title: Retrieval-Enhanced Machine Learning: Synthesis and Opportunities
summary: In the field of language modeling, models augmented with retrieval components
  have emerged as a promising solution to address several challenges faced in the
  natural language processing (NLP) field, including knowledge grounding,
  interpretability, and scalability. Despite the primary focus on NLP, we posit
  that the paradigm of retrieval-enhancement can be extended to a broader
  spectrum of machine learning (ML) such as computer vision, time series
  prediction, and computational biology. Therefore, this work introduces a formal
  framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by
  synthesizing the literature in various domains in ML with consistent notations
  which is missing from the current literature. Also, we found that while a
  number of studies employ retrieval components to augment their models, there is
  a lack of integration with foundational Information Retrieval (IR) research. We
  bridge this gap between the seminal IR research and contemporary REML studies
  by investigating each component that comprises the REML framework. Ultimately,
  the goal of this work is to equip researchers across various disciplines with a
  comprehensive, formally structured framework of retrieval-enhanced models,
  thereby fostering interdisciplinary future research.
paper_authors._id: 669a5cfac429888b30249c6c
paper_authors.name: To Eun Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-22T07:10:17.294Z
paper_authors._id: 6484ad61c39bcbb5b112fd4f
paper_authors.avatarUrl: /avatars/cec5391d3167b09d1ef22e0d8c998ea9.svg
paper_authors.fullname: To Eun Kim
paper_authors.type: user
paper_authors.user: teknology
paper_authors._id: 669a5cfac429888b30249c6d
paper_authors.name: Alireza Salemi
paper_authors._id: 669a5cfac429888b30249c6e
paper_authors.name: Andrew Drozdov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-22T07:10:21.540Z
paper_authors._id: 613fbc22be1df5145babcad0
paper_authors.avatarUrl: /avatars/2048e58c43997a60e2dabcfc13112eec.svg
paper_authors.fullname: Andrew Drozdov
paper_authors.type: user
paper_authors.user: mrdrozdov
paper_authors._id: 669a5cfac429888b30249c6f
paper_authors.name: Fernando Diaz
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-07-19T12:32:59.611Z
paper_authors._id: 65cd2d90f15f9dc0b66a0a13
paper_authors.avatarUrl: /avatars/8cd556bbe590fdd8b5f9eb134282f810.svg
paper_authors.fullname: Fernando Diaz
paper_authors.type: user
paper_authors.user: diazf
paper_authors._id: 669a5cfac429888b30249c70
paper_authors.name: Hamed Zamani
paper_title: Retrieval-Enhanced Machine Learning: Synthesis and Opportunities
paper_summary: In the field of language modeling, models augmented with retrieval components
  have emerged as a promising solution to address several challenges faced in the
  natural language processing (NLP) field, including knowledge grounding,
  interpretability, and scalability. Despite the primary focus on NLP, we posit
  that the paradigm of retrieval-enhancement can be extended to a broader
  spectrum of machine learning (ML) such as computer vision, time series
  prediction, and computational biology. Therefore, this work introduces a formal
  framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by
  synthesizing the literature in various domains in ML with consistent notations
  which is missing from the current literature. Also, we found that while a
  number of studies employ retrieval components to augment their models, there is
  a lack of integration with foundational Information Retrieval (IR) research. We
  bridge this gap between the seminal IR research and contemporary REML studies
  by investigating each component that comprises the REML framework. Ultimately,
  the goal of this work is to equip researchers across various disciplines with a
  comprehensive, formally structured framework of retrieval-enhanced models,
  thereby fostering interdisciplinary future research.
paper_ai_summary: A retrieval-enhanced machine learning (REML) framework integrates foundational information retrieval (IR) concepts to augment models across domains like NLP, computer vision, time series prediction, and computational biology.
paper_ai_keywords: Retrieval-Enhanced Machine Learning (REML) Information Retrieval (IR)