我们仍然需要人工标注者吗？提示大型语言模型进行方面情感四元组预测
Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Prediction
AI总结：
本研究探讨了大型语言模型在五个数据集上执行方面情感四元组预测（ASQP）的零样本和少样本学习能力，发现其F1分数接近最先进的微调模型，能有效减少人工标注需求。
摘要：
方面情感四元组预测（ASQP）通过识别每个观点的意见词、方面词、方面类别和情感极性，促进对文本中表达观点的详细理解。然而，标注一整套训练样本来微调ASQP模型是一个资源密集型的过程。在本研究中，我们探讨了大型语言模型（LLMs）在五个多样化数据集上执行ASQP任务的零样本和少样本学习能力。我们报告的F1分数几乎与最先进的微调模型相当，并超越了此前报告的零样本和少样本性能。在Rest16餐厅领域数据集的20-shot设置下，LLMs取得了51.54的F1分数，相比之下，表现最佳的微调方法MVP得分为60.39。此外，我们还报告了LLMs在目标方面情感检测（TASD）中的性能，其F1分数接近微调模型，在30-shot设置下于Rest16上达到68.93，而MVP为72.76。尽管人工标注者对于实现最佳性能仍然不可或缺，但LLMs可以减少ASQP任务中对大量人工标注的需求。
英文摘要：
Aspect sentiment quad prediction (ASQP) facilitates a detailed understanding of opinions expressed in a text by identifying the opinion term, aspect term, aspect category and sentiment polarity for each opinion. However, annotating a full set of training examples to fine-tune models for ASQP is a resource-intensive process. In this study, we explore the capabilities of large language models (LLMs) for zero- and few-shot learning on the ASQP task across five diverse datasets. We report F1 scores almost up to par with those obtained with state-of-the-art fine-tuned models and exceeding previously reported zero- and few-shot performance. In the 20-shot setting on the Rest16 restaurant domain dataset, LLMs achieved an F1 score of 51.54, compared to 60.39 by the best-performing fine-tuned method MVP. Additionally, we report the performance of LLMs in target aspect sentiment detection (TASD), where the F1 scores were close to fine-tuned models, achieving 68.93 on Rest16 in the 30-shot setting, compared to 72.76 with MVP. While human annotators remain essential for achieving optimal performance, LLMs can reduce the need for extensive manual annotation in ASQP tasks.
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。
