9 citations · 9 across the 1 of their papers we have counts for
4 papers
HiChunk: Evaluating and Enhancing Retrieval-Augmented Generation with Hierarchical Chunking
Wensheng Lu, Keyu Chen, Ruizhi Qiao +1
Retrieval-Augmented Generation (RAG) enhances the response capabilities of language models by integrating external knowledge sources. However, document chunking as an important par…
Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View
Hao Liao, Jiwei Zhang, Jianxun Lian +7
Recommender systems based on Large Language Models (LLMs) are often plagued by hallucinations of out-of-domain (OOD) items. To address this, we propose RecLM, a unified framework t…
Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors
Guanghua Li, Wensheng Lu, Wei Zhang +5
The proliferation of fake news has had far-reaching implications on politics, the economy, and society at large. While Fake news detection methods have been employed to mitigate th…
Aligning Large Language Models for Controllable Recommendations
Wensheng Lu, Jianxun Lian, Wei Zhang +4
Inspired by the exceptional general intelligence of Large Language Models (LLMs), researchers have begun to explore their application in pioneering the next generation of recommend…