RECENT ADVANCES IN MULTILINGUAL WORD EMBEDDINGS: BREAKING LANGUAGE BARRIERS IN AI
Keywords:
Multilingual Embeddings, Cross-lingual Transfer Learning, BGE M3-Embedding Model, Language Model Architecture, Global Communication SystemsAbstract
This article explores the transformative impact of multilingual embedding models in natural language processing, focusing on their role in revolutionizing cross-cultural communication and linguistic understanding. It examines recent advances in multilingual model architectures, particularly the BGE M3-Embedding model and BGE-Multilingual-Gemma2, highlighting their capabilities in cross-lingual information retrieval and semantic matching. The article discusses the practical applications of these technologies across various sectors, including education, business, and research, while analyzing their contribution to breaking down language barriers in global communication. Additionally, the article investigates future directions in the field, including multimodal integration, domain adaptation, and improvements in handling low-resource languages, providing insights into the evolving landscape of multilingual natural language processing.
References
Rao Ma et al., "Cross-Lingual Transfer Learning for Speech Translation," arXiv:2407.01130 [cs.CL], 13 Oct 2024. [Online]. Available: https://arxiv.org/abs/2407.01130
Akshay Nambi et al., "Breaking Language Barriers with a LEAP: Learning Strategies for Polyglot LLMs," arXiv:2305.17740 [cs.CL], 2023. [Online]. Available: https://arxiv.org/abs/2305.17740
Long Duong et al., "Multilingual Training of Crosslingual Word Embeddings," in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 894–904, Valencia, Spain, April 3-7, 2017. [Online]. Available: https://aclanthology.org/E17-1084.pdf
Zhongtao Miao et al., "Enhancing Cross-lingual Sentence Embedding for Low-resource Languages with Word Alignment," arXiv:2404.02490 [cs.CL], 3 Apr 2024. [Online]. Available: https://arxiv.org/abs/2404.02490
Jianlv Chen et al., "BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation," arXiv:2402.03216 [cs.CL], 28 Jun 2024. [Online]. Available: https://arxiv.org/abs/2402.03216
Chaofan Li et al., "Making Text Embedders Few-Shot Learners," arXiv:2409.15700v1 [cs.IR] 24 Sep 2024. [Online]. Available: https://www.arxiv.org/pdf/2409.15700
Zihao Li et al., "Quantifying Multilingual Performance of Large Language Models Across Languages," arXiv:2404.11553 [cs.CL], 16 Jun 2024. [Online]. Available: https://arxiv.org/abs/2404.11553
Vikas Kumar, "The Rise of Large Language Models: Transforming Business and Technology," LinkedIn, Aug 6, 2024. [Online]. Available: https://www.linkedin.com/pulse/rise-large-language-models-transforming-business-technology-kumar-uc61e
Lingfeng Ming et al., "Marco-LLM: Bridging Languages via Massive Multilingual Training for Cross-Lingual Enhancement," arXiv:2412.04003 [cs.CL], 5 Dec 2024. [Online]. Available: https://arxiv.org/abs/2412.04003
Junguang Jiang et al., "Resource Efficient Domain Adaptation," in MM '20: Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 2220 - 2228. [Online]. Available: https://dl.acm.org/doi/10.1145/3394171.3413701
