Choosing the Right LLM for Enterprises

As of 2024, it is clear that there are hundreds of LLMs available, with new ones being designed and issued on a consistent basis. Given the vast number of Large Language Models (LLMs) available, it might be difficult to find one that meets your unique needs. The sector is rapidly developing, with new models and improved versions appearing every week. As a result, any list of LLMs and their applications rapidly becomes obsolete. Optimizing costs while sustaining performance is crucial for the sustainable use of LLMs. As reported by AIM and other media outlets, OpenAI incurs approximately $700,000 daily to operate ChatGPT.

Choosing the best LLM is a challenging problem for businesses owing to a variety of variables. Diverse business demands imply that an LLM that works for one organisation may not work for another, and data privacy and security are critical, especially when dealing with sensitive information. Customisation requirements add another degree of complexity, since businesses frequently demand LLMs that are tailored to their unique needs, which may be resource-intensive. Cost issues are important, as the creation, deployment, and maintenance of LLMs are costly endeavours. Scalability is also important; as organisations develop, their LLM requirements change, necessitating models that scale properly. Ethical concerns and regulatory compliance are critical for maintaining confidence and avoiding legal difficulties, yet integration obstacles occur when LLMs must be effectively integrated into current systems and workflows. The quick rate of AI breakthroughs necessitates that organisations constantly adapt to new technologies, and a skills gap frequently arises, with many firms lacking the in-house competence to successfully evaluate, deploy, and manage sophisticated learning machines.

1. LyRise

Industry: Talent Matching

Deployment: LyRise, a talent-matching startup, uses a chatbot built on Llama that interacts like a human recruiter. This chatbot helps businesses find and hire top AI and data talent from a pool of high-quality profiles in Africa across various industries.

Reasons for success:
Customisation: The chatbot is designed to comprehend and match unique job needs with candidate profiles, streamlining the recruiting process.

Efficiency: Automating the first steps of recruiting saves time and resources, freeing up human recruiters to focus on more difficult jobs.

Scalability: The model can manage a huge number of interactions at once, making it suitable for developing enterprises.

2. Gabb Wireless

Industry: Child-Friendly Mobile Services

Deployment: Gabb Wireless uses a suite of open-source models from Hugging Face to add a security layer to screen messages that children send and receive. This ensures no inappropriate content is being used in interactions with people they don’t know.

Reasons for success:

protection and Security: The emphasis on kid protection connects with parents, fostering trust and brand loyalty.

Real-Time Monitoring: The ability to review messages in real-time allows for fast action if incorrect information is found.

Cost-effective: Using open-source models saves money while maintaining high levels of security and performance.

3. Shopify

Industry: E-commerce

Deployment: Shopify Sidekick is an AI-powered tool that utilizes Llama 2 to help small business owners automate various tasks for managing their commerce sites, such as generating product descriptions, responding to customer inquiries, and creating marketing content.

Reasons for success:

Automation: By automating repetitive operations, company owners may focus on key growth initiatives.

Personalisation: The technology may produce personalised content and replies, which increases consumer engagement and satisfaction.

connection: The AI tool is easily adopted and used by users because to its seamless connection with Shopify’s current platform.

4. Niantic

Industry: Gaming

Deployment: Niantic, the creator of Pokémon Go, launched a new feature called Peridot, which uses Llama 2 to generate environment-specific reactions and animations for the pet characters in the game.

Reasons for success:

Enhanced User Experience: Using LLMs to provide dynamic and contextually appropriate interactions improves the game experience.

Innovation: Adding AI-powered features keeps the game fresh and entertaining, drawing new users while maintaining existing ones.

Scalability: The model can manage a huge number of interactions, making it appropriate for a popular game with a worldwide audience.

Choosing the best Large Language Model (LLM) for a company is a complex and difficult undertaking owing to a variety of considerations. According to 4CRisk, organisations have challenges while using AI LLMs, including the necessity for data protection, security, customisation, and alignment with specific business processes. The difficulties include overcoming prejudices in public LLMs, controlling hallucinations, and dealing with resource constraints. Companies must also deal with concerns including restricted customisation possibilities, expensive development and maintenance costs, and significant security threats when using private LLMs.

According to Datanami, in the hurry to install LLM services and tools, some firms may fail to fully analyse corporate value or possible hazards, particularly in data analytics. This fast adoption might result in poor performance, lost money, and missed opportunities. For example, organisations may invest substantial time and money in training and deploying a model that eventually fails to satisfy their needs, wasting important resources and perhaps harming their image. These mistakes highlight the significance of thoroughly analysing LLMs based on individual use cases and overall solution architecture, rather than merely selecting the most sophisticated or advertised model available.

LiveBench as a Solution for Small Companies

LiveBench is a benchmarking tool that assesses the performance of Large Language Models (LLMs) in a contamination-free, objective manner. It provides a solid platform for small businesses to review and optimise their LLM implementations efficiently. Here’s how LiveBench may be an effective option for small businesses:

LiveBench is a continuous benchmark project that assesses LLMs using a variety of workloads. It is intended to reduce possible contamination by publishing new questions on a monthly basis and providing verified, objective ground-truth responses. This guarantees that the review is rigorous and equitable.

Key Features of LiveBench

LiveBench launched by Samuel Dooley and Colin White offers several key features that make it an invaluable tool for small companies. First, it provides contamination-free evaluation by regularly updating the test set with new questions from recent datasets, arXiv papers, news articles, and IMDb movie synopsis. This approach ensures that models are tested on fresh and relevant data. Second, LiveBench utilizes objective scoring with verifiable answers for each question, allowing for accurate and automated scoring without the need for human judges. Additionally, LiveBench includes 18 diverse tasks across six categories, with plans to release new, more challenging tasks over time, ensuring a comprehensive assessment of LLM capabilities.

🚨 Announcing LiveBench, a challenging new general-purpose live LLM benchmark! 🚨
Thanks @crwhite_ml and @SpamuelDooley for leading the charge!
Link: https://t.co/blOR8qLInV
Existing LLM benchmarks have serious limitations: 🧵 pic.twitter.com/O1A74cs4R0

— Micah Goldblum (@micahgoldblum) June 12, 2024

How Small Companies Can Benefit from LiveBench

Small businesses can benefit significantly from employing LiveBench in their LLM installations. One significant advantage is objective performance evaluation. For example, a small e-commerce business that wants to implement an LLM to improve its customer care chatbot can utilise LiveBench to objectively compare several LLMs. This assists the organisation in determining which model works best in comprehending and reacting to client inquiries, resulting in increased customer satisfaction and lower operating expenses.

Another significant advantage is the ability to develop continuously. A finance business that uses an LLM for fraud detection may routinely evaluate its model against LiveBench to assess performance over time and discover areas for improvement. This provides great accuracy and flexibility to new forms of fraud while retaining strong security safeguards.

LiveBench also provides a cost-effective alternative. A small marketing business looking to employ an LLM to generate personalised content for clients may use LiveBench to test various models without requiring costly in-house testing equipment. This decreases the cost and time required for model validation, allowing the company to concentrate resources on key business operations.

Scalability and adaptability are also key advantages. For example, a healthcare business creating an AI assistant to assist clinicians with patient diagnosis may leverage LiveBench’s broad tasks and regular updates to guarantee the model stays successful when new medical studies and data become available. This enables the business to expand its AI assistant to address more complicated inquiries while smoothly integrating new medical information.

Example of Successful Deployment

A prime example of successful deployment using LiveBench is TenXer Labs, a company specializing in electronic design validation. TenXer Labs uses LiveBench to provide real-time access to lab setups for evaluating electronic components. This allows customers to perform comprehensive hardware functional testing and electronics performance validation remotely. The reasons for their success include instant access, enabling customers to start evaluations immediately without procurement delays or setup overhead. Comprehensive testing support for a wide range of electronic components facilitates faster Bill of Materials (BOM) decisions and improved design for manufacturability (DFM). Moreover, the platform offers a personalized user experience with support for multiple languages and AI-powered navigation of datasheets.

The outcome for TenXer Labs has been substantial. They have successfully reduced the time-to-market for their solutions, lowered customer acquisition costs, and accelerated the sales cycle by providing a dynamic and interactive evaluation experience. LiveBench has thus proven to be a critical tool for small companies looking to optimize their LLM deployments efficiently and cost-effectively.

Conclusion: Scaling Enterprises with the Right LLM

In 2024, businesses will increasingly recognise the revolutionary power of Large Language Models (LLMs) to promote innovation, efficiency, and growth. Leaders and companies use LLMs to automate activities, improve consumer relations, and extract useful insights from massive volumes of data. To effectively expand with the proper LLM, businesses need to focus on a few essential methods. Objective performance assessment tools, such as LiveBench, provide a strong foundation for evaluating multiple LLMs, ensuring that the best model is chosen for given requirements. Continuous benchmarking and performance tracking provide continuing improvement, allowing models to remain successful and adapt to new difficulties. Cost-effective alternatives, such as using open-source LLMs or fine-tuning existing models, enable small and medium-sized businesses to employ advanced AI capabilities without incurring exorbitant expenditures. Scalability and flexibility are critical, with the correct LLM managing growing data volumes and user interactions across several business activities. Informed decision-making based on data insights improves AI implementation and eliminates costly mistakes. Seamless integration with current systems and workflows improves operational efficiency, while adherence to ethical standards and data privacy legislation ensures confidence and legal compliance. Incorporating user input guarantees that the LLM grows meaningfully while being relevant and effective. Updated viewpoints from industry executives emphasize strategic alignment with corporate goals, strong risk management frameworks, investment in AI expertise, and the use of LLMOps principles to manage the whole lifecycle.