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This survey provides a unified taxonomy for efficient task adaptation in large language models, systematically comparing weight-based fine-tuning, prompt-based learning, and emerging embedding-injection techniques. By clarifying the trade-offs and relationships between these paradigms, it offers researchers a critical roadmap for selecting appropriate adaptation strategies across diverse downstream tasks.
Efficient Task Adaptation in Large Language Models: A Survey of Weight-Based, Prompt-Based, and Embedding-Based Adaptations
arXiv:2610.00928v1 Announce Type: new Abstract: As large language models are increasingly deployed across diverse downstream tasks, efficient task adaptation has emerged as a central challenge. In response, a wide range of task adaptation methods have been proposed, spanning parameter-efficient fine-tuning, in-context learning, and embedding-injection approaches. However, these lines of work have largely evolved within individual paradigms, leaving their cross-paradigm relationships and trade-offs underexplored, especially for recently emerging embedding-based adaptations. This survey presents a unified framework that categorizes task adaptation methods by where and how task information is encoded: model weights, input prompts, or injected task embeddings. We provide a comprehensive taxonomy that integrates these paradigms, analyze their key strengths and limitations to explain how different adaptation paradigms have evolved, clarify relationships across paradigms, and highlight open problems for future research.
Abstract:As large language models are increasingly deployed across diverse downstream tasks, efficient task adaptation has emerged as a central challenge. In response, a wide range of task adaptation methods have been proposed, spanning parameter-efficient fine-tuning, in-context learning, and embedding-injection approaches. However, these lines of work have largely evolved within individual paradigms, leaving their cross-paradigm relationships and trade-offs underexplored, especially for recently emerging embedding-based adaptations. This survey presents a unified framework that categorizes task adaptation methods by where and how task information is encoded: model weights, input prompts, or injected task embeddings. We provide a comprehensive taxonomy that integrates these paradigms, analyze their key strengths and limitations to explain how different adaptation paradigms have evolved, clarify relationships across paradigms, and highlight open problems for future research.
Computation and Language (cs.CL)
arXiv:2610.00928 [cs.CL]
(or arXiv:2610.00928v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.00928 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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