PEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts
This paper proposes PEML, a parameter-efficient multi-task learning method that co-optimizes continuous prompts and low-rank model adaptation, achieving up to 6.67% average accuracy improvement on GLUE, SuperGLUE, MMLU, and commonsense reasoning benchmarks.
[2605.14055] PEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts
[Submitted on 13 May 2026]
Title:PEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts
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Abstract:Parameter-Efficient Fine-Tuning (PEFT) is widely used for adapting Large Language Models (LLMs) for various tasks. Recently, there has been an increasing demand for fine-tuning a single LLM for multiple tasks because it requires overall less data for fine-tuning thanks to the common features shared among tasks. More importantly, LLMs are resource demanding and deploying a single model for multiple tasks facilitates resource consolidation and consumes significantly less resources compared to deploying individual large model for each task. Existing PEFT methods like LoRA and Prefix Tuning are designed to adapt LLMs to a specific task. LoRA and its variation focus on aligning the model itself for tasks, overlooking the importance of prompt tuning in multi-task learning while Prefix Tuning only adopts a simple architecture to optimize prompts, which limits the adaption capabilities for multi-task. To enable efficient fine-tuning for multi-task learning, it is important to co-optimize prompt optimization and model adaptation. In this work, we propose a Parameter-Efficient Multi-task Learning (\PM), which employs a neural architecture engineering method for optimizing the continuous prompts while also performing low-rank adaption for model weights. We prototype PEML by creating an automated framework for optimizing the continuous prompts and adapting model weights. We evaluate PEML against state-of-the-arts multi-task learning methods MTL-LoRA, MultiLoRa, C-Poly, and MoE, on the GLUE, SuperGLUE, Massive Multitask Language Understanding, and commonsense reasoning benchmarks. The evaluation results present an average accuracy improvement of up to 6.67%, with individual tasks showing peak gains of up to 10.75%.
Comments: 26 pages, 8 figures, 18 Tables
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.14055 [cs.CL]
(or arXiv:2605.14055v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.14055
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Anjir Ahmed Chowdhury [view email] [v1] Wed, 13 May 2026 19:25:56 UTC (1,579 KB)
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