1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.CL2025
MoC: Mixtures of Text Chunking Learners for Retrieval-Augmented Generation System
Jihao Zhao, Zhiyuan Ji, Zhaoxin Fan +5
Retrieval-Augmented Generation (RAG), while serving as a viable complement to large language models (LLMs), often overlooks the crucial aspect of text chunking within its pipeline.…
cs.CR2025★ 1 cited
SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model
Xun Liang, Simin Niu, Zhiyu Li +8
The indexing-retrieval-generation paradigm of retrieval-augmented generation (RAG) has been highly successful in solving knowledge-intensive tasks by integrating external knowledge…