Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models

• URL: http://arxiv.org/abs/2412.12564v2
• Date: Tue, 24 Dec 2024 16:41:40 GMT
• Title: Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models
• Authors: Chengyan Wu, Bolei Ma, Zheyu Zhang, Ningyuan Deng, Yanqing He, Yun Xue,
• Abstract summary: We evaluate large language models (LLMs) under zero-shot conditions to explore their potential to tackle ABSA task. We investigate various prompting strategies, including vanilla zero-shot, chain-of-thought (CoT), self-improvement, self-debate, and self-consistency. Results indicate that while LLMs show promise in handling multilingual ABSA, they generally fall short of fine-tuned, task-specific models.
• Score: 0.9832963381777073
• License:
• Abstract: 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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