The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models

Xiliang Zhu, Shayna Gardiner, Tere Roldán, David Rossouw

Abstract
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.
Anthology ID:
2024.wassa-1.12
Volume:
Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Orphée De Clercq, Valentin Barriere, Jeremy Barnes, Roman Klinger, João Sedoc, Shabnam Tafreshi
Venues:
WASSA | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
141–152
Language:
URL:
https://aclanthology.org/2024.wassa-1.12
DOI:
10.18653/v1/2024.wassa-1.12
Bibkey:
Cite (ACL):
Xiliang Zhu, Shayna Gardiner, Tere Roldán, and David Rossouw. 2024. The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models. In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 141–152, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models (Zhu et al., WASSA-WS 2024)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.wassa-1.12.pdf
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