12 citations · 12 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
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…
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…