Large Language Models (LLMs) have become a compelling option for data analysis with specific business needs. Many self-service-focused artificial intelligence (AI) companies like Wand (wand.ai) are offering their own LLMs for advanced data analysis through a no-code, chat-based interface. These offerings from AI companies have emerged as powerful tools for extracting valuable insights. However, organizations prefer to maintain control over their data from being used by AI tools over the sensitivity of their data. With organizations being vigilant over their data usage, AI companies and open-source platforms are offering their pre-trained LLMs to be deployed on-premises or in organization-controlled accounts on cloud platforms.
What is a pre-trained large-language model?
Large language models are AI models trained on large volumes of data. If the LLM were trained with no specific task, then it would have built foundational understanding for task-specific learning. The LLM at this stage is observed as a pre-trained LLM. At this stage, it is like a student majoring in general medicine, ready to pursue a specialization. Organizations can now train them to fit their specific needs, driven by their preferred business use cases. GPT-3 (Generative Pre-trained Transformer 3) is one of many well-known, widely used pre-trained LLMs.
The pre-trained LLMs learn much faster, as they need not be trained from scratch. Once fine-tuned for specific use cases, accuracy will be greatly improved. Leveraging these pre-trained models in organization-controlled environments involves a multi-step process: preparation, training, and fine-tuning.
- Data preparation: This is an essential step to ensure the model’s effectiveness. The data that will be used to train the pre-trained LLM should be formatted, labeled, and relevant. The data should be cleaned of any non-factual statements and anything that will introduce bias. Well-prepared data can improve the accuracy of the pre-trained LLM.
- Model Training: To build on the foundational knowledge, the pre-trained LLMs are trained to strengthen general understanding, expand their knowledge base, and refine internal parameters to better represent the underlying structure for accuracy. Training LLMs requires substantial computational resources. But training pre-trained LLMs requires significantly less computational resources and is cost-effective in most cases. As pre-trained LLMs will be trained for specific business use cases, a reliable assessment of their computational efficiency is expected. It also allows for collaborative training by multiple teams within the organization, making it economically feasible by sharing the computational burden.
- Fine Tuning: The pre-trained LLM will be exposed to a domain- or business-specific dataset to adjust its weights and parameters for a specific task. LLM’s internal structure consists of a complex network of connections, where connections are the relationships between words and concepts. These connections will be tuned based on the new dataset to enable better responses within the specific context. The effectiveness of fine-tuning a pre-trained LLM will depend on the quality and relevance of the dataset used.
LLM’s are not bulletproof, but with strategic oversight, one can harness their maximum potential. As organizations continue to prioritize data privacy and compliance, they can now consider taking advantage of pre-trained LLMs for greater access control over sensitive data and safeguarding data integrity while staying compliant with regulations.
