Year: 2025
Type: conference-paper
Abstract: The massive user-generated content (UGC) available in Chinese social media is giving rise to the possibility of studying internet buzzwords.In this paper, we study if large language models (LLMs) can generate accurate definitions for these buzzwords based on UGC as examples.Our work serves a threefold contribution.First, we introduce CHEER, the first dataset of Chinese internet buzzwords, each annotated with a definition and relevant UGC.Second, we propose a novel method, called RESS, to effectively steer the comprehending process of LLMs to produce more accurate buzzword definitions, mirroring the skills of human language learning.Third, with CHEER, we benchmark the strengths and weaknesses of various off-the-shelf definition generation methods and our RESS.Our benchmark demonstrates the effectiveness of RESS while revealing crucial shared challenges: over-reliance on prior exposure, underdeveloped inferential abilities, and difficulty identifying high-quality UGC to facilitate comprehension.We believe our work lays the groundwork for future advancements in LLM-based definition generation.Our dataset and code are available at https://github.com/SCUNLP/Buzzword.
Authors: Chen Huang, Junkai Luo, Xinzuo Wang, Wenqiang Lei, Jiancheng Lv
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G1289754401, G5411532798, G6978877745
OpenAlex ID: W4412945355