Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval

• URL: http://arxiv.org/abs/2308.08285v1
• Date: Wed, 16 Aug 2023 11:10:43 GMT
• Title: Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval
• Authors: Guangyuan Ma, Xing Wu, Peng Wang, Zijia Lin, Songlin Hu
• Abstract summary: We study the potential of pre-training with Large Language Model(LLM)-based document expansion for dense passage retrieval. Our work shows strong zero-shot and out-of-domain retrieval abilities, making it more widely applicable for retrieval when initializing with no human-labeled data.
• Score: 28.906829093158592
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we systematically study the potential of pre-training with Large Language Model(LLM)-based document expansion for dense passage retrieval. Concretely, we leverage the capabilities of LLMs for document expansion, i.e. query generation, and effectively transfer expanded knowledge to retrievers using pre-training strategies tailored for passage retrieval. These strategies include contrastive learning and bottlenecked query generation. Furthermore, we incorporate a curriculum learning strategy to reduce the reliance on LLM inferences. Experimental results demonstrate that pre-training with LLM-based document expansion significantly boosts the retrieval performance on large-scale web-search tasks. Our work shows strong zero-shot and out-of-domain retrieval abilities, making it more widely applicable for retrieval when initializing with no human-labeled data.

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