Global Journal of Multidisciplinary and Applied Sciences

Transfer Learning and Generative Modeling for Low-Resource Language Processing: Recent Advances

Jonathan A Smith,

Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

Emily R. Davis

Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

Keywords: Transfer Learning,, Generative Modeling,, Low-Resource Languages,, Natural Language Processing.

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Abstract

The rapid evolution of natural language processing has predominantly benefited a small subset of the worlds languages, leaving the vast majority underrepresented in the digital era. This paper provides a comprehensive analysis of recent advancements in addressing this linguistic inequality through the dual lenses of transfer learning and generative modeling. We systematically explore how cross-lingual transfer mechanisms enable the projection of learned representations from resource-rich domains to low-resource targets, mitigating the fundamental challenge of data sparsity. Furthermore, we investigate the paradigm shift introduced by large generative models, which possess unprecedented capabilities for synthetic data augmentation, zero-shot inference, and few-shot adaptation. By synthesizing theoretical frameworks and empirical observations, we evaluate the efficacy of parameter-efficient fine-tuning techniques, typologically informed transfer strategies, and prompt-based learning methodologies. Our analysis highlights the intersection of linguistic typology and machine learning architectures, demonstrating that structural similarities between source and target languages significantly dictate the success of representation alignment. Finally, we address the critical limitations inherent in current approaches, including the amplification of algorithmic bias, the phenomena of negative transfer, and the challenges associated with the subword tokenization of morphologically rich languages. The insights presented herein aim to guide future research toward more equitable and robust multilingual systems.

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References

Huang, T., Cui, Z., Du, C., & Chiang, C. E. (2025, June). CL-ISR: A Contrastive Learning and Implicit Stance Reasoning Framework for Misleading Text Detection on Social Media. In 2025 6th International Conference on Electronic Communication and Artificial Intelligence (ICECAI) (pp. 610-616). IEEE.

Zhang, J., Chen, C., Chen, X., Yu, H., Xiang, T., Khan, A. S., ... & Adeli, E. (2025). ViBES: A Conversational Agent with Behaviorally-Intelligent 3D Virtual Body. arXiv preprint arXiv:2512.14234.

Fan, D., Zhang, A., Feng, Q., Cai, B., Liu, Y., & Ren, Y. (2021). Group maintenance optimization of subsea Xmas trees with stochastic dependency. Reliability Engineering & System Safety, 209, 107450.

Zhang, W., Zhang, C., Gu, C., Kou, J., Yuan, H., Fang, X., ... & Fang, Y. (2024, October). Hallucination in Large Language Models: From Mechanistic Understanding to Novel Control Frameworks. In 2024 7th International Conference on Universal Village (UV) (pp. 1-36). IEEE.

Zhao, H., Qi, Z., Wang, C., Zheng, Q., Lu, G., Chen, F., ... & Wu, Z. (2025). Dynamictrl: Rethinking the basic structure and the role of text for high-quality human image animation. arXiv preprint arXiv:2503.21246.

Du, C., Chiang, C. E., Huang, T., & Cui, Z. (2025, September). Adaptive Graph Convolution and Semantic-Guided Attention for Multimodal Risk Detection in Social Networks. In 2025 5th International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) (pp. 507-512). IEEE.

Zhou, J., Shuang, K., Wang, Q., Qian, B., & Guo, J. (2025). Bi-directional feature learning-based approach for zero-shot event argument extraction. Information Processing & Management, 62(5), 104199.

Ou, Y., de Bruijn, G. J., & Schulz, P. J. (2025). Social media as an emotional barometer: Bidirectional encoder representations from transformers–long short-term memory sentiment analysis on the evolution of public sentiments during Influenza A on Sina Weibo. Journal of Medical Internet Research, 27, e68205.

Zhang, H., Zhao, S., Zhou, Z., Zhang, W., & Meng, Y. (2025, September). Domain-Specific RAG with Semantic Normalization and Contrastive Feedback for Document Question Answering. In 2025 7th International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI) (pp. 750-753). IEEE.

Cui, Z., Huang, T., Chiang, C. E., & Du, C. (2025, August). Toward verifiable misinformation detection: A multi-tool LLM agent framework. In Proceedings of the 2025 International Conference on Generative Artificial Intelligence for Business (pp. 179-185).

Wang, S., Yu, Y., Feldt, R., & Parthasarathy, D. (2025). Automating a complete software test process using llms: An automotive case study. arXiv preprint arXiv:2502.04008.

Yang, Y., Tang, Y., Lin, D., & Lin, H. (2024). Correlation between building density and myopia for Chinese children: a multi-center and cross-sectional study. Investigative Ophthalmology & Visual Science, 65(7), 157-157.

Ding, H., Fang, Y., Zhu, R., Jiang, X., Zhang, J., Xu, Y., ... & Wang, Y. (2024). 3ds: Decomposed difficulty data selection’s case study on llm medical domain adaptation.

Fan, D., Sun, B., Dui, H., Zhong, J., Wang, Z., Ren, Y., & Wang, Z. (2022). A modified connectivity link addition strategy to improve the resilience of multiplex networks against attacks. Reliability Engineering & System Safety, 221, 108294.

Zhang, Y., Liu, J., Wang, J., Dai, L., Guo, F., & Cai, G. (2025, February). Federated learning for cross-domain data privacy: A distributed approach to secure collaboration. In 2025 8th International Symposium on Big Data and Applied Statistics (ISBDAS) (pp. 824-828). IEEE.

Kong, R., Li, Y., Feng, Q., Wang, W., Ye, X., Ouyang, Y., ... & Liu, Y. (2024, August). SwapMoE: Serving off-the-shelf MoE-based large language models with tunable memory budget. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 6710-6720).

Liang, Z., Wei, W., Zhang, K., & Chen, H. (2025). Research on multi-hop inference optimization of llm based on mquake framework. arXiv preprint arXiv:2509.04770.

Guo, J., Wang, Z., Pu, J., Tian, W., Duan, G., & Luo, G. (2025). Multi-Perspective Dialogue Non-Quota Selection with loss monitoring for dialogue state tracking. Expert Systems with Applications, 283, 127516.

Chen, Y. (2025). The Lexical Bundles and Discourse Markers Between Bilingual and Monolingual Teachers’ Talk: A Corpus-Based Study. Florida Journal of Educational Research, 62(3), 19-31.

Guo, Y., Sekiguchi, Y., Zeng, W., Ebihara, S., Owaki, D., & Hayashibe, M. (2025). Physics-informed learning framework for lower limb kinematic prediction with sparse sensors and its application in chronic stroke. IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., ... & Klamkin, J. (2020). Design and Optimization of Shallow-Angle Grating Coupler for Vertical Emission from Indium Phosphide Devices.

Tu, P., Huang, Y., Zheng, F., He, Z., Cao, L., & Shao, L. (2022, June). Guidedmix-net: Semi-supervised semantic segmentation by using labeled images as reference. In Proceedings of the AAAI conference on artificial intelligence (Vol. 36, No. 2, pp. 2379-2387).

Zhu, R., Jiang, X., Wu, J., Ma, Z., Song, J., Bai, F., ... & He, C. (2025, April). GRAIT: gradient-driven refusal-aware instruction tuning for effective hallucination mitigation. In Findings of the Association for Computational Linguistics: NAACL 2025 (pp. 4006-4021).

Gao, Z., Qu, Y., & Han, Y. (2025). Cross-Lingual Sponsored Search via Dual-Encoder and Graph Neural Networks for Context-Aware Query Translation in Advertising Platforms. arXiv preprint arXiv:2510.22957.

Vuruma, S. K. R., Wu, D., Gupta, S. S., Aust, L., Lookingbill, V., Henry, C., ... & Huang, M. (2024). Utilizing large language models to identify reddit users considering vaping cessation for digital interventions. arXiv preprint arXiv:2404.17607.

Zhu, R., Ma, Z., Wu, J., Gao, J., Wang, J., Lin, D., & He, C. (2025, April). Utilize the flow before stepping into the same river twice: Certainty represented knowledge flow for refusal-aware instruction tuning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 24, pp. 26157-26165).

Li, B., Gu, B., & Ding, Z. (2025). LLM-based Personalized Portfolio Recommender: Integrating Large Language Models and Reinforcement Learning for Intelligent Investment Strategy Optimization. arXiv preprint arXiv:2512.12922.

Zeng, D., Yang, Y., Tang, Y., Zhao, L., Wang, X., Yun, D., ... & Lin, H. (2025). Shaping school for childhood myopia: the association between floor area ratio of school environment and myopia in China. British Journal of Ophthalmology, 109(1), 146-151.

Yifan, O. U. (2018). Participating in Chinese Social Question and Answer Communities: A Case Study of Zhihu. com.

Liu, F., Geng, K., & Chen, F. (2025). Gone with the wind? Impacts of hurricanes on college enrollment and completion. Journal of Environmental Economics and Management, 133, 103203.

Ou, Y., Zhang, P., Yu, J., Li, M., Su, S., Zhang, M., ... & Wu, J. (2025, February). The application of the BERTopic model in natural language processing: In-depth text topic modeling. In 2025 5th International Conference on Consumer Electronics and Computer Engineering (ICCECE) (pp. 793-796). IEEE.

Ma, Y., Qu, D., & Pyrozhenko, M. (2026). Bio-RegNet: A Meta-Homeostatic Bayesian Neural Network Framework Integrating Treg-Inspired Immunoregulation and Autophagic Optimization for Adaptive Community Detection and Stable Intelligence. Biomimetics, 11(1), 48.

Vuruma, S. K. R., Wu, D., Gupta, S. S., Aust, L., Lookingbill, V., Bellamy, W., ... & Huang, M. (2024). Can GPT-4 Help Detect Quit Vaping Intentions? An Exploration of Automatic Data Annotation Approach. arXiv preprint arXiv:2407.00167.

Ahmad, N. R. (2025). Digital marketing strategies and consumer engagement: A comparative study of traditional vs. e-commerce brands. https://doi.org/10.59075/t8pba787

Ahmad, N. R. (2025). Exploring the role of digital technologies in enhancing supply chain efficiency: A case study of e-commerce companies. Indus Journal of Social Sciences, 3(1), 226–237. https://doi.org/10.59075/ijss.v3i1.618