通过高级提示工程技术增强大型语言模型的情感分类和讽刺检测
Enhancing Sentiment Classification and Irony Detection in Large Language Models through Advanced Prompt Engineering Techniques
AI总结：
本文通过高级提示工程技术提升大型语言模型在情感分类和讽刺检测中的性能，发现不同提示策略对不同模型和任务效果显著。
摘要：
本研究探讨了使用提示工程来增强大型语言模型（LLMs），特别是GPT-4o-mini和gemini-1.5-flash在情感分析任务中的应用。它评估了包括少样本学习、思维链提示和自一致性在内的先进提示技术，与基线方法进行比较。关键任务包括情感分类、基于方面的情感分析以及检测细微的 nuances 如讽刺。研究详细介绍了理论背景、数据集和方法，评估了LLMs的性能，通过准确率、召回率、精确率和F1分数进行衡量。发现表明，先进的提示显著提高了情感分析的性能，其中少样本方法在GPT-4o-mini中表现突出，而思维链提示在gemini-1.5-flash中将讽刺检测提升高达46%。因此，尽管先进的提示技术总体上提高了性能，但少样本提示在GPT-4o-mini中表现最佳，而思维链提示在gemini-1.5-flash中对讽刺检测效果显著，这表明提示策略必须根据模型和任务进行调整。这突显了提示设计必须与LLM的架构和任务的语义复杂性相匹配的重要性。
英文摘要：
This study investigates the use of prompt engineering to enhance large language models (LLMs), specifically GPT-4o-mini and gemini-1.5-flash, in sentiment analysis tasks. It evaluates advanced prompting techniques like few-shot learning, chain-of-thought prompting, and self-consistency against a baseline. Key tasks include sentiment classification, aspect-based sentiment analysis, and detecting subtle nuances such as irony. The research details the theoretical background, datasets, and methods used, assessing performance of LLMs as measured by accuracy, recall, precision, and F1 score. Findings reveal that advanced prompting significantly improves sentiment analysis, with the few-shot approach excelling in GPT-4o-mini and chain-of-thought prompting boosting irony detection in gemini-1.5-flash by up to 46%. Thus, while advanced prompting techniques overall improve performance, the fact that few-shot prompting works best for GPT-4o-mini and chain-of-thought excels in gemini-1.5-flash for irony detection suggests that prompting strategies must be tailored to both the model and the task. This highlights the importance of aligning prompt design with both the LLM's architecture and the semantic complexity of the task.
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。
