Authors: Hongming Tan, Shaoxiong Zhan, Hai Lin, Hai-Tao Zheng, Wai Kin, Chan
Abstract: In dense retrieval, embedding long texts into dense vectors can result in
information loss, leading to inaccurate query-text matching. Additionally,
low-quality texts with excessive noise or sparse key information are unlikely
to align well with relevant queries. Recent studies mainly focus on improving
the sentence embedding model or retrieval process. In this work, we introduce a
novel text augmentation framework for dense retrieval. This framework
transforms raw documents into information-dense text formats, which supplement
the original texts to effectively address the aforementioned issues without
modifying embedding or retrieval methodologies. Two text representations are
generated via large language models (LLMs) zero-shot prompting: question-answer
pairs and element-driven events. We term this approach QAEA-DR: unifying
question-answer generation and event extraction in a text augmentation
framework for dense retrieval. To further enhance the quality of generated
texts, a scoring-based evaluation and regeneration mechanism is introduced in
LLM prompting. Our QAEA-DR model has a positive impact on dense retrieval,
supported by both theoretical analysis and empirical experiments.
Source: http://arxiv.org/abs/2407.20207v1
