探索链式思维提示中的统一意义表示进行零样本ACS A
Exploring Zero-Shot ACSA with Unified Meaning Representation in Chain-of-Thought Prompting
AI总结：
本文提出了一种基于统一意义表示的链式思维提示方法，用于零样本方面-类别情感分析，探讨了其在不同模型规模上的有效性。
摘要：
方面-类别情感分析（ACS A）通过在评论中识别特定主题及其相关情感提供细粒度的见解。尽管监督学习方法主导这一领域，但新领域注释数据的稀缺性和高成本构成了重大障碍。我们主张在资源有限的情况下利用大语言模型（LLMs）进行零样本学习是一种实用的替代方法。在本工作中，我们提出了一种新的链式思维（CoT）提示技术，利用中间的统一意义表示（UMR）来结构ACS A任务的推理过程。我们评估了基于UMR的方法与标准CoT基线在三个模型（Qwen3-4B、Qwen3-8B和Gemini-2.5-Pro）和四个多样化数据集上的表现。我们的发现表明，UMR的有效性可能取决于模型。尽管初步结果表明中等规模的模型如Qwen3-8B的性能相当，但这些观察结果需要进一步研究，特别是关于其在较小模型架构中的潜在适用性。需要进一步研究以确定这些发现在不同模型规模上的通用性。
英文摘要：
Aspect-Category Sentiment Analysis (ACSA) provides granular insights by identifying specific themes within reviews and their associated sentiment. While supervised learning approaches dominate this field, the scarcity and high cost of annotated data for new domains present significant barriers. We argue that leveraging large language models (LLMs) in a zero-shot setting is a practical alternative where resources for data annotation are limited. In this work, we propose a novel Chain-of-Thought (CoT) prompting technique that utilises an intermediate Unified Meaning Representation (UMR) to structure the reasoning process for the ACSA task. We evaluate this UMR-based approach against a standard CoT baseline across three models (Qwen3-4B, Qwen3-8B, and Gemini-2.5-Pro) and four diverse datasets. Our findings suggest that UMR effectiveness may be model-dependent. Whilst preliminary results indicate comparable performance for mid-sized models such as Qwen3-8B, these observations warrant further investigation, particularly regarding the potential applicability to smaller model architectures. Further research is required to establish the generalisability of these findings across different model scales.
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。
