Dr. Tahseen Anwer Arshi, Associate Provost for Research and Sustainability and Director of the Center for Innovation and Entrepreneurship, AURAK, conducted the research, ‘Integrating Large Language Models into Entrepreneurial Ventures’.

The study provides actionable insights into the benefits of integrating LLMs into entrepreneurial activities, demonstrating the potential to empower and grow startups in areas like ideation, creativity, new product development, innovation, customer engagement, and service personalization.

Prof. Stephen Wilhite, Senior Vice President of Academic Affairs and Student Success and Provost at AURAK, says: “LLMs are critical to accelerating growth and speeding up processes in enterprises large and small. They push entrepreneurs toward peak performance due to the increased productivity resulting from automating of tasks. Our research delves into how LLMs empower entrepreneurs to harness automation smartly, leaving them to devote themselves to more complex tasks that can only be performed by humans.”

Dr. Arshi explains: “The use of LLMs in entrepreneurship is still developing, with few case studies available. However, promising examples include AI-based content generation start-ups using GPT-4 for marketing and software companies using LLMs for real-time coding. Our study demonstrates that LLMs enhance product innovation by automating tasks and facilitating rapid prototyping. They allow innovation teams to quickly create user interface descriptions, technical documentation, and wireframes, leading to faster stakeholder feedback and supporting agility in start-ups.”

Recent advancements have improved LLM technology further, moving beyond traditional transformer architectures to include innovations such as mixture-of-experts architectures and advanced quantization techniques (4-bit, 8-bit) that reduce memory usage while maintaining performance. Additionally, start-ups are employing hybrid deployment strategies combining edge computing with cloud-based model sharding for better resource efficiency.

The study recommends enhanced AI literacy among entrepreneurs through wider exposure to chatbots, recommendation algorithms and AI-powered analytics. Entrepreneurs should also be provided with hands-on opportunities with no-code AI tools like ChatGPT, Google AutoML, and Zapier AI. Such learning can be facilitated through AI hackathons, mentorship programs, and start-up accelerators. AI peer networks can be created through entrepreneur communities, LinkedIn groups, and local meetups.

The research paper was first presented at the International Conference on Business Management, Entrepreneurship & Circular Economy in November 2024. It is currently in the process of being released in Advances in Science, Technology and Engineering, published by Springer Nature.

The research carries great significance for businesses, since LLMs are widely hailed as key business drivers worldwide. Researchers at the International Institute for Management Development have termed LLMs a “technological tsunami” about to “reshape the global economic landscape”; while researchers at McKinsey estimate generative AI could add the equivalent of $2.6 to $4.4 trillion of value to the world economy annually.

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The surge in popularity of ChatGPT has led to a proliferation of large language models (LLMs), making their evaluation a significant challenge. Due to the multifaceted nature of LLMs, there is no one-size-fits-all approach to assess and select the most suitable models for enterprises. Each LLM has various dimensions to measure, and enterprises have unique priorities based on their specific use cases. Despite these complexities, thorough evaluation remains crucial before adopting any LLM. The following recommendations will outline key factors for evaluating and comparing LLMs, helping you measure and enhance their effectiveness for your organization.

Model Type: General Versus Specific Applicability
For effective comparisons, it is crucial to understand whether the LLM is general-purpose or specific to a given task or context. General-purpose LLMs, like the GPT models from OpenAI, typically support a wide range of generic use cases as they lack specific training for any particular industry, business function, or task. In contrast, domain-specific LLMs are trained or fine-tuned on specialized datasets to develop expertise in particular tasks or domains.

To select the right LLM for their organization, leaders must grasp the common use cases for each model type:

• General-purpose models: These models are generally used for broad natural language understanding and generation tasks, such as content creation and summarization. They often offer greater power and flexibility through prompt engineering (e.g., in-context learning) compared to domain-specific models.
• Domain-specific models: These models are designed for specific domains (horizontal or vertical), organizations, or tasks. They possess deeper knowledge in particular industries or sectors and can be trained to excel in specialized tasks like coding, translation, and document understanding.

Building a comprehensive LLM-powered solution may require multiple models rather than a single LLM. Organizations might need both general-purpose and domain-specific models, or even other types of AI models. These LLMs would assume different roles within the solution and “collaborate” in various ways.

Evaluating Model Capabilities: Benchmarks and Test Cases
Several LLM benchmarks and leaderboards are available, either community-driven or provided by model makers. For general-purpose models, a valuable reference for assessing capabilities is the Large Model Systems Organization’s Chatbot Arena leaderboard. This crowdsourced open platform allows users to rank different models based on their responses to the same questions, without knowing the models’ names. Model makers do not have prior knowledge of all the questions, nor can they train or fine-tune their models specifically to these questions to achieve higher rankings. This makes the leaderboard a useful starting point for evaluating and comparing the general abilities of various models.

When new models are released, model makers typically provide evaluations. If you are focused on a specific capability of a model, task-specific benchmarks can also serve as useful references. However, it is important to note that public LLM benchmarks often suffer from data leakage issues, where evaluation datasets are inadvertently included in the training datasets. This can lead to evaluation results that do not accurately reflect the model’s real-world performance.

In addition to referring to benchmarks and leaderboards, organizations need to develop their own functional test cases aligned with their specific use cases. Start by defining a clear scope and purpose for each use case, as a broader scope of LLM responses can lead to higher risks of undesired behaviors. It is crucial to avoid using LLMs in unsuitable scenarios. Create test cases that closely mirror the LLM usage scenarios in production, utilizing similar or identical data, such as question-and-answer pairs, to ensure the evaluations are as relevant and accurate as possible. The figure below explains the process of LLM evaluation.

After creating test cases, consider what measurements should be taken on the test cases. LLMs vary widely in scope; however, typically, test cases can measure factors such as accuracy, context relevance, safety and other specific metrics to particular use cases, all of which should be prioritized based on business requirements.

Apart from evaluating model capabilities, it is essential to consider nonfunctional factors such as price, speed, IP indemnification, and deployment approaches. These factors are critical for LLM assessment, especially for organizations with regulatory or strict security requirements that necessitate on-premises deployment, thereby limiting model options. Leaders must also weigh trade-offs between features like accuracy, inference cost, inference speed, and context window size to ensure the chosen model aligns with their specific needs and constraints.

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The program adopts Meta’s AI model “LLAMA 3”, targets creative thinkers, future technology entrepreneurs, and founders of startups and established companies specialising in AI applications and uses.

The program aligns with the goals of the Dubai Universal Blueprint for Artificial Intelligence (DUB.AI), that aims to support Dubai’s economic agenda (D33) by adding AED 100 billion through digital transformation and increasing economic productivity by 50% through innovation and digital solutions.

The program seeks to accelerate the achievement of these goals by positioning Dubai as a nurturing environment for AI companies and global talents, since the city is home for eight global tech unicorns.

The business incubator program will foster collaboration and help exchange knowledge and expertise in AI product development. It will enhance innovation, improve the competitiveness of entrepreneurial projects, and develop opportunities and talents. Additionally, it will support the creative initiatives that open new horizons in the digital economy, expected to reach USD 780 billion by 2030.

Abdulaziz AlJaziri, Deputy CEO of Dubai Future Foundation, emphasised the impact of global partnerships in driving AI entrepreneurship ecosystem. “Through such programs, we aim to create new opportunities and provide fertile soil for startups’ rapid growth. This will support the overall innovation system and bring innovative AI solutions and applications to life for the betterment of humanity. It also establishes Dubai’s position as a global testbed and a vibrant environment for AI-based innovation and service development across vital future sectors,” AlJaziri added.

Joelle Awwad, Head of Policy Programs, Africa Middle East and Turkiye, at Meta, stated: “At Meta, we believe that the future of AI development lies in openness and collaboration. Our commitment to working closely with regional partners like Dubai Future Foundation reflects our dedication to building awareness, enhancing capacities, and fostering practical innovation within the ecosystem. Together, we can unlock the true value of AI for both businesses and society.”

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