DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification

TL;DR

DynRank is a framework that improves passage retrieval in open-domain QA by dynamically generating prompts based on question classification, leading to better retrieval performance across various datasets.

Contribution

It introduces a dynamic prompting mechanism using question classification to enhance passage retrieval, moving beyond static prompts in open-domain QA systems.

Findings

Improved retrieval accuracy on multiple QA benchmarks

Effective question classification enhances prompt relevance

Dynamic prompts outperform static templates in retrieval tasks

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.