Problem
Research questions and friction points this paper is trying to address.
Large Language Models
Task Adaptation
Parameter-Efficient Fine-Tuning
In-Context Learning
Embedding-Based Adaptation
Efficient Task Adaptation in Large Language Models: A Survey of Weight-Based, Prompt-Based, and Embedding-Based Adaptations
🤖 AI Summary
This study addresses the insufficient exploration of relationships and trade-offs among different paradigms for cross-task adaptation of large language models by constructing the first unified analytical framework. Methodologically, it systematically integrates parameter-efficient fine-tuning, in-context learning, and embedding injection techniques, establishing a comprehensive taxonomy along the dimensions of weights, prompts, and embeddings. Employing a systematic review methodology, the work thoroughly analyzes the strengths, limitations, and intrinsic connections of each paradigm. The findings reveal the evolutionary logic underlying these three paradigms, yielding a holistic taxonomic landscape that clarifies their core advantages and constraints while identifying key open problems. Ultimately, this research provides strategic guidance for future investigations into the efficient adaptation of large models.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Task Adaptation
Unified Framework
Parameter-Efficient Fine-Tuning
In-Context Learning
Embedding-Based Adaptation
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