitioning for Efficient List-Wise Ranking](https://mlsanity.com/paper/2405.14589/) (2024)
- [Generative Query Reformulation Using Ensemble Prompting, Document Fusion, and Relevance Feedback](https://mlsanity.com/paper/2405.17658/) (2024)

## Built on by (most cited first, 1 of 1)

- [To Retrieve or Not to Retrieve? Uncertainty Detection for Dynamic Retrieval Augmented Generation](https://mlsanity.com/paper/2501.09292/) (2025)

## Abstract (arXiv)

Using LLMs as rerankers requires experimenting with various hyperparameters, such as prompt formats, model choice, and reformulation strategies. We introduce PyTerrier-GenRank, a PyTerrier plugin to facilitate seamless reranking experiments with LLMs, supporting popular ranking strategies like pointwise and listwise prompting. We validate our plugin through HuggingFace and OpenAI hosted endpoints.