Cross-Lingual Prompt Transfer via Adaptive Prompt Position Optimization
Keywords:
cross-lingual transfer, prompt tuning, adaptive prompt positioning, multilingual language models, parameter-efficient fine-tuning, system architecture, fairness, sustainabilityAbstract
Large-scale multilingual language models have enabled remarkable zero-shot cross-lingual transfer, yet parameter-efficient adaptation mechanisms such as prompt tuning remain constrained by their reliance on fixed prompt positions. The syntactic diversity, morphological richness, and divergent word order patterns across languages challenge the assumption that a single prompt placement, typically a prefix, can serve all target languages effectively. This paper presents a systems-oriented investigation of adaptive prompt position optimization for cross-lingual prompt transfer. We propose a conceptual architecture in which a lightweight position controller learns to determine the most favorable insertion point for soft prompt embeddings conditioned on language identity, typological features, and task semantics. The work examines structural trade-offs involved in designing the controller, the coordination between a shared multilingual backbone and dynamic prompt injection, and the implications for large-scale deployment. Beyond architectural considerations, we explore governance dimensions such as fairness across resource-disparate languages, robustness to syntactic perturbation, and the alignment with sustainable and equitable multilingual AI policies. By framing prompt positioning as a control problem within a socio-technical infrastructure, the paper provides a forward-looking perspective on how adaptive mechanisms can reconcile computational efficiency with linguistic inclusivity in multilingual natural language processing systems.
References
1. Lester, B., Al-Rfou, R., & Constant, N. (2021). The power of scale for parameter-efficient prompt tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 3045–3059). Association for Computational Linguistics.
2. Li, X. L., & Liang, P. (2021). Prefix-tuning: Optimizing continuous prompts for generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (pp. 4582–4597). Association for Computational Linguistics.
3. Liu, X., Ji, K., Fu, Y., Tam, W., Du, Z., Yang, Z., & Tang, J. (2022). P-tuning v2: Prompt tuning can be comparable to fine-tuning across scales and tasks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (pp. 61–68). Association for Computational Linguistics.
4. Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 8440–8451). Association for Computational Linguistics.
5. Artetxe, M., & Schwenk, H. (2019). Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond. Transactions of the Association for Computational Linguistics, 7, 597–610.
6. Hu, J., Ruder, S., Siddhant, A., Neubig, G., Firat, O., & Johnson, M. (2020). XTREME: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation. In Proceedings of the 37th International Conference on Machine Learning (pp. 4411–4421). PMLR.
7. Zhao, Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021). Calibrate before use: Improving few-shot performance of language models. In Proceedings of the 38th International Conference on Machine Learning (pp. 12697–12706). PMLR.
8. Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., & Singh, S. (2020). AutoPrompt: Eliciting knowledge from language models with automatically generated prompts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 4222–4235). Association for Computational Linguistics.
9. Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., & Miller, A. (2019). Language models as knowledge bases? In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (pp. 2463–2473). Association for Computational Linguistics.
10. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692.
11. Conneau, A., & Lample, G. (2019). Cross-lingual language model pretraining. In Advances in Neural Information Processing Systems 32 (pp. 7059–7069). Curran Associates, Inc.
12. Wang, Z., Lipton, Z. C., & Tsvetkov, Y. (2021). On the interplay between fine-tuning and embedding collapse in multilingual models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 2345–2360). Association for Computational Linguistics.
13. Huang, L., Luo, Z., & Zhao, H. (2022). Cross-lingual continuous prompting for few-shot multilingual natural language understanding. In Findings of the Association for Computational Linguistics: ACL 2022 (pp. 3140–3151). Association for Computational Linguistics.
14. Zhu, W., & Tan, M. (2023, December). SPT: Learning to selectively insert prompts for better prompt tuning. In Proceedings of the 2023 conference on empirical methods in natural language processing (pp. 11862-11878).
15. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
16. Jain, S., & Wallace, B. C. (2019). Attention is not explanation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 3543–3556). Association for Computational Linguistics.
17. Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63.
18. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
19. Ponti, E. M., Vulić, I., Cotterell, R., Parovic, M., Reichart, R., & Korhonen, A. (2021). Language modeling for morphologically rich languages: An empirical survey of strategies for subword tokenization. Transactions of the Association for Computational Linguistics, 9, 879–896.
20. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33–44). ACM.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Artificial Intelligence Engineering and Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
