  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.
