The Power of Many: The Benefits of Multiple LLMs
Large language models (LLMs) are being adopted in a variety of roles, but organisations are missing out on the benefits of multiple LLMs.
The number of organisations implementing Generative AI (GenAI) is ramping up, but they often rely on using a single LLM. This can raise concerns around potential limitations, vulnerabilities, and restrictions. Let’s look at the benefits of using multiple LLMS.
Why use multiple LLMs?
Tailored Solutions: Using multiple LLMs allows for customisation. You can fine-tune models specifically for an industry or task, incorporating specific terminology and nuances.
Improved Accuracy: Using multiple models trained on diverse datasets can reduce bias. Comparing the outputs from different models helps identify inconsistencies and inaccuracies, leading to greater reliability. Multiple LLMs can also verify each other's output, reducing the risk of generating believable but incorrect information.
Enhanced Resilience: Deploying multiple models reduces the risk of system failure. If one model experiences downtime or errors, others can continue to function.
Cost Efficiency: Different models have varying computational requirements. You can optimise costs by using simpler, faster models for routine tasks and more complex models for advanced problems.
Increased Innovation: A diverse selection of models promotes competition as you will benefit from the latest developments in a fast-moving space by incorporating new, specialised models as they become available.
Leveraging Complementary Strengths: You can benefit from combining different types of LLMs, such as general-purpose and specialised models. General-purpose LLMs offer broad knowledge and versatility, while specialised models excel in specific domains or tasks. By employing both types, you can achieve a balance between wide-ranging capabilities and deep, domain-specific expertise. This approach allows for more nuanced and accurate outputs across various applications, from general content creation to highly technical or industry-specific tasks.
While there is much evidence, bear in mind that these mostly revolve around the need to monitor costs and have humans check outputs.
Practical considerations with multiple LLMs
Management Complexity: Integrating and coordinating multiple LLMs requires robust systems for model selection, deployment, monitoring, and updates. In addition, building and managing custom LLMs is a complex and resource-intensive process, requiring significant expertise and investment.
Resource Demands: Running multiple LLMs can be expensive, including costs for hardware, energy consumption, data storage, and potentially model acquisition. The environmental impact, particularly CO2 emissions, should also be considered.
Data Quality and Management: The quality of data used to train LLMs is crucial for their performance. Managing large datasets for training multiple LLMs presents significant challenges, including data collection, cleaning, and storage.
Strategic Implementation: Organisations need a clear strategy when implementing LLMs in an enterprise setting. This often requires expert consultation to align LLM capabilities with business objectives and to navigate the complex landscape of AI technologies.
Enterprise Context Understanding: LLMs need to understand specific enterprise contexts and generate outputs that match the company's tone and style. This requires fine-tuning and potentially the development of custom models tailored to the organisation's unique needs.
The trend towards using multiple LLMs is similar to the multi-cloud strategy adopted by organisations in recent years. This approach offers greater flexibility, resilience, and the ability to use the most suitable tool for each specific task. However, managing multiple models requires additional engineering resources, including robust infrastructure, diverse skill sets, and seamless integration.
Despite these challenges, the benefits of using multiple LLMs outweigh the complexities - so long as they are managed properly, you are stringent about budgets, and you understand the need for humans to check outputs.
As AI becomes more integrated into business processes, the ability to deploy and manage a diverse portfolio of models will be crucial for maintaining competitiveness.
