DynRank: Improve Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification
Abdelrahman Abdallah, Jamshid Mozafari, Bhawna Piryani, Mohammed M. Abdelgwad, Adam Jatowt
Abstract
This paper presents DynRank, a novel framework for enhancing passage retrieval in open-domain question-answering systems through dynamic zero-shot question classification. Traditional approaches rely on static prompts and pre-defined templates, which may limit model adaptability across different questions and contexts. In contrast, DynRank introduces a dynamic prompting mechanism, leveraging a pre-trained question classification model that categorizes questions into fine-grained types. Based on these classifications, contextually relevant prompts are generated, enabling more effective passage retrieval. We integrate DynRank into existing retrieval frameworks and conduct extensive experiments on multiple QA benchmark datasets.
BibTeX
@inproceedings{abdallah-etal-2025-dynrank,
title = "{D}yn{R}ank: Improve Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification",
author = "Abdallah, Abdelrahman and
Mozafari, Jamshid and
Piryani, Bhawna and
Abdelgwad, Mohammed M. and
Jatowt, Adam",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.319/",
pages = "4768--4778"
}