1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CL2026
Why Retrieval-Augmented Generation Fails: A Graph Perspective
Kai Guo, Xinnan Dai, Zhibo Zhang +5
Retrieval-Augmented Generation (RAG) has become a powerful and widely used approach for improving large language models by grounding generation in retrieved evidence. However, RAG…
cs.CL2024★ 1 cited
Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data
Dehai Min, Nan Hu, Rihui Jin +8
Augmenting Large Language Models (LLMs) for Question Answering (QA) with domain specific data has attracted wide attention. However, domain data often exists in a hybrid format, in…