Last Updated on April 30, 2024 1:59 AM IST
In the dynamic world of technology, the waves of innovation such as prompt-tuning and fine-tuning are constantly coming. However, it is important to understand prompt-tuning vs. fine-tuning.
So, today we will embark on this journey and look at prompt-tuning vs. fine-tuning steering. And see how important it is in this revolution.
Together, they are like a brush and palette that craft and enhance the capabilities of model languages such as ChatGPT, Llama 2, Gemini, and much more.
So, let us do a deep dive and look at the differences between them. Let us begin.
Prompt-Tuning vs. Fine-tuning: What is it?
Prompt-tuning is a skill that consists of crafting better user inputs to provide better results from a generative model in a meaningful way.
For instance, if you want to use a GPT model to write a summary of the document, you can provide a prompt that specifies the format along with some key points of the summary. They include:
- Write a summary of the following document in five sentences.
- Include the title and the author of the document.
- Use bullet points to list key factors in the document.
Prompt-tuning does not require any additional data or special training. It is because these models rely on their existing knowledge. It also provides more precise control over the model’s behaviour and outputs.
You are free to adjust the prompt based on their requirements and preferences.
On the other hand, fine-tuning is a popular training technique that allows you to apply new data sets to an existing generative model.
Thus, it helps to improve its performance or adapt it to any particular task or domain. For example, if you want to use a GPT model to write product reviews, you can fine-tune it on a data set of existing product reviews. This will allow it to learn the style, tone, and vocabulary of that particular domain.
Fine-tuning can generate faster and more accurate results. This is because the model can leverage its prior data and also learn from a new data set.
To improve AI system performance for specific tasks, one should consider the following parameters:
- Data quality: The datasets should be specific to effectively address the targeted tasks.
- Training: Fine-tuning is more than just training the model to understand the data context; it is also about directing it towards the best possible outcomes. This requires creating a feedback loop through human evaluations.
Prompt-Tuning vs. Fine-Tuning
Prompt-tuning and fine-tuning are two popular AI optimization techniques. However, it is crucial to understand the key differences between these two fine methods.
The objective
Prompt-tuning focuses on producing desired output, but fine-tuning helps to improve the overall performance of the machine learning model to perform specific tasks.
However, with Appskite you can also learn about the chain-of-thought-prompting technique to enhance the AI responses.
The method
Prompt-tuning is dependent on input dependencies. Thus, you need to be clearer and more precise to achieve greater results.
On the other hand, fine-tuning depends on training a model and integrating new and specific data.
Control
In prompt-tuning, the user has complete control over the results. And, fine-tuning provides the computer with major control to produce the desired outcomes.
Adaptability
Prompt-tuning allows adjustments with immediate effects. This agility allows you to perform real-time modifications without the overhead of retraining.
On the other hand, fine-tuning requires model retraining along with fresh data. However, it is a time-consuming process and immediate modifications are not possible.
Resources
Prompt-tuning does not require any particular resources, as many generative AI applications are freely available over the internet. So, anyone can simply fine-tune their prompts to optimize the results.
While fine-tuning requires substantial resources for training and adding large volumes of data.
Now, you can access Appskite’s prompt engineering cheat sheet for optimizing AI models effectively.
Exploring Fine Tuning Practically with ChatGPT
Let us understand fine-tuning practically with an example. Suppose you are assigned the task of creating a new-year message for your organization. Here is how you can do it easily with prompt-tuning:
This technique shows us that we can guide the model to convey messages in the right direction. Also, the model delivers messages that strongly connect with the audience.
But today’s agenda is “fine-tuning,” which is a complex process that modifies the internal settings of the language model for specific tasks or domains.
It is all about playing with its internal settings so it can excel in particular fields or tasks. In simple terms, it is like tuning your favourite musical instrument to play a specific genre perfectly.
However, it takes additional time, investments, and efforts to achieve amazing results based on your needs.
So, let us understand this with a practical example. Let us turn our model into a culinary expert. Initially, it might only provide simple dishes, but with post-fine-tuning, it will offer you gourmet recipes that are really incredible.
Before fine-tuning:
After fine-tuning:
Use Cases: Prompt-Tuning and Fine-Tuning in GPT
Fine-tuning in LLMs promotes simulating human-like conversations and provides contextually relevant responses from conversational agents like chatbots and much more.
They can accurately classify text sentiment, which helps to facilitate novel tasks such as market research and customer feedback analysis.
In contrast, prompt-tuned LLMs can provide you with precise search results and responses to user queries.
Content creators primarily use prompt-tuning for generating articles, stories, and product descriptions based on specific needs.
Prompt-tuned models also provide accurate results for the questions based on the provided prompts and enhance information retrieval.
In enterprise scenarios, fine-tuning is beneficial as it helps ensure data governance and minimizes the risk of errors in LLM responses.
Enterprise AI teams often combine fine-tuning and prompt-tuning to meet their objectives.
Ultimately, the choice depends on the quality and accessibility of the data. Fine-tuning offers superior results because it allows you to customize models for your specific needs and context.
When Should You Use Prompt-Tuning or Fine-Tuning?
Currently, we are in the digital phase, where large language models (LLMs) along with billions of parameters are becoming more accessible for fine-tuning.
In the following section, we will look at some factors that will help you decide whether to opt for prompt-tuning or invest in fine-tuning.
Firstly, it is important to understand the requirements along with the capabilities of the LLM to which you have access.
For example, let us consider a scenario where you have been asked to develop a customer service Q&A system for your software platform.
Initially, it would be practical to evaluate off-the-shelf LLMs via APIs from providers such as OpenAI, Cohere, and Anthropic. Here are key factors to consider:
Data Alignment
The primary question is: was the LLM trained on data similar to yours? In the context of your Q&A software application example, it will check if the underlying code and documentation are open source.
If so, these LLMs were most likely trained on similar tokens. This implies that they can be effectively prompt-engineered to provide answers and even generate relevant code for your problem.
ML Engineering
Do you or does your organization require expertise in ML engineering and infrastructure?
As fine-tuning becomes more accessible along with user-friendly tools, it still requires expertise and resources for optimal results.
However, if fine-tuning seems challenging due to limited knowledge or budget, then prompt-tuning can be a good solution. However, it also comes with some costs.
Task Uniqueness
How unique is your task? As we know, ample data is available for tasks such as customer service Q&A. Novel or niche tasks may not be adequately represented in public datasets.
For example, developing an LLM for a medical use case requires valid medical responses and communication. However, this could be challenging due to limited data availability.
Thus, in such scenarios, fine-tuning is beneficial.
Summing Up
In the end, prompt-tuning and fine-tuning both, play a crucial role in developing and enhancing generative AI applications.
Ultimately, the decision on which to use should align with your goals, available data, and operational constraints.
Fine-tuning is preferred for enterprise-level AI applications that require high accuracy and deep domain knowledge.
On the other hand, prompt-tuning supports activities that offer an efficient and flexible solution for adapting the fine-tuned model in various scenarios.
Ultimately, take your AI knowledge to the next level with AppsKite and explore the possibilities of AI for superior performance!
F.A.Qs
Are there any potential drawbacks or limitations associated with using prompt-tuning or fine-tuning?
Both prompt-tuning and fine-tuning have their limitations and potential challenges. In prompt-tuning, there might be challenges regarding biases in the generated content. Simultaneously, in fine-tuning, there might be challenges related to data quality and domain specificity. Also, if the dataset quality is not appropriate, it could affect the model’s performance.
Can prompt-tuning and fine-tuning be applied to other AI models?
Yes, both prompt-tuning and fine-tuning techniques can be applied to various AI models, including image recognition and speech synthesis. Prompt-tuning mainly focuses on the model, while fine-tuning improves the performance along with additional data. One can refer to “Transfer Learning in Image Recognition and Speech Synthesis” by Zhang et al. and get help in customization and optimization across various AI domains.
Are there any ethical concerns or risks associated with fine-tuning AI models using sensitive or proprietary data?
Fine-tuning AI models on sensitive data has some concerns related to ethical considerations and risks, including potential privacy breaches, data leaks, and legal implications. There is also a risk of bias or discrimination if the fine-tuned models are trained on biassed datasets. To navigate these challenges, developers and organizations can refer to IBM’s AI ethics and prioritize their ethical assessments, transparency, accountability, and fairness throughout the fine-tuning process.
