title: GeAR: Generation Augmented Retrieval
summary: Document retrieval techniques form the foundation for the development of
  large-scale information systems. The prevailing methodology is to construct a
  bi-encoder and compute the semantic similarity. However, such scalar similarity
  is difficult to reflect enough information and impedes our comprehension of the
  retrieval results. In addition, this computational process mainly emphasizes
  the global semantics and ignores the fine-grained semantic relationship between
  the query and the complex text in the document. In this paper, we propose a new
  method called Generation Augmented Retrieval
  (GeAR) that incorporates well-designed fusion and decoding modules.
  This enables GeAR to generate the relevant text from documents based on the
  fused representation of the query and the document, thus learning to "focus on"
  the fine-grained information. Also when used as a retriever, GeAR does not add
  any computational burden over bi-encoders. To support the training of the new
  framework, we have introduced a pipeline to efficiently synthesize high-quality
  data by utilizing large language models. GeAR exhibits competitive retrieval
  and localization performance across diverse scenarios and datasets. Moreover,
  the qualitative analysis and the results generated by GeAR provide novel
  insights into the interpretation of retrieval results. The code, data, and
  models will be released after completing technical review to facilitate future
  research.
paper_authors._id: 677ccfefb35098e1340b036f
paper_authors.name: Haoyu Liu
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paper_authors.fullname: Haoyu Liu
paper_authors.type: user
paper_authors.user: noobimp
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paper_authors.name: Shaohan Huang
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paper_authors.fullname: HUANG SHAOHAN
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paper_authors.user: buaahsh
paper_authors._id: 677ccfefb35098e1340b0371
paper_authors.name: Jianfeng Liu
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paper_authors.fullname: Jianfeng Liu
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paper_authors.user: docacola
paper_authors._id: 677ccfefb35098e1340b0372
paper_authors.name: Yuefeng Zhan
paper_authors._id: 677ccfefb35098e1340b0373
paper_authors.name: Hao Sun
paper_authors._id: 677ccfefb35098e1340b0374
paper_authors.name: Weiwei Deng
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paper_authors.fullname: deng
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paper_authors.user: weiweideng
paper_authors._id: 677ccfefb35098e1340b0375
paper_authors.name: Feng Sun
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paper_authors.fullname: feng sun
paper_authors.type: user
paper_authors.user: forserty
paper_authors._id: 677ccfefb35098e1340b0376
paper_authors.name: Furu Wei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:28:01.282Z
paper_authors._id: 6368c512fbfe97c16a40baba
paper_authors.avatarUrl: /avatars/1c23bc7c0b6d9225699ce27647623d7a.svg
paper_authors.fullname: Furu Wei
paper_authors.type: user
paper_authors.user: thegenerality
paper_authors._id: 677ccfefb35098e1340b0377
paper_authors.name: Qi Zhang
paper_title: GeAR: Generation Augmented Retrieval
paper_summary: Document retrieval techniques form the foundation for the development of
  large-scale information systems. The prevailing methodology is to construct a
  bi-encoder and compute the semantic similarity. However, such scalar similarity
  is difficult to reflect enough information and impedes our comprehension of the
  retrieval results. In addition, this computational process mainly emphasizes
  the global semantics and ignores the fine-grained semantic relationship between
  the query and the complex text in the document. In this paper, we propose a new
  method called Generation Augmented Retrieval
  (GeAR) that incorporates well-designed fusion and decoding modules.
  This enables GeAR to generate the relevant text from documents based on the
  fused representation of the query and the document, thus learning to "focus on"
  the fine-grained information. Also when used as a retriever, GeAR does not add
  any computational burden over bi-encoders. To support the training of the new
  framework, we have introduced a pipeline to efficiently synthesize high-quality
  data by utilizing large language models. GeAR exhibits competitive retrieval
  and localization performance across diverse scenarios and datasets. Moreover,
  the qualitative analysis and the results generated by GeAR provide novel
  insights into the interpretation of retrieval results. The code, data, and
  models will be released after completing technical review to facilitate future
  research.
paper_ai_keywords: bi-encoder semantic similarity fine-grained semantic relationship Generation Augmented Retrieval (GeAR) fusion modules decoding modules relevant text generation large language models retrieval performance localization performance