Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915357035331584 |
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| author | Wu, Chengyan Ma, Bolei Zhang, Zheyu Deng, Ningyuan He, Yanqing Xue, Yun |
| author_facet | Wu, Chengyan Ma, Bolei Zhang, Zheyu Deng, Ningyuan He, Yanqing Xue, Yun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12564 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models Wu, Chengyan Ma, Bolei Zhang, Zheyu Deng, Ningyuan He, Yanqing Xue, Yun Computation and Language 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. |
| title | Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.12564 |