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Prompt: Difference between revisions
→Prompting vs. Fine-tuning
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==Prompting vs. Fine-tuning== | ==Prompting vs. Fine-tuning== | ||
Prompting and [[Fine-tuning]] represent two different ways to leverage [[large language models]] (LLMs) like [[GPT-4]]. | Prompting and [[Fine-tuning]] represent two different ways to leverage [[large language models]] (LLMs) like [[GPT-4]]. | ||
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Conversely, prompting is the technique of providing specific instructions to an LLM to guide its responses. It doesn't necessitate model retraining for each new prompt or data change, and thus, offers a quicker iterative process. Importantly, it doesn't require a labeled dataset, making it a viable option when training data is scant or absent. Prompting can be an excellent starting point for solving tasks, especially simpler ones, as it can be resource-friendly and computationally efficient. | Conversely, prompting is the technique of providing specific instructions to an LLM to guide its responses. It doesn't necessitate model retraining for each new prompt or data change, and thus, offers a quicker iterative process. Importantly, it doesn't require a labeled dataset, making it a viable option when training data is scant or absent. Prompting can be an excellent starting point for solving tasks, especially simpler ones, as it can be resource-friendly and computationally efficient. | ||
[[File:prompting_vs_finetuning1.png|400px]] | |||
Despite its advantages, prompting may underperform compared to fine-tuning for complex tasks. There's a clear trade-off in terms of [[inference]] costs. Fine-tuned models, by integrating task-specific knowledge into the model's parameters, can generate accurate responses with minimal explicit instructions or prompts, making them cheaper in the long run. In contrast, prompted models, which rely heavily on explicit instructions, can be resource-intensive and more expensive, particularly for large-scale applications. Therefore, the choice between fine-tuning and prompting will depend on the specific use case, data availability, task complexity, and computational resources. | Despite its advantages, prompting may underperform compared to fine-tuning for complex tasks. There's a clear trade-off in terms of [[inference]] costs. Fine-tuned models, by integrating task-specific knowledge into the model's parameters, can generate accurate responses with minimal explicit instructions or prompts, making them cheaper in the long run. In contrast, prompted models, which rely heavily on explicit instructions, can be resource-intensive and more expensive, particularly for large-scale applications. Therefore, the choice between fine-tuning and prompting will depend on the specific use case, data availability, task complexity, and computational resources. | ||