3 papers
cs.CL2026
LLM-Confidence Reranker: A Training-Free Approach for Enhancing Retrieval-Augmented Generation Systems
Zhipeng Song, Xiangyu Kong, Xinrui Bao +5
Large language models (LLMs) have revolutionized natural language processing, yet hallucinations in knowledge-intensive tasks remain a critical challenge. Retrieval-augmented gener…
cs.CL2026
Less is More for RAG: Information Gain Pruning for Generator-Aligned Reranking and Evidence Selection
Zhipeng Song, Yizhi Zhou, Xiangyu Kong +6
Retrieval-augmented generation (RAG) grounds large language models with external evidence, but under a limited context budget, the key challenge is deciding which retrieved passage…
cs.IR2025
Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods
Meng Yan, Cai Xu, Xujing Wang +3
Recommender systems based on graph neural networks perform well in tasks such as rating and ranking. However, in real-world recommendation scenarios, noise such as user misuse and…