1 citations · 2 across the 8 of their papers we have counts for
7 papers · 1 filter
PEARL: Front-Loading Relational Chains for Multi-Hop Table Retrieval
Subeen Ho, Hyeongu Kang, SeongKu Kang +1
While large language models (LLMs) have shown strong capabilities in tabular reasoning, retrieving relevant tables remains challenging due to the fragmented and relational structur…
SCoRD: Semantic-Assisted Continual Retriever-Reranker Distillation for LLM-Based Recommendation
Seunghyun Baek, Gyuseok Lee, Seunghan Lee +3
Recommendation systems increasingly adopt a two-stage pipeline, where an ID-based retriever retrieves candidates and an LLM-based reranker refines their rankings. To improve retrie…
PairSem: LLM-Guided Pairwise Semantic Matching for Scientific Document Retrieval
Wonbin Kweon, Runchu Tian, SeongKu Kang +4
Scientific document retrieval is a critical task for enabling knowledge discovery and supporting research across diverse domains. However, existing dense retrieval methods often st…
SPRINT: Scalable and Predictive Intent Refinement for LLM-Enhanced Session-based Recommendation
Gyuseok Lee, Wonbin Kweon, Zhenrui Yue +5
Large language models (LLMs) have enhanced conventional recommendation models via user profiling, which generates representative textual profiles from users' historical interaction…
Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking
Yunyi Zhang, Ruozhen Yang, Siqi Jiao +2
Scientific paper retrieval is essential for supporting literature discovery and research. While dense retrieval methods demonstrate effectiveness in general-purpose tasks, they oft…
CoRank: LLM-Based Compact Reranking with Document Features for Scientific Retrieval
Runchu Tian, Xueqiang Xu, Bowen Jin +2
Scientific retrieval is essential for advancing scientific knowledge discovery. Within this process, document reranking plays a critical role in refining first-stage retrieval resu…