5 papers
Improving Rare Medication Recommendation with Counterfactual Data Augmentation and Large Language Models
Shinhwan Kang, Soo Yong Lee, Jaewon Kim +2
AI-based medication recommendation systems have attracted substantial attention due to their potential to enhance patient safety and therapeutic outcomes. Despite the clinical impo…
KidnapRAG: A Black-Box Attack for Hijacking Reasoning in Agentic Retrieval-Augmented Generation Systems
Chanwoo Choi, Euntae Kim, Kyuho Lee +6
Retrieval-Augmented Generation (RAG) systems are vulnerable to poisoning attacks that inject malicious documents into the retrieval process to manipulate model outputs. Recent Agen…
The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systems
Chanwoo Choi, Jinsoo Kim, Sukmin Cho +2
With the growing adoption of retrieval-augmented generation (RAG) systems, various attack methods have been proposed to degrade their performance. However, most existing approaches…
Review-driven Personalized Preference Reasoning with Large Language Models for Recommendation
Jieyong Kim, Hyunseo Kim, Hyunjin Cho +4
Recent advancements in Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks, generating significant interest in their application to…
In-Context Learning with Noisy Labels
Junyong Kang, Donghyun Son, Hwanjun Song +1
In-context learning refers to the emerging ability of large language models (LLMs) to perform a target task without additional training, utilizing demonstrations of the task. Recen…