5 citations · 5 across the 4 of their papers we have counts for
5 papers
Inference-Free Multimodal Learned Sparse Retrieval for Production-Scale Visual Document Search
Gyu-Hwung Cho, Youngjune Lee, Kiyoon Jeong +5
As large-scale visual-document corpora such as arXiv papers and enterprise PDFs continue to grow, visual-document retrieval has gained increasing attention; yet it still lacks a de…
Sparse and Dense Retrievers Learn Better Together: Joint Sparse-Dense Optimization for Text-Image Retrieval
Jonghyun Song, Youngjune Lee, Gyu-Hwung Cho +3
Vision-Language Pretrained (VLP) models have achieved impressive performance on multimodal tasks, including text-image retrieval, based on dense representations. Meanwhile, Learned…
AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking
Soyoung Yoon, Gyuwan Kim, Gyu-Hwung Cho +1
Listwise reranking with large language models (LLMs) enhances top-ranked results in retrieval-based applications. Due to the limit in context size and high inference cost of long c…
RRADistill: Distilling LLMs' Passage Ranking Ability for Long-Tail Queries Document Re-Ranking on a Search Engine
Nayoung Choi, Youngjune Lee, Gyu-Hwung Cho +10
Large Language Models (LLMs) excel at understanding the semantic relationships between queries and documents, even with lengthy and complex long-tail queries. These queries are cha…
SLM as Guardian: Pioneering AI Safety with Small Language Models
Ohjoon Kwon, Donghyeon Jeon, Nayoung Choi +6
Most prior safety research of large language models (LLMs) has focused on enhancing the alignment of LLMs to better suit the safety requirements of humans. However, internalizing s…