Model Adaptation Strategies
Model adaptation strategies are techniques for customizing large language model behavior to suit specific tasks and domains. They include prompting, few-shot learning, retrieval-augmented generation, and fine-tuning, each suited to different requirements for knowledge, behavior change, and resource investment.
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Intro
Model Adaptation Strategies
Large language models arrive pre-trained on vast corpora, but production applications rarely use them unchanged. Adaptation can change output format, supply private knowledge, or tune domain-specific behavior. Choosing the appropriate technique is one of the most consequential decisions in an LLM project.
The Adaptation Spectrum
Model adaptation exists on a spectrum from lightweight runtime techniques to permanent model modifications. At one end, prompting requires no changes to model weights and costs nothing to experiment with. At the other, fine-tuning permanently alters model behavior through training. Between these extremes lie hybrid approaches that combine external knowledge with model capabilities.
The choice depends on three factors: what you are changing (knowledge or behavior), how often that content changes, and what resources you can invest. A customer support bot answering questions from a product manual has different requirements than a code generator that must output in a specific style.
Prompting and In-Context Learning
Prompting shapes model behavior entirely through input text. System prompts, user prompts, and few-shot examples all fall under this category. No model weights are modified; everything happens at inference time.
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Sources
- https://www.databricks.com/blog/rag-vs-fine-tuning
Supports
- RAG supplies retrieved knowledge at inference time, while fine-tuning changes a model through additional training.
- https://arxiv.org/html/2406.06399v1
Supports
- Model adaptation methods have different data, compute, evaluation, and maintenance requirements.
- https://arxiv.org/html/2403.01432v3
Supports
- Parameter-efficient fine-tuning methods adapt models by training a small subset of parameters or added modules.
- https://denser.ai/blog/rag-vs-fine-tuning/
Supports
- A practical comparison can use knowledge freshness, behavior change, and operating cost to choose between RAG and fine-tuning.
- https://machinelearningmastery.com/the-machine-learning-practitioners-guide-to-fine-tuning-language-models/
Supports
- Fine-tuning requires task-specific training data, model evaluation, and ongoing maintenance.
- https://arxiv.org/html/2408.01346v1
Supports
- LLM adaptation includes prompting, retrieval, full fine-tuning, and parameter-efficient alternatives.