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20162024
most citedPerspectives on Large Language Models for Relevance Judgment

170 citations · 981 across the 54 of their papers we have counts for

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Showing 2021Show all

12 papers · 1 filter

cs.CL2021★ 2 cited

Tackling Query-Focused Summarization as A Knowledge-Intensive Task: A Pilot Study

Weijia Zhang, Svitlana Vakulenko, Thilina Rajapakse +2

Query-focused summarization (QFS) requires generating a summary given a query using a set of relevant documents. However, such relevant documents should be annotated manually and t…

cs.IR2021

Controllable Recommenders using Deep Generative Models and Disentanglement

Samarth Bhargav, Evangelos Kanoulas

In this paper, we consider controllability as a means to satisfy dynamic preferences of users, enabling them to control recommendations such that their current preference is met. W…

cs.CV2021★ 5 cited

Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poems

Dan Li, Shuai Wang, Jie Zou +4

In this work we propose a new task: artistic visualization of classical Chinese poems, where the goal is to generatepaintings of a certain artistic style for classical Chinese poem…

cs.IR2021★ 29 cited

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…

cs.IR2021★ 48 cited

Cross-Market Product Recommendation

Hamed Bonab, Mohammad Aliannejadi, Ali Vardasbi +2

We study the problem of recommending relevant products to users in relatively resource-scarce markets by leveraging data from similar, richer in resource auxiliary markets. We hypo…

cs.CL2021

Legal Search in Case Law and Statute Law

Julien Rossi, Evangelos Kanoulas

In this work we describe a method to identify document pairwise relevance in the context of a typical legal document collection: limited resources, long queries and long documents.…