313 citations · 922 across the 48 of their papers we have counts for
53 papers
Large Language Model Augmented Narrative Driven Recommendations
Sheshera Mysore, Andrew McCallum, Hamed Zamani
Narrative-driven recommendation (NDR) presents an information access problem where users solicit recommendations with verbose descriptions of their preferences and context, for exa…
Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answering
Alireza Salemi, Mahta Rafiee, Hamed Zamani
This paper studies a category of visual question answering tasks, in which accessing external knowledge is necessary for answering the questions. This category is called outside-kn…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
Soft Prompt Decoding for Multilingual Dense Retrieval
Zhiqi Huang, Hansi Zeng, Hamed Zamani +1
In this work, we explore a Multilingual Information Retrieval (MLIR) task, where the collection includes documents in multiple languages. We demonstrate that applying state-of-the-…
Multivariate Representation Learning for Information Retrieval
Hamed Zamani, Michael Bendersky
Dense retrieval models use bi-encoder network architectures for learning query and document representations. These representations are often in the form of a vector representation…
A Personalized Dense Retrieval Framework for Unified Information Access
Hansi Zeng, Surya Kallumadi, Zaid Alibadi +2
Developing a universal model that can efficiently and effectively respond to a wide range of information access requests -- from retrieval to recommendation to question answering -…
A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering
Alireza Salemi, Juan Altmayer Pizzorno, Hamed Zamani
Knowledge-Intensive Visual Question Answering (KI-VQA) refers to answering a question about an image whose answer does not lie in the image. This paper presents a new pipeline for…
Generalized Weak Supervision for Neural Information Retrieval
Yen-Chieh Lien, Hamed Zamani, W. Bruce Croft
Neural ranking models (NRMs) have demonstrated effective performance in several information retrieval (IR) tasks. However, training NRMs often requires large-scale training data, w…
LaMP: When Large Language Models Meet Personalization
Alireza Salemi, Sheshera Mysore, Michael Bendersky +1
This paper highlights the importance of personalization in large language models and introduces the LaMP benchmark -- a novel benchmark for training and evaluating language models…
Editable User Profiles for Controllable Text Recommendation
Sheshera Mysore, Mahmood Jasim, Andrew McCallum +1
Methods for making high-quality recommendations often rely on learning latent representations from interaction data. These methods, while performant, do not provide ready mechanism…
Learning List-Level Domain-Invariant Representations for Ranking
Ruicheng Xian, Honglei Zhuang, Zhen Qin +7
Domain adaptation aims to transfer the knowledge learned on (data-rich) source domains to (low-resource) target domains, and a popular method is invariant representation learning,…
You can't pick your neighbors, or can you? When and how to rely on retrieval in the NN-LM
Andrew Drozdov, Shufan Wang, Razieh Rahimi +3
Retrieval-enhanced language models (LMs), which condition their predictions on text retrieved from large external datastores, have recently shown significant perplexity improvement…
FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation
Sebastian Hofstätter, Jiecao Chen, Karthik Raman +1
Retrieval-augmented generation models offer many benefits over standalone language models: besides a textual answer to a given query they provide provenance items retrieved from an…
Multi-Task Retrieval-Augmented Text Generation with Relevance Sampling
Sebastian Hofstätter, Jiecao Chen, Karthik Raman +1
This paper studies multi-task training of retrieval-augmented generation models for knowledge-intensive tasks. We propose to clean the training set by utilizing a distinct property…
Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems
Sebastian Hofstätter, Nick Craswell, Bhaskar Mitra +2
Recently, several dense retrieval (DR) models have demonstrated competitive performance to term-based retrieval that are ubiquitous in search systems. In contrast to term-based mat…
MIMICS-Duo: Offline & Online Evaluation of Search Clarification
Leila Tavakoli, Johanne R. Trippas, Hamed Zamani +2
Asking clarification questions is an active area of research; however, resources for training and evaluating search clarification methods are not sufficient. To address this issue,…
Retrieval-Enhanced Machine Learning
Hamed Zamani, Fernando Diaz, Mostafa Dehghani +2
Although information access systems have long supported people in accomplishing a wide range of tasks, we propose broadening the scope of users of information access systems to inc…
Curriculum Learning for Dense Retrieval Distillation
Hansi Zeng, Hamed Zamani, Vishwa Vinay
Recent work has shown that more effective dense retrieval models can be obtained by distilling ranking knowledge from an existing base re-ranking model. In this paper, we propose a…
Conversational Information Seeking
Hamed Zamani, Johanne R. Trippas, Jeff Dalton +1
Conversational information seeking (CIS) is concerned with a sequence of interactions between one or more users and an information system. Interactions in CIS are primarily based o…
Explaining Documents' Relevance to Search Queries
Razieh Rahimi, Youngwoo Kim, Hamed Zamani +1
We present GenEx, a generative model to explain search results to users beyond just showing matches between query and document words. Adding GenEx explanations to search results gr…
DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions
Neha Kennard, Tim O'Gorman, Rajarshi Das +6
At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume vast amounts of highly technical text.…
Analysing Mixed Initiatives and Search Strategies during Conversational Search
Mohammad Aliannejadi, Leif Azzopardi, Hamed Zamani +3
Information seeking conversations between users and Conversational Search Agents (CSAs) consist of multiple turns of interaction. While users initiate a search session, ideally a C…
Current Challenges and Future Directions in Podcast Information Access
Rosie Jones, Hamed Zamani, Markus Schedl +11
Podcasts are spoken documents across a wide-range of genres and styles, with growing listenership across the world, and a rapidly lowering barrier to entry for both listeners and c…
Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking
Sebastian Hofstätter, Bhaskar Mitra, Hamed Zamani +2
An emerging recipe for achieving state-of-the-art effectiveness in neural document re-ranking involves utilizing large pre-trained language models - e.g., BERT - to evaluate all in…