Multi-Task Learning through Layer-Wise Prompt Allocation in Foundation Language Models
Keywords:
foundation models, multi-task learning, prompt tuning, layer-wise allocation, systems architecture, fairness, sustainabilityAbstract
The proliferation of large-scale foundation models has reshaped the landscape of multi-task learning, enabling a single model to serve diverse language tasks through parameter-efficient adaptation strategies such as prompt tuning. However, conventional prompt-based approaches generally apply prompts uniformly at either the input layer or a fixed set of layers, disregarding the hierarchical feature transformations that occur across the depth of a transformer architecture. This paper introduces a system-oriented perspective on multi-task learning through layer-wise prompt allocation, a framework in which continuous soft prompts are dynamically allocated to different transformer layers according to task-specific demands, representational complexity, and computational constraints. We examine the architectural abstractions, allocation policies, and underlying infrastructure necessary to realize such a system. In doing so, we analyze structural trade-offs related to memory footprint, computational granularity, gradient synchronization, and prompt routing. The paper further discusses governance, fairness, and robustness implications of dynamic prompt allocation in multi-agent or multi-stakeholder environments, where tasks may conflict or exhibit skewed resource consumption. Deployment and sustainability considerations are addressed, including carbon-aware scheduling, life-cycle management of prompt modules, and the tensions between specialization and reusability. By framing layer-wise prompt allocation as an integrated systems design problem rather than a narrow algorithmic optimization, this work illuminates the broader socio-technical dimensions of adaptive multi-task language models and proposes a research agenda that bridges systems engineering, policy design, and responsible artificial intelligence.
References
1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998–6008).
2. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171–4186).
3. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. In Advances in neural information processing systems (pp. 1877–1901).
4. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.
5. 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).
6. 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 (Volume 1: Long Papers) (pp. 4582–4597).
7. Sanh, V., Webson, A., Raffel, C., Bach, S., Sutawika, L., Alyafeai, Z., ... & Rush, A. M. (2022). Multitask prompted training enables zero-shot task generalization. In International Conference on Learning Representations.
8. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems.
9. 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).
10. Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1–35.
11. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., ... & Lowe, R. (2022). Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems.
12. Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., ... & Kaplan, J. (2022). Constitutional AI: Harmlessness from AI feedback. arXiv preprint arXiv:2212.08073.
13. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623).
14. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645–3650).
15. Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L. M., Rothchild, D., ... & Dean, J. (2021). Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350.
16. Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017). Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. In International Conference on Learning Representations.
17. Fedus, W., Zoph, B., & Shazeer, N. (2022). Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. Journal of Machine Learning Research, 23(120), 1–39.
18. Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., ... & Fiedel, N. (2023). PaLM: Scaling language modeling with pathways. Journal of Machine Learning Research, 24(240), 1–113.
19. Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., & Catanzaro, B. (2019). Megatron-LM: Training multi-billion parameter language models using model parallelism. arXiv preprint arXiv:1909.08053.
20. Rajbhandari, S., Rasley, J., Ruwase, O., & He, Y. (2020). ZeRO: Memory optimizations toward training trillion parameter models. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1–16). IEEE.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Data Intelligence and AI 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.
