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20212026
most citedLearning to Rewrite Prompts for Personalized Text Generation

23 citations · 34 across the 18 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.LG2023

A Metadata-Driven Approach to Understand Graph Neural Networks

Ting Wei Li, Qiaozhu Mei, Jiaqi Ma

Graph Neural Networks (GNNs) have achieved remarkable success in various applications, but their performance can be sensitive to specific data properties of the graph datasets they…

cs.CL2023

Meta Semantic Template for Evaluation of Large Language Models

Yachuan Liu, Liang Chen, Jindong Wang +2

Do large language models (LLMs) genuinely understand the semantics of the language, or just memorize the training data? The recent concern on potential data contamination of LLMs h…

cs.CL2023★ 3 cited

Automated Evaluation of Personalized Text Generation using Large Language Models

Yaqing Wang, Jiepu Jiang, Mingyang Zhang +4

Personalized text generation presents a specialized mechanism for delivering content that is specific to a user's personal context. While the research progress in this area has bee…

cs.CL2023★ 23 cited

Learning to Rewrite Prompts for Personalized Text Generation

Cheng Li, Mingyang Zhang, Qiaozhu Mei +2

Facilitated by large language models (LLMs), personalized text generation has become a rapidly growing research direction. Most existing studies focus on designing specialized mode…

cs.LG2023★ 3 cited

Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

Jin Huang, Xingjian Zhang, Qiaozhu Mei +1

Large language models (LLMs) are gaining increasing attention for their capability to process graphs with rich text attributes, especially in a zero-shot fashion. Recent studies de…