5 papers
Clickbait Detection via Large Language Models
Han Wang, Yi Zhu, Ye Wang +3
Clickbait, which aims to induce users with some surprising and even thrilling headlines for increasing click-through rates, permeates almost all online content publishers, such as…
Redefining Simplicity: Benchmarking Large Language Models from Lexical to Document Simplification
Jipeng Qiang, Minjiang Huang, Yi Zhu +3
Text simplification (TS) refers to the process of reducing the complexity of a text while retaining its original meaning and key information. Existing work only shows that large la…
New Evaluation Paradigm for Lexical Simplification
Jipeng Qiang, Minjiang Huang, Yi Zhu +3
Lexical Simplification (LS) methods use a three-step pipeline: complex word identification, substitute generation, and substitute ranking, each with separate evaluation datasets. W…
Progressive Document-level Text Simplification via Large Language Models
Dengzhao Fang, Jipeng Qiang, Yi Zhu +3
Research on text simplification has primarily focused on lexical and sentence-level changes. Long document-level simplification (DS) is still relatively unexplored. Large Language…
Prompt-tuning for Clickbait Detection via Text Summarization
Haoxiang Deng, Yi Zhu, Ye Wang +4
Clickbaits are surprising social posts or deceptive news headlines that attempt to lure users for more clicks, which have posted at unprecedented rates for more profit or commercia…