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A Parameter-Efficient Transfer Learning Approach through Multitask Prompt Distillation and Decomposition for Clinical NLP

arXiv:2604.06650v1 Announce Type: new Abstract: Existing prompt-based fine-tuning methods typically learn task-specific prompts independently, imposing significant computing and storage overhead at scale when deploying multiple clinical natural language processing (NLP) systems. We present a multitask prompt distillation and decomposition framework that learns a single shared metaprompt from 21 diverse clinical source tasks and adapts it to unseen target tasks with fewer than 0.05% trainable parameters. Evaluated across five clinical NLP task types (named entity recognition, relation extraction, question answering, natural language inference, and summarization) on 10 held-out target datasets using three backbone models (LLaMA 3.1 8B, Meditron3 8B, gpt-oss 20B), our framework consistently outperforms LoRA by 1.5~1.7% despite using orders of magnitude fewer parameters, and exceeds single-task prompt tuning by 6.1~6.6%. The gpt-oss 20B model achieves the highest overall performance, part

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Cheng Peng, Mengxian Lyu, Ziyi Chen, Yonghui Wu · · 1 min read · 21 views
#cs.CL #cs.AI

arXiv:2604.06650v1 Announce Type: new Abstract: Existing prompt-based fine-tuning methods typically learn task-specific prompts independently, imposing significant computing and storage overhead at scale when deploying multiple clinical natural language processing (NLP) systems. We present a multitask prompt distillation and decomposition framework that learns a single shared metaprompt from 21 diverse clinical source tasks and adapts it to unseen target tasks with fewer than 0.05% trainable parameters. Evaluated across five clinical NLP task types (named entity recognition, relation extraction, question answering, natural language inference, and summarization) on 10 held-out target datasets using three backbone models (LLaMA 3.1 8B, Meditron3 8B, gpt-oss 20B), our framework consistently outperforms LoRA by 1.5~1.7% despite using orders of magnitude fewer parameters, and exceeds single-task prompt tuning by 6.1~6.6%. The gpt-oss 20B model achieves the highest overall performance, particularly on clinical reasoning tasks. The strong zero- and few-shot performance demonstrates better transferability of the shared prompt representation.

Executive Summary

This article introduces a novel multitask prompt distillation and decomposition framework designed for parameter-efficient transfer learning in clinical Natural Language Processing (NLP). By learning a single shared 'metaprompt' from 21 diverse clinical source tasks, the framework significantly reduces computational and storage overhead compared to traditional task-specific prompt tuning or LoRA. The approach achieves superior performance across five clinical NLP task types and 10 held-out datasets, utilizing fewer than 0.05% trainable parameters. Its strong zero- and few-shot capabilities demonstrate enhanced transferability, particularly when integrated with larger backbone models like gpt-oss 20B, making it a highly efficient solution for deploying scalable clinical NLP systems.

Key Points

  • ▸ Introduces a multitask prompt distillation and decomposition framework for parameter-efficient clinical NLP.
  • ▸ Learns a single shared 'metaprompt' from 21 diverse clinical source tasks, significantly reducing trainable parameters (<0.05%).
  • ▸ Outperforms LoRA by 1.5-1.7% and single-task prompt tuning by 6.1-6.6% across various clinical NLP tasks.
  • ▸ Evaluated on five task types (NER, RE, QA, NLI, Summarization) and 10 datasets using LLaMA 3.1 8B, Meditron3 8B, and gpt-oss 20B.
  • ▸ Demonstrates superior zero- and few-shot performance, indicating enhanced transferability of the shared prompt representation.

Merits

Exceptional Parameter Efficiency

The framework achieves remarkable performance with fewer than 0.05% trainable parameters, addressing a critical bottleneck in deploying large-scale NLP systems in resource-constrained clinical environments.

Broad Task and Model Generalizability

Demonstrates consistent superiority across a wide array of clinical NLP tasks, diverse datasets, and multiple large language model backbones, underscoring its robustness and versatility.

Enhanced Transferability

The strong zero- and few-shot performance highlights the effectiveness of the distilled metaprompt in transferring knowledge to unseen tasks, reducing the need for extensive task-specific fine-tuning data.

Addresses Scalability Challenges

By learning a single shared metaprompt, the method directly tackles the computing and storage overhead associated with independently learned task-specific prompts, enabling more efficient deployment.

Demerits

Dependence on Source Task Diversity

The quality and diversity of the 21 source tasks are crucial for the effectiveness of the distilled metaprompt. A lack of representativeness might limit generalization to highly novel target tasks.

Black-Box Nature of Prompt Decomposition

While effective, the internal mechanisms of how prompt decomposition optimally captures and transfers knowledge might lack interpretability, making debugging or targeted improvement challenging.

Computational Cost of Initial Metaprompt Training

Training the initial shared metaprompt across 21 source tasks could still be computationally intensive, potentially posing an initial barrier for smaller research groups or institutions, despite subsequent efficiency gains.

Expert Commentary

This paper presents a significant advancement in the field of clinical NLP, addressing the critical challenge of scalability and resource efficiency. The 'metaprompt' concept, leveraging multitask distillation and decomposition, is intellectually elegant and empirically robust. Its ability to achieve superior performance with orders of magnitude fewer trainable parameters than existing methods like LoRA or single-task prompt tuning is a compelling demonstration of ingenuity. The comprehensive evaluation across diverse tasks, datasets, and backbone models lends substantial credibility to the findings. Particularly noteworthy is the framework's capacity for strong zero- and few-shot learning, which is paramount in data-scarce clinical domains. This work moves beyond incremental improvements, offering a paradigm shift towards truly generalizable and deployable clinical NLP systems. Future research should perhaps delve into the interpretability of the decomposed prompt components and explore mechanisms for continuous adaptation of the metaprompt as new clinical tasks emerge.

Recommendations

  • ✓ Further investigate the interpretability of the decomposed prompt components to understand how specific clinical knowledge is encoded and transferred.
  • ✓ Explore methods for dynamically updating or refining the shared metaprompt with new source tasks to ensure continuous adaptation and relevance.
  • ✓ Conduct a detailed analysis of the computational resources (time, energy) required for the initial metaprompt training phase compared to traditional methods.
  • ✓ Evaluate the framework's resilience to concept drift in clinical data over time, particularly in the context of evolving medical knowledge and terminology.

Sources

Original: arXiv - cs.CL

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