Topic-DPR: Topic-based Prompts for Dense Passage Retrieval

• URL: http://arxiv.org/abs/2310.06626v1
• Date: Tue, 10 Oct 2023 13:45:24 GMT
• Title: Topic-DPR: Topic-based Prompts for Dense Passage Retrieval
• Authors: Qingfa Xiao, Shuangyin Li, Lei Chen
• Abstract summary: We present Topic-DPR, a dense passage retrieval model that uses topic-based prompts. We introduce a novel positive and negative sampling strategy, leveraging semi-structured data to boost dense retrieval efficiency.
• Score: 6.265789210037749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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