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Prompt Tuning vs. Fine-Tuning—Differences, Best Practices and Use Cases

Your Guide to Generative AI Infrastructure

LLMs are characterized by their substantial number of parameters, which are the internal variables the model uses to make predictions and generate text. These parameters range from millions to billions, enabling the models to process and produce language with remarkable accuracy and fluency.

Building an LLM involves exposing the model to vast datasets to learn patterns, associations, and language structures. This process, known as pre-training, equips the model with a broad understanding of language.  You can then adapt the LLM further for specific tasks or applications, ranging from text completion and translation to question answering and content generation.

Fine-tuning and prompt tuning are two approaches to customizing LLMs. Both methods leverage the vast knowledge of LLMs so they can be reused in different contexts, but they use different strategies.

This article explores the key differences between the methods and the use cases for both.      

Summary of prompt tuning vs. fine-tuning

Concept Prompt tuning Fine-tuning
Process Adds a layer of adjustable vectors(or soft prompts) that the model interprets along with the input text. Involves fine-tuning an LLM’s parameters with a new target dataset.
Parameter adjustment Keeps the model’s parameters frozen and only adjusts soft prompts. Adjusts the parameters of the pre-trained model.
Input format Rewrites the input by adding natural language prompts. Maintains the original input format.
Resource intensity More efficient, leveraging existing model parameters without extensive retraining. Typically requires more computational resources for retraining.
Objective alignment Aligns the task with the model’s pre-training (e.g., MLM) objective through modified inputs. Directly optimizes the model for the specific task.

Prompt tuning 

Prompt tuning enhances the capabilities of large language models (LLMs) by employing soft prompts—adjustable vectors that are optimized and integrated alongside input text to guide the model’s responses. This approach maintains the model’s pre-existing parameters in a frozen state, leveraging the pre-trained knowledge without altering the core architecture. Soft prompts are dynamic and continuously optimized during training to align the model’s output with specific task objectives.

Key components in prompt tuning include:

  • In-context demonstration: This involves providing the LLM with examples that show the expected input-output format. For instance, a soft prompt for a SQL query might include a counting question paired with its SQL command, teaching the model the desired response format.
  • Train examples: These are broader and cover various scenarios, helping the model generalize across different contexts.
  • Verbalizers: These act as translators between the model’s outputs and task-specific categories, ensuring precise alignment. For example, a verbalizer in sentiment analysis would categorize the output “happy” as a positive sentiment.

For example, in an LLM-powered chatbot, soft prompts could include typical customer queries with ideal responses. Verbalizers would classify the tone of the inquiry, aiding the chatbot in adjusting its response tone.

Soft tunable prompt embeddings are prepended to a retrieved in-context demonstration, which is followed by the training example. (Source)

Soft tunable prompt embeddings are prepended to a retrieved in-context demonstration, which is followed by the training example. (Source)

The gradient flow in the above image plays a crucial role by adjusting the soft prompts based on performance feedback. As the model produces outputs, it learns from deviations between its responses and the desired outcomes. The error gradient from this mismatch is used to refine the soft prompts, enhancing the model’s accuracy over time.

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Implementation

Load your LLM. Let’s use GPT-2 as an example.

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

Design a series of prompts encapsulating the tasks you want the model to perform, using task-relevant language and structure. Use a tokenizer to convert your prompts into a model-compatible format. Please note – the steps described occur under the hood when we send prompts to LLM, so we are doing this only if we are prompt-tuning our own model or an open-source model.

 # Define a prompt (instructions given to the model)
prompt = "The capital of France is"

# Encode the prompt into model input tokens
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")

Initialize and concatenate soft prompts to your inputs.

# Generate soft prompts
soft_prompt = model.generate_soft_prompts(num_tokens=50)
# Concatenate soft prompts with the original input tokens
inputs = torch.cat([soft_prompt, inputs], dim=-1)

The LLM is then passed in the input with the soft prompt embeddings. The model is not restrained, and the weights have not been updated.

 outputs = model(inputs)

*Note that any provided code is simplified and will require more steps in production

Fine-tuning

This process fine-tunes the parameters of an LLM on a new dataset to adapt it for a specific task. For example, fine-tuning LLM BERT on a smaller dataset of movie reviews and adjusting its parameters to classify sentiments as positive or negative. The main idea behind fine-tuning is to reduce the time and data required to develop high-performing models for specific tasks. Instead of pre-training a new LLM from scratch (out of scope and budget for most organizations), you can tailor it with minimal adjustments for a particular problem. 

Once you have prepared your dataset, start fine-tuning with a lower learning rate to avoid overwriting the pre-learned features too quickly.

The process of fine-tuning a model (Source)

The process of fine-tuning a model (Source)

Implementation

Load your LLM. This example uses GPT-2

model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

Prepare your task-specific dataset in a format compatible with the LLM. This involves tasks like text tokenization, resizing images, or encoding labels.

# Replace this with your own data loading and preprocessing
train_dataset = ...  # Your training dataset
eval_dataset = ...   # Your evaluation dataset

Next, you can alter the model’s hyperparameters or incorporate task-specific layers to better suit particular tasks. For example, you can add a specialized output layer for a classification task or fine-tune the learning rate. Typically, a lower learning rate makes small weight adjustments without drastically altering the learned features. You can also use Low-Rank Adaptation (LoRA), which inserts a smaller number of new weights into the model.

# Initialize the Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)
# Fine-tune the model
trainer.train()
# Save the fine-tuned model
trainer.save_model("./gpt2-fine
‘tuned")

*Note that any provided code is simplified and will require more steps in production

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Prompt tuning vs. fine-tuning

Initially, a pre-trained large language model (LLM) comes off the rack, designed to fit many use cases broadly but none perfectly. Fine-tuning and prompt tuning are commonly used methods to customize the LLM for your specific task or use case. 

Process summarized

Fine-tuning is the process of adjusting an underlying model by updating the weights of its pre-trained network so it can better generalize to the specific context. During fine-tuning, models receive additional training data that consists of labeled examples of the desired future output of the large language model. This process recalibrates the model’s existing knowledge, enhancing its ability to interpret and respond to the nuances of the task at hand. 

In contrast, prompt tuning introduces tunable embeddings alongside the prompts, essentially adding a layer of adjustable vectors that the model interprets along with the input text. The tunable embeddings are similar to giving the model a set of adjustable lenses through which it views the input prompts, thereby sharpening its focus and improving its predictions.

Parameter adjustments

Fine-tuning adjusts the language model’s parameters. It employs a specialized dataset to recalibrate the model’s internal weights. This precise tailoring allows the model to fine-tune its outputs, providing a custom fit for the designated application.

In contrast, prompt tuning preserves the foundational weights and adapts the model to new tasks through crafted inputs and mappings. The method adjusts only the prompts, thereby steering the model’s output in the desired direction while leaving its core architecture unchanged.

Input format

The input format mirrors the LLM pre-training phase, ensuring the model receives data in a recognizable structure. However, the content of these inputs is task-specific for the new context. Conversely, prompt tuning redefines the input presentation. It embeds natural language prompts within the inputs to provide additional context to guide the model to best complete the specified task.

Resource intensity

Fine-tuning and prompt tuning aim to solve the same problem and are often similar in performance, but prompt tuning is more efficient and, therefore, less costly. This is due to the lack of need to source and label additional data sets. 

Challenges

Fine-tuning LLMs can be resource-intensive, requiring significant computational power and time. Acquiring high-quality, task-specific data is challenging. Despite best efforts, the model may overfit the small, task-specific dataset, reducing its generalization ability.

In contrast, effective prompts in prompt tuning require striking a balance between specificity and generality. Even after embeddings are created, generalizing adaptations across tasks poses challenges, especially when transitioning between different domains or programming languages. The choice of verbalizer is crucial for accurate model output alignment.

Real-world examples – prompt tuning vs. fine-tuning

We give more examples below.

Task Fine-tuning approach Prompt tuning Approach Preference
Code summarization Adapts the model by training on pairs of code and their summaries. Enhances summarization by crafting prompts that highlight key functionalities. Prompt tuning may provide more accurate summaries by directly leveraging the model’s pre-trained knowledge with specific prompts.
Code translation Retrains the model on bilingual code datasets for accurate translations. Uses prompts to provide context and guide the model in translating code between languages Prompt tuning potentially offers more precise translations by adding contextual knowledge through prompts, making it highly effective for translating nuanced code.
Emotion detection in text Modifies the model to recognize and categorize emotional cues within large, diverse text datasets. Applies prompts that cue the model to identify keywords and phrases indicative of emotional tone. Fine-tuning is preferred to deeply integrate the subtleties and complexities of emotional expression into the model’s understanding.
Medical diagnosis from patient records Trains the model on medical datasets to understand and diagnose based on complex medical histories. Uses prompts that mimic medical questioning to elicit diagnostic information. Fine-tuning is the choice here due to the need for in-depth learning of medical terminologies and decision-making processes, which goes beyond the scope of prompt tuning.

When to choose prompt tuning vs. fine-tuning

Opt for fine-tuning when your task necessitates an in-depth, customized approach, especially if it deviates significantly from the scenarios encountered during the model’s initial training. It shines when there is a wealth of specific data you can leverage to fine-tune model outputs to a high degree of accuracy. 

Fine-tuning is also the preferred method when peak performance is a non-negotiable aspect of the task, particularly in domains that require a deep understanding of specialized knowledge and terminology, such as legal or medical fields. Lastly, if ample computational resources allow for the training and deployment of large models, fine-tuning is an excellent choice to tailor the model precisely for the task’s demands.

In contrast, prompt tuning offers an efficient means of adaptation when the goal is to maintain the model’s broad understanding across various tasks. It suits projects requiring quick iteration and flexibility, where rapid prototyping is more valuable than customization. It provides a simpler and more controlled method for making nuanced adjustments, mitigating the risk of overfitting or forgetting previously learned information.

Prompt tuning is also ideal when operating under limited computational resources or when fine-tuning presents prohibitive costs or time constraints. It’s also well-suited for situations with scarce task-specific data; prompt tuning can creatively direct the model to perform adequately using whatever data is available. 

Best practices in prompt tuning and fine-tuning

Prompt tuning requires an understanding of the specific task domain and the judicious use of verbalizers for optimal adaptation. The construction of soft prompts and the precision of verbalizers hinge on the availability of high-quality, domain-specific data. Similarly, the preparation and quality of the new dataset play pivotal roles in fine-tuning. 

Nexla, a platform that automates data engineering for generative AI, can significantly support both processes. 

Nexla enhances the fine-tuning process through its unique concept of Nexsets—data products that streamline the integration, transformation, delivery, and monitoring of data. You can use Nexsets to ensure that the data used for fine-tuning is not only of high quality but also perfectly aligned with the specific nuances and requirements of the task at hand. 

Similarly, Nexla’s automated data pipelines deliver a steady and accurate flow of training data, which is crucial for prompt effectiveness and verbalizer accuracy. Nexla’s platform also excels in facilitating the iterative nature of prompt tuning. The platform allows for feedback from ongoing model training to be incorporated back into the system, enabling adjustments to data pipelines. This feedback loop supports the continuous enhancement of model accuracy.

Lastly, frequently evaluate your LLM model on a validation set to monitor its performance and adjust your training strategy accordingly

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Conclusion

Fine-tuning and prompt tuning are two powerful methods for adapting pre-trained models to tackle specific tasks with increased accuracy and efficiency. While fine-tuning offers deep customization by adjusting a model’s entire weight structure, prompt tuning allows for a more agile approach, tuning only the inputs to the model. Both have their place in the AI toolkit, and the choice between them depends on the specific needs and constraints of the task.

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