Sentiment analysis serves as a pivotal component in Natural Language Processing (NLP). Advancements in multilingual pre-trained models such as XLM-R and mT5 have contributed to the increasing interest in cross-lingual sentiment analysis. The recent emergence in Large Language Models (LLM) has significantly advanced general NLP tasks, however, the capability of such LLMs in cross-lingual sentiment analysis has not been fully studied. This work undertakes an empirical analysis to compare the cross-lingual transfer capability of public Small Multilingual Language Models (SMLM) like XLM-R, against English-centric LLMs such as Llama-3, in the context of sentiment analysis across English, Spanish, French and Chinese. Our findings reveal that among public models, SMLMs exhibit superior zero-shot cross-lingual performance relative to LLMs. However, in few-shot cross-lingual settings, public LLMs demonstrate an enhanced adaptive potential. In addition, we observe that proprietary GPT-3.5 and GPT-4 lead in zero-shot cross-lingual capability, but are outpaced by public models in few-shot scenarios.

翻译：情感分析是自然语言处理（NLP）中的关键组成部分。XLM-R和mT5等多语言预训练模型的进展，推动了跨语言情感分析领域日益增长的研究兴趣。近期大语言模型（LLM）的兴起显著推进了通用NLP任务的发展，然而，此类LLM在跨语言情感分析中的能力尚未得到充分研究。本研究通过实证分析，比较了公开的小型多语言语言模型（SMLM）（如XLM-R）与以英语为中心的大语言模型（如Llama-3）在跨英语、西班牙语、法语和中文的情感分析任务中的跨语言迁移能力。我们的研究结果表明，在公开模型中，SMLM相对于LLM展现出更优越的零样本跨语言性能。然而，在少样本跨语言设置下，公开的LLM表现出更强的适应潜力。此外，我们观察到专有的GPT-3.5和GPT-4在零样本跨语言能力上领先，但在少样本场景下被公开模型超越。