Retrieval-Enhanced Machine Learning: Synthesis and Opportunities
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.
669a5cfac429888b30249c6c
To Eun Kim
claimed_verified
2024-07-22T07:10:17.294Z
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To Eun Kim
user
teknology
669a5cfac429888b30249c6d
Alireza Salemi
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Andrew Drozdov
claimed_verified
2024-07-22T07:10:21.540Z
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Andrew Drozdov
user
mrdrozdov
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Fernando Diaz
extracted_pending
2024-07-19T12:32:59.611Z
65cd2d90f15f9dc0b66a0a13
/avatars/8cd556bbe590fdd8b5f9eb134282f810.svg
Fernando Diaz
user
diazf
669a5cfac429888b30249c70
Hamed Zamani
Retrieval-Enhanced Machine Learning: Synthesis and Opportunities
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.
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.
Retrieval-Enhanced Machine Learning (REML) Information Retrieval (IR)