Open Source LLMs: Pros and Cons for your Organization Adoption

With the emergence of large language models (LLMs), the support industry has undergone a paradigm shift. LLMs’ exceptional abilities in text generation, summarization, language translation, and answering complex queries have revolutionized how businesses interact with customers.

While adopting this advanced technology is a pressing need for industries to gain a strategic advantage, it’s crucial to consider the significant cost of leveraging LLMs.

This is where open-source LLMs come into play. They are an irresistible alternative for organizations seeking to harness this technology.

But wait!

Before you make such a crucial decision, let’s explore open-source LLMs and their advantages and limitations.

What are Open-source LLMs?

An open-source LLM is free for public use; you can customize it as per their requirements.

Let me explain this to you: You’re in need of an enterprise search solution to enhance the user and agent experience, but you also require LLM to enhance the search functionalities.

And the best part? You can achieve this with open-source LLM, even without the burden of license fees. So deploy the open-source LLM on your platform and fine-tune it to match your needs perfectly.

The most prominent example of open-source LLM is OpenAI’s GPT series of models.

Open the Door to Innovation: Pros of Open-Source LLMs

There are various advantages to businesses adopting open-source LLMs.

Transparency & Flexibility

Open-source LLMs offer transparency, which is an essential aspect for businesses. You can inspect their inner workings, including training data, and algorithms and fine-tune models to your business’s unique needs.

This enables businesses to:

• Trust the model more, as they can see how it delivered the particular results.
• Ensure the model aligns with ethical and legal guidelines.
• Adapt the model to their specific requirements and data.

Collaboration and Innovation

Open-source LLMs have created a collaboration and knowledge-sharing culture in the AI community. These models are available to all developers, providing access to their design, data, and pre-trained versions.

This allows researchers and developers from various industries to contribute. Different contributors have diverse perspectives and skills and may identify unique problems or new possibilities that the original developers hadn’t considered.

For instance, you deployed an open-source LLM but encountered challenges with the model’s performance in a specific language. However, someone from the AI community identified this issue and worked on improving the model’s performance.

They might even develop a new method for reducing bias or create tools to make the model more accessible to non-technical users.

This type of collaboration speeds up progress and can advance text generation and language understanding.

Cost Savings

One major consideration for businesses while adopting LLM is cost. Licensing fees can be a significant financial burden over time, especially if you require large-scale deployment. But as mentioned above, open-source models eliminate licensing fees, allowing wider adoption and democratizing access to this powerful technology.

These models are advantageous for small businesses with limited budgets or who want to allocate more resources to other business areas instead of technology.

Customization and Adaptation

Who doesn’t want flexibility and customization? Everyone does! Open-source LLMs offer it and adapt to specific use cases and domains.

Developers can customize pre-trained models by fine-tuning their parameters. Additionally, they can train them using a domain-specific dataset to perform particular tasks. This improves the models’ performance and effectiveness, resulting in enhanced user experience.

It means that developers can customize pre-trained models by adjusting their settings and training them further with specific data related to their particular field or application.

This makes the models more effective and better suited to the developers’ unique needs, resulting in better performance and user experience.

The Dark Side of Openness: Cons in Open-Source LLMs

Open source is a double-edged sword, offering various benefits but also having some limitations that shouldn’t be overlooked.

Hallucination

One common concern while leveraging open-source LLMs is hallucination. This means that LLM generates responses that look realistic but are factually inaccurate. It exalts the risk of spreading misinformation and exposing confidential information.

However, the risks don’t end there. Intentionally manipulating training data with false or biased information is a real possibility.

The real challenge lies in detecting such manipulation, particularly in large datasets. This difficulty can lead to the generation of incorrect, unfair, or discriminatory outcomes by open-source LLMs. This can significantly impact decision-making and potentially damage an organization’s reputation.

Hidden Costs

While open-source LLMs may eliminate licensing fees, their successful implementation often requires a multifaceted technical team. Therefore, you might need to invest in hiring experts in machine learning, data science, and software engineering.

Additionally, community support can exist, but the organization will have responsibility for maintaining, updating, and ensuring compatibility of these LLMs with the existing system. It may not be feasible for all organizations.

Data security and Privacy

Open-source LLMs often train on massive datasets, including sensitive information like financial or medical details. These LLMs can potentially expose such information as query responses, raising concerns about data privacy and security.

Imagine a scenario where you need to analyze a client’s complex tax situation. You leverage the open-source LLM integrated into your organization’s system and feed the client’s income, deductions, and strategies.

It provided valuable insights, but there’s a catch: LLMs retain traces of training data to learn and improve. If someone feeds a similar query, an LLM can unknowingly reveal the client’s sensitive financial information.

This is a disturbing revelation about open-source LLMs, as data leakage can have legal implications and put your organization’s reputation at risk.

Intellectual Property Issues

So you know open-source LLMs are prone to data security risks, but there’s more! These LLMs can encounter legal issues related to intellectual property (IP) rights, licensing, and usage restrictions.

For instance, developers create an open-source language model for translating text using a mix of public and proprietary data. A startup integrates this model into a commercial language learning app.

However, issues arise over ownership of the model. Who has rights to proprietary data, and how can the app be monetized under the open-source license? Therefore, clear agreements on ownership and data use are crucial. They avoid legal disputes and ensure fair access to the technology.

Scalability

Open-source LLMs are powerful, yet they face challenges regarding scalability. These models require significant computational resources to train and leverage in real-time.

Imagine a picture where only 10k users were interacting at first, and then the number of users interacting with this model increased to 25k. As the user size goes up, the demand for resources like memory and processing power also rises significantly. This can result in slower response time, performance bottlenecks, or even system failure.

Making the Right Choice

Now that you have an idea of open-source LLMs’ pros and cons, it’s time to make an informed decision about adoption. Still in doubt? Then, ask these questions before proceeding:

If open-source LLMs aren’t the perfect fit for your needs, no worries! Small Language Models (SLMs) can help!

Small Language Models (SLMs) offer a promising alternative to open-source LLMs. They are renowned for their efficiency, speed, and ability to operate on lower computing resources. Their advantages over open-source LLMs include easy deployment, cost-effective maintenance, and optimization for specific tasks.

Takeaway

Open-source LLMs offer a compelling path for organizations seeking to leverage LLM technology without the burden of hefty licensing fees. However, it’s not a one-size-fits-all solution.

Therefore, evaluating your resources, technical capabilities, and specific needs is crucial before making the switch.

LLMs can be powerful tools for businesses. Hence, choose and implement them strategically to gain a competitive edge and unlock new possibilities in the ever-evolving landscape of AI.

If you need expertise assistance, we can help!