most citedDo Large Language Models Know about Facts?

9 citations · 14 across the 12 of their papers we have counts for

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cs.CL2023

A Survey of Text Watermarking in the Era of Large Language Models

Aiwei Liu, Leyi Pan, Yijian Lu +7

Text watermarking algorithms are crucial for protecting the copyright of textual content. Historically, their capabilities and application scenarios were limited. However, recent a…

cs.CL20232 cited

Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction

Xuming Hu, Junzhe Chen, Aiwei Liu +3

How can we better extract entities and relations from text? Using multimodal extraction with images and text obtains more signals for entities and relations, and aligns them throug…

cs.CL20239 cited

Do Large Language Models Know about Facts?

Xuming Hu, Junzhe Chen, Xiaochuan Li +4

Large language models (LLMs) have recently driven striking performance improvements across a range of natural language processing tasks. The factual knowledge acquired during pretr…

cs.CL2023

Enhancing Cross-lingual Transfer via Phonemic Transcription Integration

Hoang H. Nguyen, Chenwei Zhang, Tao Zhang +2

Previous cross-lingual transfer methods are restricted to orthographic representation learning via textual scripts. This limitation hampers cross-lingual transfer and is biased tow…

cs.CL2023

An Unforgeable Publicly Verifiable Watermark for Large Language Models

Aiwei Liu, Leyi Pan, Xuming Hu +4

Recently, text watermarking algorithms for large language models (LLMs) have been proposed to mitigate the potential harms of text generated by LLMs, including fake news and copyri…

cs.CL2023

A Survey on Evaluation of Large Language Models

Yupeng Chang, Xu Wang, Jindong Wang +13

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to…