2 citations · 7 across the 10 of their papers we have counts for
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GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking
Yingjian Chen, Haoran Liu, Yinhong Liu +8
Large language models (LLMs) are widely used, but they often generate subtle factual errors, especially in long-form text. These errors are fatal in some specialized domains such a…
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential
Wei Tang, Yixin Cao, Jiahao Ying +4
Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models (LLMs). Targeting its bottleneck of retriever performance,…
FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion
Wei Tang, Zhiqian Wu, Yixin Cao +2
Knowledge graph completion (KGC) aims to predict missing facts in knowledge graphs (KGs), which is crucial as modern KGs remain largely incomplete. While training KGC models on mul…
NLPBench: Evaluating Large Language Models on Solving NLP Problems
Linxin Song, Jieyu Zhang, Lechao Cheng +3
Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP). Despite these successes, there remains a…