LLM Customization: Advantages and Techniques

A recent study published by Iopex revealed that almost up to 67% of organizations globally utilize generative AI models that leverage LLM technology to effectively generate content using human language. [1]

Over the past decade, various companies including Google and OpenAI have released numerous pre-trained large language models capable of facilitating most generic needs of any business. However, these models are not quite effective in knowledge-specific domains like healthcare and finance.

That’s where large language model (LLM) customization comes in. By customizing available LLMs, organizations can better leverage the LLMs’ natural language processing capabilities to optimize workflows, derive insights, and create personalized solutions. Ultimately, LLM customization can provide an organization with the tools it needs to gain a competitive edge in the market.

This article will explore the various large language model customization techniques and explore some of the biggest benefits of customizing large language models.

What is LLM customization?

Customizing an LLM typically involves tailoring a pre-trained LLM to suit a particular application or industry, thus enhancing its performance and context awareness in the relevant field. During the process, data scientists and developers leverage specialization and fine-tuning methods to refine the models’ capabilities based on knowledge and domain-specific data. [2]

While the steps and methodologies applied during LLM customization may differ slightly across organizations based on project requirements, there is an overwhelming consistency in the measures applied when customizing a large language model. The steps applied in the process include:

Data Collection

Like other AI models, large language models rely on their training data to understand and generate content. As such, to create a custom LLM, you first have to collect vast amounts of high-quality, domain-specific datasets.

When properly applied, these datasets serve as the foundation for training the model to not only perform better but also generate more accurate results in the desired context.

LLM Model Selection

Not all LLM models are built the same. Some models like ChatGPT are designed to understand and generate human-like text, while more advanced models like Gemini have multimodal capabilities that make them uniquely suitable for applications that may involve both text and image analysis.

As such, it is vital to carefully select an appropriate LLM to use as the base model in the customization process. In that regard, it is advisable to select more versatile models that offer impressive versatility and performance in numerous tasks and applications.

LLM Fine-Tuning

The fine-tuning phase involves training the model on domain-specific datasets. During this stage, developers and data scientists meticulously adjust the model’s parameters to align with the target purpose while retaining its linguistic knowledge.

Read more: Fine-Tuning LLMs. Benefits, Costs, Challenges

Hyperparameter Tuning

Hyperparameters control the structure, performance, and functions of LLMs. Therefore, hyperparameter tuning involves tuning model performance for optimal results. In hyperparameter tuning, data scientists experiment with various hyperparameters like batch size, learning rate, and regularization. [3]

Domain Knowledge Integration

The fine-tuning process alone cannot guarantee model performance on domain-specific tasks. To improve performance and contextual awareness in the necessary field, LLMs are typically imposed with domain-specific concepts, vocabulary, and rules. Ultimately, this helps align the model with industry norms, thus making it more dependable.

Guideline Adherence

When customizing large language models for utilization in highly regulated industries, it is vital to incorporate the various domain regulations and guidelines into the model’s training data. This way, the model is better able to generate compliant content that meets industry standards.

Tone and Style Alignment

Organizations don’t just require a model that can generate relevant content. They also need the content to align with the organization’s tone and style.

Validation and Testing

After the customization process is completed, the model has to be thoroughly evaluated using validation and test datasets. This ensures that the model performs as expected and meets project requirements.

Iterative Refinement

Results obtained from the validation and testing phase, as well as user feedback, may suggest that the model needs further refinement. The iterations employed during the refinement process may include everything from strategic adjustments to additional rounds of fine-tuning.

LLM customization techniques

Large language models customization is a crucial aspect of model deployment in domain-specific applications. The techniques applied are meant to tailor the model’s output to align with the desired context. Depending on project requirements and objectives, organizations can use any of the following techniques when customizing models:

Prompt Engineering

Prompt engineering typically involves customizing the model at inference time with show-and-tell examples. When applied, it can help design inputs that produce optimal outputs.

In this method, the LLM is provided with prompt examples and completions. Data scientists then prepend detailed instructions to a new prompt, allowing the model to generate the desired output. It is important to note that prompt engineering does not involve changing the model’s parameters.

Some of the most common approaches employed in prompt engineering include:

• Few-shot prompting

The few-shot prompting approach in prompt engineering involves prepending several sample prompts and completion pairs to a prompt. This way, the model is better able to generate appropriate responses for an unseen prompt. [4]

One of the greatest benefits of utilizing this approach is it does not require fine-tuning and requires a relatively smaller amount of data compared to other techniques employed in customizing LLMs. Unfortunately, it adds to inference latency, which increases the time it takes the model to generate an output after receiving a prompt.

System Prompting

In the system prompting approach, data scientists add a system-level prompt to the users’ prompt to provide specific and detailed instructions. This way, the model is better able to produce the intended results.

On the downside, the specificity and quality of the system prompt applied can have a profound impact on the accuracy and relevance of the model’s responses, thus necessitating the need to carefully evaluate the prompt before integrating it into the model.

• Chain-of-thought reasoning

Chain-of-thought reasoning presents a unique approach to prompt engineering. It involves training the model to break down complex problems into simpler steps, thus improving model performance on multi-step tasks. As such, this approach works well for arithmetic, logical, and deductive reasoning tasks.

Parameter-Efficient Fine-Tuning (Peft)

The PEFT technique utilizes clever optimizations to selectively add or update several parameters or layers to the original model architecture. This way, data scientists can train various model parameters for specific use cases.

Additionally, the pre-trained model weights are kept frozen, and the data scientists only need to update a few parameters using domain-specific datasets, thus improving the model’s accuracy on domain-specific tasks without negatively impacting its performance.

Prompt Learning

Prompt learning offers an efficient way to customize LLMs by making it possible to use pre-trained models on various downstream tasks without having to tune the model’s full set of parameters. Essentially, it enables data scientists to add new tasks to the LLM without disrupting or overwriting any previous tasks on which the model has already been pre-trained.

This approach also mitigates the issue of catastrophic forgetting that is often encountered in the fine-tuning process. The issue occurs when the model learns new behaviors during the fine-tuning process at the cost of the foundational knowledge acquired during pretraining. Therefore, by freezing the original model parameters, prompt learning effectively prevents catastrophic forgetting.

• Prompt tuning

In pre-trained large language models, all soft prompt embeddings are computed as a 2D matrix with the size total_virtual_tokens Xhidden_size. As such, each task that the LLM is prompt-tuned to perform is assigned a unique 2D embedding matrix. Additionally, the tasks don’t share any parameters during inference or training.

• P-tuning

In the p-tuning variation approach, data scientists use an MLP or LSTM model called a prompt_encorder to predict virtual token embeddings. At the start of the process, the prompt_encorder embeddings are randomly initialized, and the base LLM parameters are frozen. As such, only the prompt_encorder weights are updated at each step during training.

When the process is completed, prompt-tuned virtual tokens from the prompt encoder are automatically moved to the prompt_table. The prompt_encorder is then removed from the model, facilitating the preservation of previously p-tuned fine prompts while retaining the ability to add new prompt-tuned or p-tuned soft prompts in the future.

Fine-Tuning

Fine-tuning is one of the best methods for customizing LLMs. It helps achieve higher accuracy when compared to PEFT and prompt engineering techniques. On the downside, it requires significant computational resources and high-quality data. Some of the most notable techniques employed in fine-tuning when customizing LLMs include:

• Supervised fine-tuning (SFT)

The SFT approach involves fine-tuning all the LLM’s parameters on labeled input and output data, enabling data scientists to teach the model hard-to-follow user-specific instructions and domain-specific terms. SFT is also referred to as instruction tuning since it involves fine-tuning models on a collection of datasets described through instructions.

• Reinforcement learning with human feedback (RLHF)

When applied correctly, reinforcement learning with human feedback can enable a model to achieve better alignment with human preferences and values. The technique uses reinforcement learning to enable the model to modify its behavior based on the feedback it receives.

RLHF involves a three-stage fine-tuning process that utilizes human feedback as the loss function. The RLHF process utilizes the SFT approach described above as the first stage. The second stage involves training the SFT model as a reward model.

In the final stage, the RLHF process focuses on fine-tuning the initial policy model against the reward model by employing a proximal policy optimization (PPO) algorithm for reinforcement learning.

Read more: What is fine-tuning in NLP?

What are the advantages of customizing LLMs?

LLMs have completely changed how businesses undertake important operations. When leveraged correctly, LLMs can enable businesses to process and analyze huge amounts of textual data through their advanced algorithms.

For instance, LLMs can provide numerous benefits in marketing by generating ideas, drafting personalized responses to improve email marketing, and monitoring customer data, including sentiment analysis and engagement patterns.

Similarly, the healthcare sector can benefit immensely from LLM capabilities in extracting insights from medical records, clinical texts, and notes, enabling faster and more accurate diagnosis.

LLMs can also improve operations and enhance medical training. According to recent studies, the global AI market in the healthcare sector is predicted to reach $45.2 billion by 2026, up from $5 billion in 2020. [5]

Final thoughts

LLMs are poised to revolutionize how businesses operate. They can facilitate seamless content creation, data analysis, research and development, and much more. However, general LLMs don’t perform quite well when it comes to domain-specific tasks that require in-depth knowledge and linguistic attributes.

Customizing LLMs enables organizations to tailor them to the organization’s goals. For instance, customized LLMs in the health sector can facilitate quicker and more accurate diagnosis as well as improve professional development and training.

The method you choose to employ when customizing an LLM comes down to several factors, including project requirements and objectives, computational resources, and the model in question. When properly utilized, a customized LLM can align with organizational goals, thus helping to streamline processes.

References

[1] iopex.ai. The Growing Adoption of LLMs in Production in the Enterprise. URL: https://tiny.pl/d9hbm. Accessed on April 11, 2024
[2] Github.blog. Customizing and fine-tuning LLMs: What You Need to Know. URL: https://github.blog/2024-02-28-customizing-and-fine-tuning-llms-what-you-need-to-know/,Accessed on April 11, 2024
[3] AWS.amazon.com. What is Hyperparameter Tuning? URL: https://tiny.pl/d9hbj. Accessed on April 11, 2024
[4] Promptingguide.ai. Few-Shot Prompting. URL:
https://www.promptingguide.ai/techniques/fewshot. Accessed on April 11, 2024
[5] Forbes.com. The Current State Of The Healthcare AI Revolution, URL: https://www.forbes.com/sites/forbestechcouncil/2021/04/28/the-current-state-of-the-healthcare-ai-revolution/?sh=1e470b7b2980,Accessed on April 11, 2024