Prompt-based learning's efficacy across numerous natural language processing tasks has led to its integration into dense passage retrieval. Prior research has mainly focused on enhancing the semantic understanding of pre-trained language models by optimizing a single vector as a continuous prompt. This approach, however, leads to a semantic space collapse; identical semantic information seeps into all representations, causing their distributions to converge in a restricted region. This hinders differentiation between relevant and irrelevant passages during dense retrieval. To tackle this issue, we present Topic-DPR, a dense passage retrieval model that uses topic-based prompts. Unlike the single prompt method, multiple topic-based prompts are established over a probabilistic simplex and optimized simultaneously through contrastive learning. This encourages representations to align with their topic distributions, improving space uniformity. Furthermore, we introduce a novel positive and negative sampling strategy, leveraging semi-structured data to boost dense retrieval efficiency. Experimental results from two datasets affirm that our method surpasses previous state-of-the-art retrieval techniques.

翻译：提示学习在众多自然语言处理任务中的有效性促使其被引入密集段落检索。先前研究主要通过优化单一向量作为连续提示来增强预训练语言模型的语义理解能力。然而，这种方法会导致语义空间坍缩：相同的语义信息渗透到所有表征中，使其分布收敛至受限区域，从而阻碍密集检索过程中相关段落与无关段落的区分。为解决该问题，我们提出Topic-DPR——一种基于主题提示的密集段落检索模型。与单一提示方法不同，该模型在概率单纯形上建立多个主题提示，并通过对比学习同时优化。此举促使表征与其主题分布对齐，提升空间均匀性。此外，我们引入一种新的正负采样策略，利用半结构化数据提升密集检索效率。两个数据集上的实验结果表明，我们的方法超越了以往最先进的检索技术。