通过检索增强生成实现LLMs对名人善/恶声誉的判断
Good/Evil Reputation Judgment of Celebrities by LLMs via Retrieval Augmented Generation
AI总结：
本文研究LLMs能否判断名人善恶声誉，提出利用ChatGPT提取并分类名人相关句子为“方面”，结合检索增强生成（RAG）框架进行善恶判断，实验证明该方法显著优于现有含RAG功能的服务。
摘要：
本文旨在探讨大型语言模型（LLMs）能否理解善恶，从而对名人的善恶声誉进行判断。具体而言，我们首先将大型语言模型（即ChatGPT）应用于从网页上关于名人的文章中收集提及目标名人的句子的任务。接下来，由ChatGPT根据内容对收集到的句子进行分类，其中ChatGPT为每个类别分配一个类别名称。这些分配的类别名称被称为每位名人的“方面”。然后，通过应用检索增强生成（RAG）框架，我们展示了大型语言模型在判断每位名人的方面和描述的善恶声誉的任务中非常有效。最后，在证明所提方法优于结合RAG功能的现有服务方面，我们表明所提出的判断每位名人的方面/描述的善恶的方法显著优于结合RAG功能的现有服务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
