5 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.IR2026
LITTA: Late-Interaction and Test-Time Alignment for Visually-Grounded Multimodal Retrieval
Seonok Kim
Retrieving relevant evidence from visually rich documents such as textbooks, technical reports, and manuals is challenging due to long context, complex layouts, and weak lexical ov…
cs.CL2025
MedBioRAG: Semantic Search and Retrieval-Augmented Generation with Large Language Models for Medical and Biological QA
Seonok Kim
Recent advancements in retrieval-augmented generation (RAG) have significantly enhanced the ability of large language models (LLMs) to perform complex question-answering (QA) tasks…
cs.CL2025★ 5 cited
MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation
Seonok Kim
Large Language Models (LLMs) have demonstrated impressive capabilities across natural language processing tasks. However, their application to specialized domains such as medicine…