Cross-modal retrieval has been advanced by vision-language pre-trained models that encode images and texts into a shared dense embedding space. While dense representations effectively capture overall semantic similarity, they often obscure fine-grained visual-textual information needed for precise cross-modal matching....
Yoonseo Kim, Jung A. Choi, Cheonyoung Park et al.· 0 citations
In university academic advising, identical questions can require different answers depending on a student's department, admission cohort, and degree program, causing profile-blind retrievers to surface plausible but inapplicable evidence. We present proFILL, a method for transforming hoBIT, our college's current rule-b...
Yoonseo Kim, S. Lee, Joongheon Kim et al.· 0 citations
CAIRO, a user context-aware item profiling framework for LLM-based reranking, first structures raw metadata and reviews into objective features and subjective traits, and employs a lightweight profiler to select the most relevant information for each user-item pair with limited serving-time overhead.
Dojun Hwang, Seunghan Lee, Cheonyoung Park et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
