Prompt Engineering Vs finetuning

Limitations of prompt engineering

Prompt engineering involves crafting specific input prompts to guide a language model's output. While it can be an effective way to improve model performance, there are some limitations due to number of examples in a prompt due to token size:

1. Token size: As you add more data examples to the prompt, the token size increases. Large Language Models have a maximum token limit, which can restrict the number of examples you can provide in a single prompt. For example, if you want to use a pre-trained language model to generate product descriptions, you may be limited by the maximum token limit when crafting your prompts.

2. Cost: With increased token size, the computational cost of processing the input also increases. This can lead to higher expenses when using cloud-based language models, as they often charge based on the number of tokens processed. For example, if you are using a cloud-based language model to generate responses to customer inquiries, the cost may increase as you add more examples to your prompts.

3. Latency: As the token size and computational cost increase, the latency of the model also increases. This means that it may take longer for the model to generate a response, which can be problematic in real-time applications such as chatbots or voice assistants. Additionally, if the model is being used in a time-sensitive application, such as stock trading, the increased latency can lead to missed opportunities or incorrect decisions.

4. Overfitting: Prompt engineering can also lead to overfitting, where the model becomes too specialized to the specific prompts provided and may not generalize well to new data. This can be mitigated by using a diverse set of prompts and regularly updating them, but it is still a potential limitation to consider.

5. Human bias: The prompts provided by humans may contain biases that are inadvertently incorporated into the model's output. For example, if the prompts are written by a team with a specific cultural or linguistic background, the model may generate biased or inaccurate responses for users from different backgrounds. This can be addressed by using diverse prompts and incorporating bias detection and mitigation techniques into the model training process.

6. Accuracy: Accuracy of the Large Language Model depends on number of examples provided in the prompt. But you can only provide few examples in a single prompt. If you have a big dataset, you cannot use all the data in one prompt due to token size limitation. This reduces the overall accuracy and effectiveness of the model. To overcome this limitation, you may need to finetune the Large Language Model where you can provide as many as examples you want for increasing the accuracy.

7. Need for Larger Language Models: When you have few prompts, you will be opting for larger Language Models to improve the performance. This will increase the latency, cost, and resource requirements for using the model.

Finetuning

Finetuning is the process of training a pre-trained Large Language Model on a specific dataset or task for a limited number of epochs. This helps the model learn the nuances of the task and improve its performance on that specific task. For example, let's say you have a pre-trained language model that has been trained on a large corpus of text. You can finetune this model on a dataset of movie reviews to create a sentiment analysis model that performs well on movie reviews.

Quote

But the most important trend is that the whole setting of training a neural network from scratch on some target task is quickly becoming outdated due to finetuning, especially with the emergence of foundation models like GPT.

- Andrej Karpathy, Ex.Director of AI at Tesla

Why Finetuning

Finetuning offers several benefits over prompt engineering:

• Ability to use Large Dataset

Finetuning allows you to use a larger dataset for training, which can lead to better model performance. For example, if you want to train a language model to generate product descriptions, you can finetune the model on a large dataset of product descriptions to create a more accurate model.

• Performance

Finetuned models can achieve higher performance on specific tasks compared to models that rely solely on prompt engineering. For example, if you want to create a chatbot that can answer customer inquiries, finetuning a language model on a dataset of customer inquiries can lead to better performance than relying solely on prompt engineering.

• Cost Reduction

By finetuning the model on a specific dataset, you can achieve better performance with fewer tokens, which can lead to reduced computational costs when using cloud-based large language models. This is because the model is more likely to generate accurate and relevant output without the need for lengthy prompts with many data examples, reducing the number of tokens processed and the associated costs.

• Latency

Finetuning can also lead to reduced latency compared to prompt engineering, as the model has been trained on a specific task and is more likely to generate relevant output without the need for lengthy prompts. This will help in providing better user experience.

• Smaller Language Models Perform Better

When doing finetuning with a large dataset for a specific task, smaller models can perform in par with the larger language models. This leads to reduced computational costs and faster inference times, making it more practical for real-time applications. This improves the cost and latency benefits further.

• Generalization

Finetuning can help the model generalize better to new, unseen data, as it has been trained on a more diverse set of examples. For example, if you want to train a language model to summarize news articles, finetuning the model on a dataset of news articles can help the model generalize to new, unseen articles.

• Control

Finetuning a model allows for better control over the model's behavior and output. By training the model on a specific dataset, you can guide the model to generate responses that are more relevant and appropriate for the task at hand. For example, if you want to create a language model that generates politically neutral responses, you can finetune the model on a dataset of politically neutral text to achieve better control

Prompt Engineering Vs finetuning comparison

Aspect	Prompt Engineering	Finetuning
Cost	Can be higher due to increased token size	Lower, as token size is not a constraint
Performance	Limited by the quality of the prompt and token size	Can achieve higher performance with larger datasets
Dataset size	Restricted by token limit	Can handle larger datasets
Latency	Can be higher due to increased token size	Lower, as token size is not a constraint
Generalization	Limited by the diversity of prompts	Can generalize better to new, unseen data
Control	Limited control over model behavior and output	Better control over model behavior and output
Overfitting	Can lead to overfitting	Can mitigate overfitting by using a diverse set of examples
Accuracy	Limited by the number of examples provided in the prompt	Can achieve higher accuracy with larger datasets
Resource requirements	Requires larger language models	Smaller models can perform in par with larger models

Which one to choose?

The choice between prompt engineering and finetuning depends on your specific use case and requirements:

1. Prompt Engineering: Choose prompt engineering if you have a small dataset or need a quick solution without investing in additional training. For example, if you want to create a simple question-answering system using a pre-trained language model, you can craft a prompt like "Question: What is the capital of France? Answer: " and let the model complete the answer.

2. Finetuning: Choose finetuning if you have a larger dataset and want to achieve higher performance on a specific task. For example, if you want to create a sentiment analysis model for movie reviews, finetuning the model on a large dataset of labeled movie reviews will likely yield better results than relying on prompt engineering alone.

In summary, prompt engineering can be effective for improving model performance with limited datasets, but it has limitations due to token size and can lead to overfitting. Finetuning, on the other hand, allows for better performance with larger datasets, reduced costs and latency, better generalization, and more control over the model's behavior and output. So, choose the one that suits your needs the best!