3 papers
cs.CL2025
KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering
Yushi Sun, Kai Sun, Yifan Ethan Xu +4
Retrieval-Augmented Generation (RAG) mitigates hallucination in Large Language Models (LLMs) by incorporating external data, with Knowledge Graphs (KGs) offering crucial informatio…
cs.LG2025
SCAR: A Characterization Scheme for Multi-Modal Dataset
Ri Su, Zhao Chen, Caleb Chen Cao +2
Foundation models exhibit remarkable generalization across diverse tasks, largely driven by the characteristics of their training data. Recent data-centric methods like pruning and…
cs.CL2024
Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability
Xiangsen Chen, Xuming Hu, Nan Tang
Retrieve-augmented generation (RAG) frameworks have emerged as a promising solution to multi-hop question answering(QA) tasks since it enables large language models (LLMs) to incor…