3 papers
cs.CL2026
XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
Qili Zhang, Qianren Mao, Yangyifei Luo +15
Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output…
cs.CL2026
DPI: Exploiting Parameter Heterogeneity for Interference-Free Fine-Tuning
Xiaoyu Liu, Xiaoyu Guan, Di Liang +1
Supervised fine-tuning (SFT) is a crucial step for adapting large language models (LLMs) to downstream tasks. However, conflicting objectives across heterogeneous SFT tasks often i…
cs.IR2025
DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning
Qingyun Sun, Jiaqi Yuan, Shan He +5
Graph Retrieval-Augmented Generation has emerged as a powerful paradigm for grounding large language models with external structured knowledge. However, existing Graph RAG methods…