The use of Generative AI (Gen AI) has accelerated at a rapid rate, making a noticeable impact across industries. In addition, Gen AI has quickly become a favorite among enterprises given its potential to drive innovation, efficiency, and personalized solutions.
Against the background of Artificial Intelligence, customer satisfaction has emerged as a prime motivator of innovation. Customers are demanding more and more features, and in this competitive market, organizations using AI technology are showing enhanced performance and better productivity than before.
The year 2022 marked a major transformation in the digital landscape that led to a spurt of start-ups diving into the field of Gen AI. GAI has metamorphized from creating not just human-like text, but also realistic images and videos, music, art, etc. showing remarkable potential across various domains. Continue reading the blog to explore more on Gen AI, Gen AI models and key strategies to fine-tune the models to maximize performance.
Generative AI operates on the principles of learning from vast amounts of data to create new content, whether it is text, images, or other media. It makes thorough use of machine learning models, like neural networks, to make sense of patterns and structures within the data. During training, the AI balances its parameters to reduce errors, enhancing its potential to generate realistic outputs. Fine-tuning includes refining these models on specific datasets to enhance performance in particular tasks. This process helps the AI produce more accurate and relevant results, making it a powerful tool for various applications across industries.
It is also essential to understand that pre-trained models have evolved from the earlier AI-trained models on fixed data sets. The Gen AI pre-trained models have had a vast dataset to work with, including not just similarities but also anomalies, which have given them a wider experience and exposure to a range of information. These pre-trained AI models have now acquired in-depth knowledge, the nuances, and subtle differences that exist in the information present. This has enabled these models to overcome the restrictions of single narrow tasks and can now specialize in creative content. Additionally, Gen AI has now started creating its new data instances. The way organizations can use these new instances can significantly enhance effectiveness, boost productivity, and lead to smarter decision-making.
The primary concept of Gen AI refers to unsupervised and semi-supervised machine learning algorithms allowing computers to utilize existing data in the form of text, images, audio, or video files to create newer and innovative outputs. But before we dive into understanding ways to fine-tune these models, let’s take a quick look at what Gen AI models are and their types.
Mentioned below are the popular Gen AI models
1. Generative Adversarial Networks or GANs
GANs refer tosophisticated deep learning architectures comprising two main components: the generator and the discriminator. The generator creates synthetic data that replicates the original data, while the discriminator distinguishes between genuine and generated data. With adversarial training, the generator continually improves the realistic aspect of its outputs, while the discriminator becomes better at identifying authentic data from synthetic.
As a powerful generative model, GANs are widely used in deep learning to generate samples that enhance data augmentation and preprocessing techniques. They have diverse applications, such as image processing and biomedicine, where they generate high-quality synthetic data for research and analysis.
2. Variational Autoencoders or VAEs
VAEs are powerful Gen AI models that blend the potential of autoencoders with probabilistic modeling to gain compact data representation. VAEs operate by encoding input data into a lower-dimensional latent space, allowing the production of new samples by extracting points from the learned distribution. Their uniqueness makes them useful in several fields, such as image generation, data compression, anomaly detection, and more.
3. Diffusion Model
Generative diffusion models can deliver new data based on the information they were trained on. For example, a diffusion model trained on a collection of human faces can generate new, realistic faces with a variety of features and expressions, even the ones do not present in the original dataset. The core concept of diffusion models is to convert a simple, easily accessible distribution into a more complex and meaningful one. This conversion is achieved through a sequence of reversible steps. Once the model grasps this transformation process, it can generate new samples by starting from a simple distribution and gradually moving towards the desired, complex data distribution.
Fine-Tuning Gen AI Models: The Secret to Better Performance
To derive the full potential of the GAI models, fine-tuning these pre-trained models is essential. When tailored to fit as per the specific domain and aligned for the specific requirement output, GAI can deliver amazing results.
Fine-tuning GAI models is making the pre-trained models more refined for specific tasks as pertinent to the domain it is being trained to deliver exact outputs. For example, if we take an existing database of specific diseases and train the model on that large database of symptoms across a broad spectrum covering geographical areas and cross-community patients, we could end up with a random output. But, to get to a specific output, using certain specific labeled data with prompt engineering queries can help narrow and drill down to specific results.
As we understand, pre-trained models are often exposed to large datasets of information and have a wide understanding of different patterns and structures found in the data. Thus, these pre-trained models are adaptive and flexible.
However, with time, these models can get creative too and that’s when GAI becomes a critical tool to business innovations. These pre-trained GAI models can now not only reflect the details of the original data but can adjust to new and unseen patterns. Besides they can now understand the context within which they are needed to respond and deliver much more than standard responses or results.
Thus, fine tuning can be defined as taking a pre-trained model and making it more proficient for a specific task or dataset.
So, the dilemma often is when to use prompt engineering and when to use the fine-tuning approach. For that, we need to understand the subtle differences between the two, as it depends on the specific task and resources of datasets available in that domain. The following table details the key differences between the two.
| Prompt Engineering | Fine-tuning |
| Prompt engineering depends on the queries posed but the LLMs can still deliver irrelevant output | For better accuracy and quality, fine-tuning helps deliver a relevant response |
| Prompt engineering focuses on eliciting better output for users | Fine-tuning focuses on enhancing model performance on certain tasks |
| Prompt Engineering aims to improve output by creating more detailed and effective inputs | Fine-tuning involves training a model on new data to improve knowledge in specific areas |
| Depends on designing effective prompts to guide pre-trained models with minimal computational resources | Offers a balance between customization and efficiency but requires higher computational resources at some stages during the process |
Once we have the nuances as to when to use and how to use prompt engineering and the fine-tuning approach, our next step is to understand what strategies are required. Thus, to understand what strategies to use when fine-tuning GAI models to maximize performance, we first need to understand the steps involved in fine-tuning pre-trained models.
First step: Pre-trained Models
Pre-trained models are trained in large datasets that cover a broad range of information. So, it has what is called general knowledge but no specific drilled-down information that can be optimized for any specific tasks
Second Step: Specific Training data
For the specific task at hand to get the specific desired narrowed down output, the model is further trained on specific data that is labeled. Like giving the surgeon and dentist their own specific tools to work on to either pull out an organ or tooth respectively. This step may require more computational power at this stage
Third Step: Aligning and Refinement of the Dataset
In this step, adjustments and alignment of the parameters are done to fine-tune the desired output, so that the model can align itself and better perform the specific task required.
Before diving into the strategies employed, understanding the advantages and challenges of using the fine-tuning approach is critical for business innovation and productivity.
| Advantages | Challenges |
| Efficient: Does not need to train the model from scratch | Dependent: The base pre-trained model must be quality perfect and have relevant data |
| Speedy: achieves results faster as already has basic information | Overfit: If trained on too small a dataset, later may not work well on new parameters being introduced |
| Accurate: Target-oriented and hence accurate for specific tasks | Limited capability: Its scope is limited as it is based on the basic pre-trained model and difficult to start with the fundamental basics |
Thus, we see that the fine-tuning approach allows models to learn domain-specific features, mitigate biases, and generate outputs tailored to requirements
Here are 5 strategies we propose to fine-tune generative AI models effectively to help maximize performance for product innovation and productivity.
Targeted Data Augmentation
Augmenting the training data with relevant examples can improve model generalization and robustness. By incorporating additional data samples or applying transformations such as rotation, scaling, or cropping, the model learns to generate diverse outputs while preserving the essential characteristics of the dataset.
Transfer Learning
In transfer learning, we leverage pre-trained models to expedite training on a new task with the gained knowledge. When we initialize the generative model with weights learned from a pre-trained model, we make fine-tuning more efficient, as fewer iterations are needed to adapt to the target dataset. Especially when working with limited data or computational resources, this approach is effective.
Architectural Modifications
This is a very critical strategy when it comes to modifying the generative model’s architecture. Complex patterns can be detected when we experiment with network layers sizes, functions and regularization techniques. This tends to produce accurate outputs and more realistic samples. For example, defrosting certain sections of the model layers helps to retain certain pre-trained parts of the model and gives scope to modification especially when catering to specific parts that need more focus in later stages.
Adversarial Training
Continued adversarial training helps improve the stability and convergence of generative models. By adjusting the balance between the generator and discriminator networks and fine-tuning their hyperparameters, such as learning rates and optimization algorithms, the model can achieve better equilibrium and generate outputs of higher quality.
Evaluation and Feedback Loop
Regular evaluation of generated samples and soliciting feedback from domain experts are integral parts of the fine-tuning process. Analyzing metrics such as perceptual similarity, diversity, and novelty helps assess the model’s performance objectively. Incorporating user feedback enables iterative refinement, guiding the model towards generating outputs that meet specific criteria and user expectations.
Fine-tuning pre-set models is a game-changing method for improving creative AI uses. Fine-tuning GAI models is a very delicate process that requires a combination of domain knowledge, experimentation, and iterative refinement. By employing strategies such as targeted data augmentation, transfer learning, architectural modifications, adversarial training, and continuous evaluation, developers can enhance the performance and versatility of their models. Mastering the art of fine-tuning will be instrumental in unlocking the full potential of generative models across diverse applications. Calsoft with over 25 years of experience in technology and one of the early adopters of Gen AI, supports and enables organizations to adapt to market dynamics delivering seamless experiences by bridging the gap between traditional and innovation. Our Gen AI services provide Product Development & User Experience Design; Testing & Quality Engineering; and integrations and Plugin Development to Integrate Products and Platforms with Generative AI Tools. Our developers are trained to make use of the might of pre-set models and modify them to suit our client’s unique application needs, making us an ally to deliver a 360-degree approach to Gen AI-based requirements.
