用检索增强生成判断名人善恶声誉，效果优于现有服务。
Good/Evil Reputation Judgment of Celebrities by LLMs via Retrieval Augmented Generation
- 通过RAG框架分析名人相关语句的善恶倾向
- 对名人不同方面（如行为、言论）的声誉判断准确率显著提升
- 适合研究舆论情感与公众形象的学者及平台风控人员
本文旨在探究大语言模型（LLMs）是否能理解善恶，并用于判断名人的声誉。首先，利用ChatGPT从网络文章中提取提及目标名人的句子；随后，由ChatGPT对这些句子内容进行分类并命名，形成每个名人的“方面”（aspects）；接着，采用检索增强生成（RAG）框架，验证模型在判断各方面及描述的善恶声誉方面表现优异；最后，对比现有集成RAG功能的服务，本方法在判断名人各方面的善恶上显著更优。
原文摘要 · Abstract (English)
The purpose of this paper is to examine whether large language models (LLMs) can understand what is good and evil with respect to judging good/evil reputation of celebrities. Specifically, we first apply a large language model (namely, ChatGPT) to the task of collecting sentences that mention the target celebrity from articles about celebrities on Web pages. Next, the collected sentences are categorized based on their contents by ChatGPT, where ChatGPT assigns a category name to each of those categories. Those assigned category names are referred to as "aspects" of each celebrity. Then, by applying the framework of retrieval augmented generation (RAG), we show that the large language model is quite effective in the task of judging good/evil reputation of aspects and descriptions of each celebrity. Finally, also in terms of proving the advantages of the proposed method over existing services incorporating RAG functions, we show that the proposed method of judging good/evil of aspects/descriptions of each celebrity significantly outperform an existing service incorporating RAG functions.
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