你确定是正面的吗？通过不确定性量化整合思维链智能体用于方面类别情感分析
Are You Sure You're Positive? Consolidating Chain-of-Thought Agents with Uncertainty Quantification for Aspect-Category Sentiment Analysis
AI总结：
针对新领域标注数据稀缺及监督方法迁移性差的问题，提出结合多个思维链智能体与不确定性量化的技术，在零样本设置中利用大型语言模型提升方面类别情感分析的性能与可迁移性。
摘要：
方面类别情感分析通过识别产品评论中与特定观点相关的特定主题，提供细粒度见解。监督学习方法在该领域占主导地位。然而，新领域的数据稀缺且标注成本高昂。我们认为，在数据集标注所需时间和资源有限的情况下，在零样本设置中利用大型语言模型是有益的。此外，标注偏差可能导致监督方法取得良好结果，但在缺乏标注且要求可重复性的新领域中迁移性较差。在我们的工作中，我们提出了新颖技术，通过利用大型语言模型的令牌级不确定性分数来组合多个思维链智能体。我们使用Llama和Qwen模型的3B和70B+参数大小变体进行实验，展示了这些方法如何满足实际需求，并开启了关于在标签稀缺条件下如何衡量准确性的讨论。
英文摘要：
Aspect-category sentiment analysis provides granular insights by identifying specific themes within product reviews that are associated with particular opinions. Supervised learning approaches dominate the field. However, data is scarce and expensive to annotate for new domains. We argue that leveraging large language models in a zero-shot setting is beneficial where the time and resources required for dataset annotation are limited. Furthermore, annotation bias may lead to strong results using supervised methods but transfer poorly to new domains in contexts that lack annotations and demand reproducibility. In our work, we propose novel techniques that combine multiple chain-of-thought agents by leveraging large language models' token-level uncertainty scores. We experiment with the 3B and 70B+ parameter size variants of Llama and Qwen models, demonstrating how these approaches can fulfil practical needs and opening a discussion on how to gauge accuracy in label-scarce conditions.
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。
