动态分类生成提示，提升问答系统文档检索效果
DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification
- 根据问题类型动态生成提示，替代固定模板
- 在多个基准数据集上显著提升检索准确率
- 适合需要灵活适配多类问题的开放域问答系统
本文提出DynRank，一种通过基于问题分类的动态零样本提示来提升开放域问答系统中段落检索性能的新框架。传统方法依赖静态提示和预定义模板，可能限制模型在不同问题和上下文中的适应性。相比之下，DynRank引入动态提示机制，利用预训练的问题分类模型将问题细分为多种类型，并据此生成语境相关的提示，从而实现更有效的段落检索。我们将DynRank集成到现有检索框架中，并在多个QA基准数据集上进行了大量实验，验证了其有效性。
原文摘要 · Abstract (English)
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.
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