
NSF Org: |
IIS Division of Information & Intelligent Systems |
Recipient: |
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Initial Amendment Date: | August 12, 2024 |
Latest Amendment Date: | March 24, 2025 |
Award Number: | 2402873 |
Award Instrument: | Continuing Grant |
Program Manager: |
Cornelia Caragea
ccaragea@nsf.gov (703)292-2706 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | October 1, 2024 |
End Date: | September 30, 2027 (Estimated) |
Total Intended Award Amount: | $769,633.00 |
Total Awarded Amount to Date: | $612,970.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
101 COMMONWEALTH AVE AMHERST MA US 01003-9252 (413)545-0698 |
Sponsor Congressional District: |
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Primary Place of Performance: |
COMMONWEALTH AVE AMHERST MA US 01003-9346 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | Info Integration & Informatics |
Primary Program Source: |
01002425DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Retrieval-Enhanced Machine Learning (REML) refers to a subset of machine learning models that make predictions by utilizing the results of one or more retrieval models from collections of documents. REML has recently attracted considerable attention due to its wide range of applications, including knowledge grounding for question answering and improving generalization in large language models. However, REML has mainly been studied from a machine learning perspective, without focusing on the retrieval aspects. Preliminary explorations have demonstrated the importance of retrieval on downstream REML performance. This observation has motivated this project in order to provide an alternative view to REML and study REML from an information retrieval (IR) perspective. In this perspective, the retrieval component in REML is framed as a search engine capable of supporting multiple, independent predictive models, as opposed to a single predictive model as is the case in the majority of existing work.
This project consists of three major research thrusts. First, the project will develop novel architectures and optimization solutions that provide information access to multiple machine learning models conducting a wide variety of tasks. Next, the project will study training and inference efficiency in the context of REML by focusing on the utilization of retrieval results by downstream machine learning models and the feedback they provide. Third, the project will study approaches for responsible REML by examining data control for content providers in REML and fairness and robustness across multiple downstream models. Without loss of generality, the project will primarily focus on a number of real-world language tasks, such as open-domain question answering, fact verification, and open-domain dialogue systems.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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