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cs.IR2025
InfoGain-RAG: Boosting Retrieval-Augmented Generation via Document Information Gain-based Reranking and Filtering
Zihan Wang, Zihan Liang, Zhou Shao +7
Retrieval-Augmented Generation (RAG) has emerged as a promising approach to address key limitations of Large Language Models (LLMs), such as hallucination, outdated knowledge, and…
cs.IR2024
TriSampler: A Better Negative Sampling Principle for Dense Retrieval
Zhen Yang, Zhou Shao, Yuxiao Dong +1
Negative sampling stands as a pivotal technique in dense retrieval, essential for training effective retrieval models and significantly impacting retrieval performance. While exist…