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

Chengyan Wu, Bolei Ma, Zheyu Zhang, Ningyuan Deng, Yanqing He +1 more
12/17/2024
cs.CL

No official code found. Best implementation: swaggy66/eval-llm-mabsa (0 stars, last commit 2025-07-03).

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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Code Implementations(1)

swaggy66/eval-llm-mabsaCommunity100%

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

01PythonNov 30, 20241 years ago

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Cite this paper

@article{wu2024evaluating,
  title  = {Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models},
  author = {Chengyan Wu and Bolei Ma and Zheyu Zhang and Ningyuan Deng and Yanqing He and Yun Xue},
  year   = {2024},
  eprint = {2412.12564},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.12564v3}
}

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