Can Large Language Models Understand Internet Buzzwords Through User-Generated Content

TL;DR

This paper introduces a new dataset and method to evaluate and improve large language models' ability to generate accurate definitions for Chinese internet buzzwords using user-generated content.

Contribution

It presents CHEER, a Chinese buzzword dataset, and RESS, a novel method to enhance LLMs' comprehension and definition generation of buzzwords.

Findings

RESS outperforms baseline methods in accuracy

Shared challenges include over-reliance on prior exposure

Difficulty in identifying high-quality UGC

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.