3 papers
cs.CL2025
Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation
Guanhua Chen, Wenhan Yu, Xiao Lu +3
While Retrieval-Augmented Generation (RAG) plays a crucial role in the application of Large Language Models (LLMs), existing retrieval methods in knowledge-dense domains like law a…
cs.CL2025
SGIC: A Self-Guided Iterative Calibration Framework for RAG
Guanhua Chen, Yutong Yao, Lidia S. Chao +2
Recent research in retrieval-augmented generation (RAG) has concentrated on retrieving useful information from candidate documents. However, numerous methodologies frequently negle…
cs.CL2025
Not All LoRA Parameters Are Essential: Insights on Inference Necessity
Guanhua Chen, Yutong Yao, Ci-Jun Gao +3
Current research on LoRA primarily focuses on minimizing the number of fine-tuned parameters or optimizing its architecture. However, the necessity of all fine-tuned LoRA layers du…