4 papers
RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects
Yiteng Tu, Weihang Su, Yujia Zhou +2
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieved from a knowledge base. However, its effectiveness is fundamen…
Parametric Retrieval Augmented Generation
Weihang Su, Yichen Tang, Qingyao Ai +6
Retrieval-augmented generation (RAG) techniques have emerged as a promising solution to enhance the reliability of large language models (LLMs) by addressing issues like hallucinat…
Foundations of GenIR
Qingyao Ai, Jingtao Zhan, Yiqun Liu
The chapter discusses the foundational impact of modern generative AI models on information access (IA) systems. In contrast to traditional AI, the large-scale training and superio…
Unsupervised dense retrieval with conterfactual contrastive learning
Haitian Chen, Qingyao Ai, Xiao Wang +3
Efficiently retrieving a concise set of candidates from a large document corpus remains a pivotal challenge in Information Retrieval (IR). Neural retrieval models, particularly den…