title: Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback
summary: Building effective dense retrieval systems remains difficult when relevance
  supervision is not available. Recent work has looked to overcome this challenge
  by using a Large Language Model (LLM) to generate hypothetical documents that
  can be used to find the closest real document. However, this approach relies
  solely on the LLM to have domain-specific knowledge relevant to the query,
  which may not be practical. Furthermore, generating hypothetical documents can
  be inefficient as it requires the LLM to generate a large number of tokens for
  each query. To address these challenges, we introduce Real Document Embeddings
  from Relevance Feedback (ReDE-RF). Inspired by relevance feedback, ReDE-RF
  proposes to re-frame hypothetical document generation as a relevance estimation
  task, using an LLM to select which documents should be used for nearest
  neighbor search. Through this re-framing, the LLM no longer needs
  domain-specific knowledge but only needs to judge what is relevant.
  Additionally, relevance estimation only requires the LLM to output a single
  token, thereby improving search latency. Our experiments show that ReDE-RF
  consistently surpasses state-of-the-art zero-shot dense retrieval methods
  across a wide range of low-resource retrieval datasets while also making
  significant improvements in latency per-query.
paper_authors._id: 67226cc975879ec4954d888b
paper_authors.name: Nour Jedidi
paper_authors._id: 67226cc975879ec4954d888c
paper_authors.name: Yung-Sung Chuang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-31T09:54:55.410Z
paper_authors._id: 5df84571da6d0311fd3d5407
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1650651305661-5df84571da6d0311fd3d5407.png
paper_authors.fullname: Yung-Sung Chuang
paper_authors.type: user
paper_authors.user: voidism
paper_authors._id: 67226cc975879ec4954d888d
paper_authors.name: Leslie Shing
paper_authors._id: 67226cc975879ec4954d888e
paper_authors.name: James Glass
paper_title: Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback
paper_summary: Building effective dense retrieval systems remains difficult when relevance
  supervision is not available. Recent work has looked to overcome this challenge
  by using a Large Language Model (LLM) to generate hypothetical documents that
  can be used to find the closest real document. However, this approach relies
  solely on the LLM to have domain-specific knowledge relevant to the query,
  which may not be practical. Furthermore, generating hypothetical documents can
  be inefficient as it requires the LLM to generate a large number of tokens for
  each query. To address these challenges, we introduce Real Document Embeddings
  from Relevance Feedback (ReDE-RF). Inspired by relevance feedback, ReDE-RF
  proposes to re-frame hypothetical document generation as a relevance estimation
  task, using an LLM to select which documents should be used for nearest
  neighbor search. Through this re-framing, the LLM no longer needs
  domain-specific knowledge but only needs to judge what is relevant.
  Additionally, relevance estimation only requires the LLM to output a single
  token, thereby improving search latency. Our experiments show that ReDE-RF
  consistently surpasses state-of-the-art zero-shot dense retrieval methods
  across a wide range of low-resource retrieval datasets while also making
  significant improvements in latency per-query.
paper_ai_keywords: Large Language Model (LLM) dense retrieval systems hypothetical document generation relevance feedback relevance estimation task nearest neighbor search