CAREER: Enriching Conversational Information Retrieval via Mixed-Initiative Interactions

University of Massachusetts Amherst:
Hamed Zamani, PI

Project Award Information

NSF Award Number: 2143434
Award Title: CAREER: Enriching Conversational Information Retrieval via Mixed-Initiative Interactions
Duration: 7/01/2022 - 06/30/2027

Project Abstract

It has become clear that providing access to information through natural language conversations will play a significant role in the future of search technology. This will be enabled by developing efficient and effective conversational search engines. Existing systems are generally designed based on a query-response paradigm, in which the user initiates the interaction by submitting a query, and the system responds to the query with one or more documents. This process repeats until the user terminates the search session. This is not an optimal interaction design for conversational search systems. For instance, the system may ask a clarifying question instead of providing an answer to ambiguous questions. Or the system can recommend new information even though it is not an explicit response to the search query, but it may contribute to the ultimate goal of user satisfaction. The mentioned query-response paradigm does not support these natural conversational interactions. This CAREER award aims to advance the state-of-the-art by envisioning solutions that go beyond this paradigm.

To achieve this goal, this project studies theoretical and machine learning solutions for generating and handling mixed-initiative interactions in information seeking conversations. In more detail, this project explores the following three research thrusts: (1) developing theoretical foundations for measuring mixed-initiative information seeking conversations; (2) developing models for clarifying the user's information needs which is considered as the most common mixed-initiative interaction type; and (3) developing models for proactive informational contributions to ongoing conversations. In addition to these algorithmic and modeling contributions, this project also develops a number of invaluable resources for advancing the field of conversational information retrieval, including a conversational scholarly assistant agent that will be used as a tool for online experimentation and public data creation.

Publications

IR-1285: Hamed Zamani, Johanne R. Trippas, Jeff Dalton, and Filip Radlinski, "Conversational Information Seeking," in Foundation and Trends in Information Retrieval (FntIR), 2023, Vol. 17: No. 3-4, pp 244-456.

IR-1292: Sheshera Mysore, Mahmood Jasim, Andrew McCallum, and Hamed Zamani, "Editable User Profiles for Controllable Text Recommendation," in the Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ‘23), Taipei, Taiwan, July 23-27, 2023, pp. 993-1003.

IR-1300: Hansi Zeng, Surya Kallumadi, Zaid Alibadi, Rodrigo Nogueira, and Hamed Zamani, "A Personalized Dense Retrieval Framework for Unified Information Access," in the Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ‘23), Taipei, Taiwan, July 23-27, 2023, pp. 121-130.

IR-1311: Chris Samarinas, Pracha Promthaw, Rohan Lekhwani, Sheshera Mysore, Sung Huang, Atharva Nijasure, Hansi Zeng, and Hamed Zamani, "Multi-Modal Augmentation for Large Language Models with Applications to Task-Oriented Dialogues," in the 2nd Proceedings of Alexa Prize TaskBot (2023).

IR-1312: Sheshera Mysore, Andrew McCallum, and Hamed Zamani, "Large Language Model Augmented Narrative Driven Recommendations," in Proceedings of the 17th ACM Conference on Recommender Systems (RecSys '23), September 18-22, 2023, Singapore, pp. 777–783.

IR-1301: Tavakoli, L., Zamani, H., Trippas, J., Scholer, F. and Sanderson, M., "Online and Offline Evaluation in Search Clarification," in ACM Transactions on Information Systems (TOIS); Nov. 2024; Volume 43, Issue 1, Article No. 2; pp. 1-30

IR-1305: Tavakoli, L., Castiglia, G., Calo, F., Deldjoo, Y., Zamani, H. and Trippas, J., "Understanding Modality Preferences in Search Clarification," In Online Proceedings of the 1st Workshop on Multimodal Search and Recommendations (CIKM MMSR '24) at the 33rd ACM International Conference on Information and Knowledge Management (CIKM '24), Boise, ID, October 21-25, 2024

IR-1307: Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani, "LaMP: When Large Language Models Meet Personalization," in the Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), August 11-16, 2024, Bangkok, Thailand pp. 7370-7392.

IR-1316: Alireza Salemi and Hamed Zamani, "Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models," in the Proceedings of The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '24), July 14-18, 2024, Washington, DC, USA, pp. 741-751.

IR-1318: Chris Samarinas and Hamed Zamani, "ProCIS: A Benchmark for Proactive Retrieval in Conversations," to appear in the Proceedings of The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '24), July 14–18, 2024, Washington D.C., USA

IR-1319: (2024) Zamani, H. and Bendersky, M., "Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization," in the Proceedings of The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '24), Washington, DC, USA, July 14-18, 2024, pp. 2641-2646.

IR-1320: Sudarshan Lamkhede, Hamed Zamani, Moumita Bhattacharya, and Hongning Wang, "Third Workshop on Personalization and Recommendations in Search (PaRiS)," in the Proceedings of The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '24), July 14–18, 2024, Washington D.C., USA, pp. 3065-3069.

IR-1323: Samarinas, C., Promthaw, P., Nijasure, A., Zeng, H., Killingback, J. and Zamani, H., "Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models," CIIR Technical Report.

IR-1328: Aliannejadi, M., Gwizdka, J. and Zamani, H., "Interactions with Generative Information Retrieval Systems," In Information Access in the Era of Generative AI (Editors: Chirag Shah and Ryen White), pp. 47-71, 2025.

IR-1345: Samarinas, C., Krubner, A., Salemi, A., Kim, Y. and Zamani, H., "Beyond Factual Accuracy: Evaluating Coverage of Diverse Factual Information in Long-form Text Generation," To Appear in the Proceedings of The 63rd Annual Meeting of the Association for Computational Linguistics, Vienna, Austria, July 27–August 1st, 2025.

IR-1346: Samarinas, C. and Zamani, H., "Distillation and Refinement of Reasoning in Small Language Models for Document Re-ranking," To Appear in the Proceedings of the 15th International Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR '25), Padua, Italy, July 18, 2025.

IR-1348: Tavakoli, L. and Zamani, H., "Reliable Annotations with Less Effort: Evaluating LLM-Human Collaboration in Search Clarifications," To appear in the Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR '25), Padua, Italy, July 18, 2025.

Point of Contact: Hamed Zamani - zamani@cs.umass.edu
Center for Intelligent Information Retrieval (CIIR)
Manning College of Information and Computer Sciences
University of Massachusetts Amherst
140 Governors Drive
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. 2143434. 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.