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Searching Personal Collections
Michael Bendersky, Donald Metzler, Marc Najork +1
This article describes the history of information retrieval on personal document collections.
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Zhen Qin, Rolf Jagerman, Kai Hui +9
Ranking documents using Large Language Models (LLMs) by directly feeding the query and candidate documents into the prompt is an interesting and practical problem. However, researc…
How Does Generative Retrieval Scale to Millions of Passages?
Ronak Pradeep, Kai Hui, Jai Gupta +5
Popularized by the Differentiable Search Index, the emerging paradigm of generative retrieval re-frames the classic information retrieval problem into a sequence-to-sequence modeli…
Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification
Jai Gupta, Yi Tay, Chaitanya Kamath +5
With the devastating outbreak of COVID-19, vaccines are one of the crucial lines of defense against mass infection in this global pandemic. Given the protection they provide, vacci…
Retrieval Augmentation for T5 Re-ranker using External Sources
Kai Hui, Tao Chen, Zhen Qin +4
Retrieval augmentation has shown promising improvements in different tasks. However, whether such augmentation can assist a large language model based re-ranker remains unclear. We…
Atomized Search Length: Beyond User Models
John Alex, Keith Hall, Donald Metzler
We argue that current IR metrics, modeled on optimizing user experience, measure too narrow a portion of the IR space. If IR systems are weak, these metrics undersample or complete…
Rethinking Search: Making Domain Experts out of Dilettantes
Donald Metzler, Yi Tay, Dara Bahri +1
When experiencing an information need, users want to engage with a domain expert, but often turn to an information retrieval system, such as a search engine, instead. Classical inf…
Surprise: Result List Truncation via Extreme Value Theory
Dara Bahri, Che Zheng, Yi Tay +2
Work in information retrieval has largely been centered around ranking and relevance: given a query, return some number of results ordered by relevance to the user. The problem of…
Choppy: Cut Transformer For Ranked List Truncation
Dara Bahri, Yi Tay, Che Zheng +2
Work in information retrieval has traditionally focused on ranking and relevance: given a query, return some number of results ordered by relevance to the user. However, the proble…
Separate and Attend in Personal Email Search
Yu Meng, Maryam Karimzadehgan, Honglei Zhuang +1
In personal email search, user queries often impose different requirements on different aspects of the retrieved emails. For example, the query "my recent flight to the US" require…
Domain Adaptation for Enterprise Email Search
Brandon Tran, Maryam Karimzadehgan, Rama Kumar Pasumarthi +2
In the enterprise email search setting, the same search engine often powers multiple enterprises from various industries: technology, education, manufacturing, etc. However, using…
Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering
Jiaming Shen, Maryam Karimzadehgan, Michael Bendersky +2
User information needs vary significantly across different tasks, and therefore their queries will also differ considerably in their expressiveness and semantics. Many studies have…