PyTerrier-GenRank: The PyTerrier Plugin for Reranking with Large Language Models
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 29 references that are in the map. First posted on arXiv 6 Dec 2024.
Builds on
7 of 29 references.
References · 7
- 2020Overview of the TREC 2019 deep learning trackcited by 282
- 2019Multi-Stage Document Ranking with BERTcited by 200
- 2023RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!cited by 151
- 2023RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Modelscited by 69
- 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 52
- 2024Top-Down Partitioning for Efficient List-Wise Rankingcited by 13
- 2024Generative Query Reformulation Using Ensemble Prompting, Document Fusion, and Relevance Feedbackcited by 5
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