تقرير
The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models
| العنوان: | The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models |
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| المؤلفون: | Zhu, Xiliang, Gardiner, Shayna, Roldán, Tere, Rossouw, David |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language |
| الوصف: | 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. Comment: Accepted to WASSA workshop at ACL2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.19358 |
| رقم الأكسشن: | edsarx.2406.19358 |
| قاعدة البيانات: | arXiv |
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