使用大型语言模型评估零样本多语言基于方面的情感分析
Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models
AI总结：
本文在零样本条件下评估大型语言模型在多语言基于方面的情感分析任务中的表现，发现其虽具潜力但不及微调模型，且简单提示策略常优于复杂策略。
摘要：
基于方面的情感分析（ABSA）作为一种序列标注任务，在多语言语境下日益受到关注。以往研究主要集中于为ABSA专门微调或训练模型，我们在零样本条件下评估大型语言模型（LLMs），以探索其在最小化任务特定适应的情况下应对该挑战的潜力。我们对一系列LLMs在多语言ABSA任务上进行了全面实证评估，在九个不同模型中考察了多种提示策略，包括基础零样本、思维链（CoT）、自我改进、自我辩论和自我一致性。结果表明，尽管LLMs在处理多语言ABSA方面显示出潜力，但通常仍不及微调的任务特定模型。值得注意的是，更简单的零样本提示往往优于更复杂的策略，特别是在英语等高资源语言中。这些发现强调，需要进一步改进基于LLM的方法，以有效应对跨多种语言的ABSA任务。
英文摘要：
Aspect-based sentiment analysis (ABSA), a sequence labeling task, has attracted increasing attention in multilingual contexts. While previous research has focused largely on fine-tuning or training models specifically for ABSA, we evaluate large language models (LLMs) under zero-shot conditions to explore their potential to tackle this challenge with minimal task-specific adaptation. We conduct a comprehensive empirical evaluation of a series of LLMs on multilingual ABSA tasks, investigating various prompting strategies, including vanilla zero-shot, chain-of-thought (CoT), self-improvement, self-debate, and self-consistency, across nine different models. Results indicate that while LLMs show promise in handling multilingual ABSA, they generally fall short of fine-tuned, task-specific models. Notably, simpler zero-shot prompts often outperform more complex strategies, especially in high-resource languages like English. These findings underscore the need for further refinement of LLM-based approaches to effectively address ABSA task across diverse languages.
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