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

Kaustubh D. Dhole

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2412.05339 v1 pith:R7Q7LQ7D submitted 2024-12-06 cs.IR cs.AIcs.CL

Kaustubh D. Dhole This is my paper

classification cs.IRcs.AIcs.CL

keywords pluginllmspyterrierpyterrier-genrankrerankingstrategieschoiceendpoints

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original abstract

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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Constructing and Evaluating Declarative RAG Pipelines in PyTerrier
cs.IR 2025-06 conditional novelty 6.0 of 10

PyTerrier-RAG extends PyTerrier with datatypes, readers, datasets, and metrics for building and evaluating declarative retrieval-augmented generation pipelines.

Pith tools