3 papers
cs.LG2026
Beyond RAG vs. Long-Context: Learning Distraction-Aware Retrieval for Efficient Knowledge Grounding
Seongwoong Shim, Myunsoo Kim, Jae Hyeon Cho +1
Retrieval-Augmented Generation (RAG) is a framework for grounding Large Language Models (LLMs) in external, up-to-date information. However, recent advancements in context window s…
cs.IR2026
ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generation
Sunguk Shin, Meeyoung Cha, Byung-Jun Lee +1
Retrieval-Augmented Generation (RAG) grounds language models in factual evidence but introduces critical challenges regarding knowledge conflicts between internalized parameters an…
cs.CL2025
K/DA: Automated Data Generation Pipeline for Detoxifying Implicitly Offensive Language in Korean
Minkyeong Jeon, Hyemin Jeong, Yerang Kim +3
Language detoxification involves removing toxicity from offensive language. While a neutral-toxic paired dataset provides a straightforward approach for training detoxification mod…