title: RE-AdaptIR: Improving Information Retrieval through Reverse Engineered
    Adaptation
summary: Large language models (LLMs) fine-tuned for text-retrieval have demonstrated
  state-of-the-art results across several information retrieval (IR) benchmarks.
  However, supervised training for improving these models requires numerous
  labeled examples, which are generally unavailable or expensive to acquire. In
  this work, we explore the effectiveness of extending reverse engineered
  adaptation to the context of information retrieval (RE-AdaptIR). We use
  RE-AdaptIR to improve LLM-based IR models using only unlabeled data. We
  demonstrate improved performance both in training domains as well as zero-shot
  in domains where the models have seen no queries. We analyze performance
  changes in various fine-tuning scenarios and offer findings of immediate use to
  practitioners.
paper_authors._id: 66797016335cad277cdd25df
paper_authors.name: William Fleshman
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-06-24T13:29:06.481Z
paper_authors._id: 64fa215b52e82dd43249f7e4
paper_authors.avatarUrl: /avatars/f73424b8fc72073cb0916e2d2cf9ca56.svg
paper_authors.fullname: William Fleshman
paper_authors.type: user
paper_authors.user: will-fleshman
paper_authors._id: 66797016335cad277cdd25e0
paper_authors.name: Benjamin Van Durme
paper_title: RE-AdaptIR: Improving Information Retrieval through Reverse Engineered
    Adaptation
paper_summary: Large language models (LLMs) fine-tuned for text-retrieval have demonstrated
  state-of-the-art results across several information retrieval (IR) benchmarks.
  However, supervised training for improving these models requires numerous
  labeled examples, which are generally unavailable or expensive to acquire. In
  this work, we explore the effectiveness of extending reverse engineered
  adaptation to the context of information retrieval (RE-AdaptIR). We use
  RE-AdaptIR to improve LLM-based IR models using only unlabeled data. We
  demonstrate improved performance both in training domains as well as zero-shot
  in domains where the models have seen no queries. We analyze performance
  changes in various fine-tuning scenarios and offer findings of immediate use to
  practitioners.
paper_ai_keywords: reverse engineered adaptation RE-AdaptIR LLM-based IR models zero-shot domains