跨语言情感分析的模型竞技场：大语言模型时代的比较研究
The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models
AI总结：
本文在英语等多语言情感分析任务中实证比较了小型多语言模型与大语言模型，发现公开SMLM零样本性能更优，而公开LLM及GPT系列在少样本场景下适应力或表现存在差异。
摘要：
情感分析是自然语言处理（NLP）的核心组件。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的研究热潮。近期大语言模型（LLM）的出现显著推进了通用NLP任务，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。本工作进行了实证分析，在英语、西班牙语、法语和中文的情感分析语境下，比较了XLM-R等公开小型多语言模型（SMLM）与Llama-3等以英语为中心的LLM的跨语言迁移能力。研究结果表明，在公开模型中，SMLM相对于LLM展现出更优的零样本跨语言性能。然而，在少样本跨语言设置下，公开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，但在少样本场景下却被公开模型超越。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
