A Collaborative Project with the University of Massachusetts Amherst and Carnegie Mellon University
University of Massachusetts Amherst:
Hamed Zamani, PI
Carnegie Mellon University:
Fernando Diaz, PI
Project Award Information
NSF Award Number: 2402873
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.
Publications
IR-1336: Quinn, D., Nouri, M., Patel, N., Salihu, J., Salemi, A., Lee, S., Zamani, H. and Alian, M., "Accelerating Retrieval-Augmented Generation," in the Proceedings of the ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 25), Rotterdam, The Netherlands, March 30-April 3, 2025, pp. 15-32.
IR-1340: Jin, B., Zeng, H., Yue, Z., Yoon, J., Arık, S., Wang, D., Zamani, H. and Han, J., "Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning," in the Proceedings of the 2nd Conference on Language Modeling (COLM 2025), Montreal, Canada, October 7-10, 2025.
IR-1350: Salemi, A. and Zamani, H., "Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization ," in the Proceedings of the 11th ACM SIGIR / The 15th International Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR 2025), Padua, Italy, July 18, 2025, pp. 183-193.
IR-1354: Salemi, A., Samarinas, C. and Zamani, H., "Plan-and-Refine in RAG: Generating Diverse and Comprehensive Responses through Global Exploration and Local Exploitation ," In the Proceedings of the 2026 ACM Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR 2026), Melbourne/Naarm, Australia, July 25, 2026, pp. 324-336.
IR-1356: Salemi, A. and Zamani, H., "LaMP-QA: A Benchmark for Personalized Long-form Question Answering," in the Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025), Suzhou, China, November 4-9, 2025, pp. 1139-1159.
IR-1360: Diaz, F., Drozdov, A., Kim, T., Salemi, A. and Zamani, H., "The Second Tutorial on Retrieval-Enhanced Machine Learning: Synthesis and Opportunities," In proceedings of 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’25), Padua, Italy, July 13-18, 2025, pp. 4130-4133.
IR-1361: Diaz, F., Drozdov, A., Kim, T., Salemi, A. and Zamani, H., "Retrieval-Enhanced Machine Learning: Synthesis and Opportunities," In proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP ’24), Tokyo, Japan, December 9-12, 2024, pp. 299-302.
IR-1369: Killingback, J., Rafiee, M., Manas, M. and Zamani, H., "Scaling Laws for Embedding Dimension in Information Retrieval," In the Proceeding of the ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2026), Melbourne, Australia, July 20-24, 2026, pp. 790-800.
IR-1374: Amirizaniani, M., Salemi, A. and Zamani, H., "Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering," In the Proceeding of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2026), July 20–24, 2026, Melbourne, Australia, pp. 3580–3586.
IR-1377: Rafiee, M., Soudani, H., Abbasiantaeb, Z., Aliannejadi, M., Hasibi, F. and Zamani, H., "Total Recall QA: A Verifiable Evaluation Suite for Deep Research Agents," In the Proceeding of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2026), July 20–24, 2026, Melbourne, Australia, pp. 3365-3373.
IR-1378: Kim, T., Salemi, A., Diaz, F. and Zamani, H., "Evaluation of Agents under Simulated AI Marketplace Dynamics," In the Proceeding of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2026), July 20–24, 2026, Melbourne, Australia, pp. 2672–2683.
IR-1379: Zeng, H., Collins, L., Kumar, B., Shah, N. and Zamani, H., "CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search," To appear in the Proceedings of the Conference on Language Modeling (COLM 2026), San Francisco, CA, October 6 - 9, 2026.
IR-1390: Zeng, H., "Generative Information Retrieval for Real World," Ph.D. Dissertation, University of Massachusetts Amherst, September 2026.
IR-1391: Alam, Z., Salemi, A. and Zamani, H., "Critic-R: Improving Agentic Search using Instruction-tuned Retrievers with Natural Language Introspective Feedback," To appear in the Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026), October 24-29, 2026, Budapest, Hungary.
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 Amherst) 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.
