4 papers
Do Current Retrievers Cover All the Evidence? A Controlled Study of Conjunctive Cross-Page Retrieval
Sungguk Cha, DongWook Kim, Mintae Kim +3
Finding a long document relevant to a multi-part request is not the same as establishing that it contains every requested piece of evidence. We study this gap for conjunctive docum…
ReinPool: Reinforcement Learning Pooling Multi-Vector Embeddings for Retrieval System
Sungguk Cha, DongWook Kim, Mintae Kim +3
Multi-vector embedding models have emerged as a powerful paradigm for document retrieval, preserving fine-grained visual and textual details through token-level representations. Ho…
Annotation-Free Reinforcement Learning Query Rewriting via Verifiable Search Reward
Sungguk Cha, DongWook Kim, Taeseung Hahn +3
Optimizing queries for Retrieval-Augmented Generation (RAG) systems poses a significant challenge, particularly across diverse modal indices. We introduce RL-QR, a novel annotation…
ixi-GEN: Efficient Industrial sLLMs through Domain Adaptive Continual Pretraining
Seonwu Kim, Yohan Na, Kihun Kim +7
The emergence of open-source large language models (LLMs) has expanded opportunities for enterprise applications; however, many organizations still lack the infrastructure to deplo…