A Collaborative Project with the University of Massachusetts Amherst and Carnegie Mellon University
University of Massachusetts Amherst:
Hamed Zamani, PI
Mohit Iyyer, co-PI
Carnegie Mellon University:
Fernando Diaz, PI
Project Award Information
NSF Award Number: 2143434
Award Title: Collaborative Research: III: Medium: Retrieval-Enhanced Machine Learning Through an Information Retrieval Lens
Duration:10/01/2024 - 09/30/2027
Project 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 or otherwise abstractly represented items. 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 motivates an alternative view of the field by studying 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.
Point of Contact: Hamed Zamani - zamani@cs.umass.edu
Center for Intelligent Information Retrieval (CIIR)
Manning College of Information and Computer Sciences
140 Governors Drive
University of Massachusetts Amherst
Amherst, MA 01003-9264
This material is based upon work supported in part by the Center for Intelligent Information Retrieval (CIIR) and in part by the National Science Foundation under Grant No. 2402873 (UMass) and 2402874 (CMU). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.