3 papers
cs.CL2026
Feedback Adaptation for Retrieval-Augmented Generation
Jihwan Bang, Seunghan Yang, Kyuhong Shim +3
Retrieval-Augmented Generation (RAG) systems are typically evaluated under static assumptions, despite being frequently corrected through user or expert feedback in deployment. Exi…
cs.CL2025
Think Straight, Stop Smart: Structured Reasoning for Efficient Multi-Hop RAG
Jihwan Bang, Juntae Lee, Seunghan Yang +1
Multi-hop retrieval-augmented generation (RAG) is a promising strategy for complex reasoning, yet existing iterative prompting approaches remain inefficient. They often regenerate…
cs.LG2024
Feature Diversification and Adaptation for Federated Domain Generalization
Seunghan Yang, Seokeon Choi, Hyunsin Park +3
Federated learning, a distributed learning paradigm, utilizes multiple clients to build a robust global model. In real-world applications, local clients often operate within their…