Private GPTs: Evaluating LLMs for your Business

Chat GPT has sparked a seismic shift in business and technology, embodying the nature of a double-edged sword. On one hand, it rapidly attracted over 100 million users in its first two months; on the other, it navigated a data breach, emerging with just a few scars. As a substantial number of professionals turn to these tools to boost productivity, organizations and IT leadership are devising innovative strategies to incorporate these technologies into their operations without compromising security. Among these advancements, the emergence of Private GPTs stands out as particularly promising.?

Understanding the Power of Private GPTs?

Unlike the publicly available GPTs, Private GPTs, or Large Language Models (LLMs), offer the control, compliance, and privacy standards that most organizations require. They can be trained on private, proprietary datasets, ensuring that user inputs remain confidential and that all intellectual property remains with the organization. With sectors like sales and marketing already buzzing with possibilities, the journey into understanding and leveraging Private GPTs and LLMs is one that many organizations are eagerly embarking on.?

Setting the Stage for Private GPT Implementation?

Before diving deep into the world of private LLMs, it's crucial to have a clear understanding of the problem at hand. As the saying goes, "When you have a hammer, everything looks like a nail." It's natural to reimagine existing solutions with AI-based approaches such as the Private GPT, and here are some essential considerations for those embarking on this bandwagon:?

• Define the Problem Clearly: Understand the existing problem and assess how Private GPT can optimize efficiency or replace outdated solutions. For example, if your organization's primary challenge is to automate customer support, determine how Private GPTs can be trained to handle frequently asked questions, reducing the load on human agents.?
• Prioritize Customer Trust: Ensure AI implementations bolster customer trust and validate the solution's effectiveness in all use cases. For example, if you're a healthcare company, you might have sensitive patient data. When training your Private GPT, ensure that all personal identifiers are stripped of the data, and that the model doesn't inadvertently generate any private information in its responses.?
• Analyze the Economics: Balance the cost of developing and training Private GPTs with the anticipated benefits, ensuring a favorable ROI. For example, if the goal is to reduce customer service response times with a Private GPT, compare the costs of training and maintaining the model against potential savings from decreased manpower hours and increased customer satisfaction.?
• Assess Technical Feasibility: Focus on data quality, model selection, and validation methods to ensure robust deployment. For example, if you're a retail business wanting to use Private GPT for product descriptions, ensure your existing database can interface with the GPT model and that you have the computational resources for training, especially during peak product release periods.?
• Recognize Unintended Consequences: Monitor the output of Private GPT for unexpected patterns to understand potential implications.? For example, if you deploy a Private GPT to help customers choose the right insurance policy, keep an eye on the policies it recommends. Should it consistently suggest premium plans to customers seeking basic coverage or vice versa, it's a sign that the model may need adjustments to align with customer needs.

Now that we have a framework to evaluate if AI-based tools, such as Private GPTs, would be a good choice to solve the problem at hand, let's focus on some of the common challenges that are perceived when evaluating, training, and deploying LLMs in business settings.??

Demystifying LLM Deployment Challenges?

Hosting your own LLM sounds like a massive undertaking that would require an entire data center. However, it is possible to set up and train one of these on a decently sized workstation, server, or docker instance in relatively short order. This won’t have the power, performance or terabytes of training data used by the publicly available GPTs, but it can give an indication of how the model interacts with your data. With this foundational understanding in place, let's delve into the practical steps for evaluating how LLMs fit into your business operations.?

Creospan’s LLM Evaluation Methodology??

Building the Foundation: Platform and Framework?

Setting up the right environment is the first step. This often involves installing Python and choosing a deep-learning framework. TensorFlow and PyTorch are among the popular choices that work well with Nvidia GPUs and software (CUDA). TinyGrad is a newer entrant into this space, attempting to make AMD cards accessible on their Neural Network Framework. Follow a path that aligns with your organization and infrastructure resources but be sure to host the models on a consistent platform, so measurements are relative to the model differences and not the environment differences.?

Choosing a Large Language Model??

With the environment ready, the next step is selecting an LLM that aligns with your needs. Repositories like Hugging Face’s Transformers Library, OpenAI, and Google’s TensorFlow Hub are treasure troves of pre-trained models. Be sure to verify that the licensing agreement will keep company data private. Also, ensure that the model’s use case (general purpose, translation, chat, knowledge retrieval, code generation) aligns with the implementation.??

• Hugging Face Transformers Library: https://huggingface.co/models ?
• OpenAI: https://platform.openai.com/docs/models ?
• Google’s TensorFlow Hub: https://tfhub.dev/ ?

Training Large Language Models?

Most models on these repositories are “pre-trained”. This means the model understands the structure, grammar and syntax of a language, but has not been trained in any specific area of knowledge. The term used for training a model with a dataset for a purpose is known as “fine-tuning” that model. This involves organizing your specialized dataset for intake. Optimizing training parameters. Evaluating performance and ensuring compliance.???

• Curating a dataset - Text based input such as paragraphs of text are easy for an LLM to take in. However, input with lots of graphs, tables and charts are far more difficult to interpret and may require additional labeling or contextual descriptions.?
• Optimizing Training Parameters – Parameters such as Learning Rate, Batch Size, Number of Epochs, Loss Function, Weight Decay and Dropout Rate each influence the performance of a model. These should not be expected to be consistent across LLMs – a tester would need to tune these parameters looking for optimal results within the model before performing cross model comparisons.?
• Evaluating Performance – Depending on the intended usage, a consistent set of tasks can be defined and used to challenge each model. Have the tasks align with your expected usage. Tasks can include: summarization, reasoning, language translation, code generation, fact extraction, recommendations, etc. The challenging part is consistent scoring. Scoring will require human assessment of the responses by the model. This will be subjective across testers. The complexity of scoring responses can vary based on what is important to the organization, but it can also be as simple as ‘helpful’ vs ‘not helpful’.???
• Ensuring Compliance – Ideally, users of an LLM all have access to the breadth of data populated within the LLM. Establishing guard rails for user groups can be challenging, not only for data access, but also for ethical, regulatory, and company-specific standards. Any concerns identified while evaluating performance should be noted and addressed. However, it will not end there. Compliance will require continual monitoring and has to be part of an overall AI Operations plan for an organization.?

Conclusion?

Evaluating Large Language Models is pivotal for organizations seeking the ideal version of private GPT that holistically aligns with their needs. By harnessing publicly available models and maintaining consistency in datasets, businesses can optimize the potential of these LLMs, even in the most sensitive sectors. Tailoring common test cases to specific business requirements further refines the model's applicability. The true power of these generative technologies lies in their ability to automate and enhance various business processes, leading to heightened efficiency and personalization. By mastering these technologies and methodologies, organizations can craft a holistic pathway to refine their business processes and position themselves as the vanguard of a competitive future.?

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