Why larger LLMs aren’t always better for enterprise use cases

When it comes to language models, there’s a prevailing belief that bigger is always better. It’s as if the mere size and number of parameters of a language model determines its superiority in understanding and generating human language. But when it comes to enterprise use cases, this isn’t necessarily the case.

Just like you wouldn’t use an oversized wrench for tasks that require fine-tuning and precision, a larger language model may not always be the best fit for every enterprise use case. In this blog post, we’ll explore the potential drawbacks and limitations of larger language models. We’ll also uncover the untapped potential of tailored language models that align with specific enterprise requirements.

Additionally, we’ll discuss the specific considerations that C-suite technology decision-makers should keep in mind when evaluating large learning models (LLMs) for their organization’s unique use cases. By challenging the prevailing belief, our goal is to provide a more nuanced perspective on the role of LLMs in enterprise contexts. We aim to help you make informed choices that align with your specific needs and goals.

Summarized by Writer

• Bigger language models may not be the best fit for every enterprise use case.
• Potential drawbacks and limitations of larger language models include computational cost and resource requirements, diminishing returns on performance improvement, overfitting risks, ethical considerations and biases, and lack of specificity and relevance to specific tasks or communities.
• Custom-built models have advantages such as reduced resource requirements, faster inference and lower latency, quicker deployment and iteration cycles, greater specificity and customization, and enhanced interpretability and explainability.
• C-suite technology decision-makers should consider their business problems, computational resources, cost dynamics, time-to-deploy urgency, and prioritize a user-centric approach when evaluating large language models.
• Purpose-built models can often provide a more efficient and effective solution for specific enterprise needs.
• Download the Writer LLM Vendor Evaluation Checklist to help choose the right solution for the enterprise

A quick word on LLM parameters

In the context of LLMs, a parameter is a fundamental element of the model’s learning architecture. Think of an LLM as a complex network of artificial neurons, structured in a way that mirrors, in a very basic form, how the human brain works. These artificial neurons are connected by links, and each link has an associated weight, which is a numerical value. These weights are the “parameters.”

When an LLM is trained, it goes through a vast amount of text data. During this process, it adjusts the values of these parameters to reduce the difference between its output and the correct output. This process is called “training,” and it involves tweaking these parameters so that the model gets better at making predictions or generating text based on the input it receives.

The number of parameters in an LLM is typically very large, often running into billions or even trillions. Each parameter captures a tiny piece of information about the language — like how words are commonly used together, the structure of sentences, or the subtleties of different text styles.

The limitations of larger language models for enterprise use cases

Large language models typically consist of one billion or more parameters. The sheer size of LLMs can pose several challenges for enterprises that warrant careful consideration. 

1. Computational cost and resource requirements

Training and deploying a large language model can be like fueling a rocket ship. The computational power and resources needed can make it an expensive and often impractical endeavor for many organizations.  It’s a significant investment that not every enterprise can afford. Take, for instance, the training of GPT-3 required at least $5 million worth of GPUs. This highlights the substantial financial investment demanded by these models.

2. Diminishing returns on performance improvement

While LLMs may initially show improved performance, there comes a point where the returns start to diminish. The computational costs increase significantly, but the performance gains become less significant. As Brenden Lake, a computational cognitive scientist  from NYU noted, “It becomes a challenge to find high-quality data, and the energy costs rack up while model performance improves less quickly.”

3. Overfitting risks 

Imagine a model that becomes too fixated on its training data, like a parrot repeating the same phrases over and over. That’s overfitting in a nutshell. Large language models can fall into this trap, making them less adaptable to new or different data.  Factors that contribute to overfitting include high variance and low bias, complex model architecture, and insufficient training data.

To mitigate this, it’s crucial to consider regularization techniques, model simplification, and ensuring an adequate amount of diverse training data. This reduces the risk and enhances the reliability of language models for enterprise use.

4. Ethical considerations and biases

As AI models grow in size, so do the ethical implications. The bigger they get, the more potential there is for biases and unfair outcomes. According to renowned AI ethicist Timnit Gebru, the trend of creating ever-larger AI models amplifies ethical concerns, posing a serious challenge for enterprises that prioritize fairness and inclusivity in their AI applications. These challenges underscore the need for a nuanced approach and careful consideration when integrating larger language models into enterprise workflows.

5. Lack of specificity and relevance to specific tasks or communities

LLMs trained on diverse datasets may not fully meet the specific needs of enterprise use cases. They struggle with common tasks like customer segmentation and lack adaptability to different communities. 

One approach for addressing this challenge is to use a family of models, each designed for specific tasks or communities. By doing this, enterprises can better ensure that the models are tailored to the specific requirements of the tasks or communities they serve.

Why purpose-built language models are better for the enterprise

When it comes to enterprise use cases, purpose-built models have a clear edge over larger learning models. Here are the advantages they bring to the table:

1. Reduced resource requirements

Purpose-built models generally require fewer computational resources, making them more accessible for organizations with limited infrastructure capabilities. Reduced resource requirements contribute to cost-effectiveness in terms of both training and deployment. This cost-effective approach allows enterprises to leverage AI technology without hefty infrastructure investments.

2. Faster inference and lower latency

Purpose-built models often lead to faster inference times, making them suitable for real-time applications where low latency is crucial. This improved responsiveness is particularly valuable in scenarios requiring quick decision-making, such as customer support chatbots or real-time data analysis.

3. Quicker deployment and iteration cycles

The nimbleness of purpose-built models extends to their swift training and deployment, which enables faster iteration cycles. This agility empowers enterprises to adapt and refine their models based on real-world feedback and evolving needs. Rapid iteration is key to staying competitive and ensuring AI models remain up-to-date and effective

For example, a healthcare organization used Writer to cut the writing and review process by 600 hours per month, enabling faster content creation and deployment.

4. Greater specificity and customization 

Purpose-built models can be tailored for specific enterprise use cases, allowing for customization to meet the unique requirements of a given application. This customization ensures that the model addresses the nuances of a particular domain, resulting in more accurate and relevant outputs. 

For instance, Palmyra Med is a model built by Writer specifically to meet the needs of the healthcare industry, providing specialized medical language understanding.

5. Enhanced interpretability and explainability

Purpose-built models with simpler architectures offer enhanced interpretability and explainability, which is crucial for enterprises that require transparency and understanding of the model’s decision-making process. 

These models align with the White House Executive Order on AI, which mandates that LLM companies develop more transparent and measurable models. By using purpose-built language models, organizations can meet regulatory requirements, build trust with stakeholders, and demonstrate their commitment to ethical and responsible AI practices.

Key considerations for C-suite technology decision-makers

As a C-suite technology decision-maker, you hold the key to harnessing the potential of large language models for your organization. By carefully considering these key factors, you can make informed decisions that align with your organization’s specific needs, resources, and goals.

1. Consider your business problem

Assess the specific use case requirements of your organization and determine whether a larger model is necessary or if a purpose-built one can adequately meet your needs. Consider the complexity of the tasks, the domain-specific knowledge required, and the level of customization necessary for optimal performance.

2. Computational resources

Evaluate the available computational resources within your organization. Consider whether your organization has the necessary resources to support the training and operational requirements of an LLM. Purpose-built models are often more resource-efficient, making them a viable choice for organizations with limited infrastructure capabilities.

3. Consider cost dynamics

Weigh the cost implications of using a large language model, including infrastructure costs, training time, and ongoing maintenance. Compare these costs with the potential benefits and value the LLM can provide to your organization. Purpose-built models, with reduced resource requirements, often contribute to cost-effectiveness in terms of both training and deployment.

4. Assess time-to-deploy urgency

Gauge the urgency and timeline for LLM deployment. Large models tend to demand extended training periods and more nuanced fine-tuning. If time is of the essence, a Purpose-built model may offer expedited deployment and iteration cycles. Strike a strategic balance between model size, training duration, and the imperative for swift deployment.

5. Prioritize user-centric approach

Factor in the user experience implications of LLM utilization. Models that lag in performance may fall short in real-time applications,‌ undermining their business value. Additionally, general-purpose models may not match the performance of purpose-built, enterprise-specific LLMs in terms of quality and accuracy.

“Less is more” vs “bigger is better”: evaluate your options for enterprise use

When it comes to large language models in enterprise use cases, the motto “bigger is better” may not be the best approach to take. While larger language models may offer impressive capabilities, they also come with the set of challenges we addressed in this post. Purpose-built models can often provide a more efficient and effective solution for specific enterprise needs.

As you weigh your options, it’s crucial to carefully evaluate your requirements, consider the trade-offs, and make informed decisions. To help you in this process, we’ve created the Writer LLM Vendor Evaluation Checklist. This comprehensive checklist will guide you through the key factors to consider when evaluating LLM vendors, ensuring that you choose the right solution for your enterprise. Download the checklist and embark on your journey to ‌realize the power of LLMs in a way that truly aligns with your organization’s needs and goals.