利用大语言模型中的思维链提示增强细粒度情感分类
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models
AI总结：
本文研究利用大语言模型的思维链提示提升应用商店评论的细粒度情感分类，在2000条亚马逊评论上准确率从84%提升至93%，验证了显式推理的有效性。
摘要：
我们探索了在大语言模型（LLMs）中使用思维链（CoT）提示，以提高应用商店评论中细粒度情感分类的准确性。传统的基于数值和极性的评分往往无法捕捉用户反馈中蕴含的细微情感。我们在2000条亚马逊应用评论上评估了思维链提示与简单提示的有效性，将每种方法的预测结果与人工判断进行比较。思维链提示将分类准确率从84%提高到93%，凸显了显式推理在提升情感分析性能方面的优势。
英文摘要：
We explore the use of Chain-of-Thought (CoT) prompting with large language models (LLMs) to improve the accuracy of granular sentiment categorization in app store reviews. Traditional numeric and polarity-based ratings often fail to capture the nuanced sentiment embedded in user feedback. We evaluated the effectiveness of CoT prompting versus simple prompting on 2000 Amazon app reviews by comparing each method's predictions to human judgements. CoT prompting improved classification accuracy from 84% to 93% highlighting the benefit of explicit reasoning in enhancing sentiment analysis performance.
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。
