There are many benefits to running a large language model for a company or product, but the most fundamental is the ability to provide real-world data.
译自 6 Reasons Private LLMs Are Key for Enterprises 。
With OpenAI's ChatGPT released to the public, it stands to reason that the Big Language Model (LLM) has taken the world by storm. Fun and powerful, LLM brings new ideas to the way we work and interact with people. For decades, we have been interacting with computers in structured ways such as programming languages and user interfaces. These structured interactions have a high barrier to entry and require users to interact with computers in the way and language they expect. Large language models completely subvert this model, allowing users to interact with computers in natural language.
Nvidia defines LLM as "a deep learning algorithm that recognizes, summarizes, translates, predicts, and generates text and other forms of content based on knowledge gained from massive data sets." Unfortunately, training a large language model requires a lot of computing resources, hundreds or even thousands of graphics cards, terabytes of data, and a lot of time, which makes the training of custom models only a very few large enterprises have the strength. In addition, it raises the following considerations:
- What if you need the data in LLM to be up-to-date?
- What if you need customer-specific data in LLM?
- What if you need sensitive or proprietary data in LLM?
If these issues are critical to your business, you need a large, proprietary language model.
What is a proprietary large language model?
Proprietary large language models can be summarized as the following key characteristics:
- It is hosted within your computing infrastructure and runs alongside other business workloads.
- It is trained based on company, industry, or product data. The available data is real-time and actionable.
- It only provides accurate situation-specific information to the parties authorized to access.
There are two main forms of proprietary large-scale language models. The first is a custom-trained model using a company or industry-specific dataset, and the second is a combination of a hosted large-language model such as Llama 2.0 with retrieval augmentation generation (RAG). This article focuses on the second form, RAG.
Retrieve the enhanced build
According to IBM, "RAG is an AI framework that retrieves facts from an external knowledge base, trains large language models on the latest and most accurate information, and gives users insight into the process of building large language models." ”
The word "ground" is apt to describe what RAG is doing for large language models, and it describes what happens when additional information is provided to large language models in queries. When providing contextual information to a large language model in a query, it tends to give higher weight than the information in the larger corpus used for training, so that the model's response is based on the context provided, which is "making it realistically based". This grounding in reality reduces the occurrence of illusions while providing users with a more accurate response.
A typical RAG design is as follows:
Retrieve enhanced generative design drawings
There are many benefits to running a large, proprietary language model for a company or product, but the essence is the ability to provide users with real-time, contextualized data that can be queried in natural language.
Data Protection
Large-scale language models that can process sensitive data, such as medical records or financial data, and then harness the power of generative AI to make breakthroughs in those areas. Because models are hosted on internal infrastructure and exposed only to authorized personnel, you can reduce risk by building powerful customer-centric applications, chatbots, or simplifying how employees access company data without having to send it to a third party.
Customization
With proprietary large-scale language models, you can adapt models and responses to your company, industry, or customer needs. This specific information is usually not included in large language models that are common or publicly available. You can provide your model with customer support cases, internal knowledge base articles, sales data, app usage data, and more to ensure you get the response you need.
Control
Updates to public large language models often take months. However, proprietary models can control factors such as update cycles based on user needs.
It is important to control the version or model used, because if you change the model used to create the embedding, you will need to recreate or version all embeddings. Versioned embeddings will allow you to continue using the old embedding because you can reference the old model if necessary.
Reduce costs
Using proprietary large-scale language models can reduce the cost of purchasing models or patented AI software from external companies. According to LeewayHertz, this is especially important for small and medium-sized businesses and developers on a budget. In addition, using a proprietary model helps companies avoid vendor lock-in, which can lead to significant cost savings in the long run.
More accurate
Large language models trained on more specific information can provide more accurate and targeted information. At the same time, it reduces the risk of absurd responses. You've probably used a large public language model like ChatGPT and seen its quirky reactions. Sometimes it gives very accurate information, and sometimes it gives completely wrong information and presents it as fact. This is largely due to the extensiveness of the datasets used for public model training. When a model is given very specific context, the likelihood of getting an accurate response increases exponentially.
reliability
The performance of common large language models is sometimes unreliable, and it is not uncommon for infrastructure overload to delay queries. As we all know, user attention span is limited, and increasing interaction latency increases the risk of user churn. Running a large language model that you own allows you to keep an eye on the response time of the model and increase resources if necessary.
Next?
With SingleStore, you can combine relational data with vectors to provide context for queries using your application's real-time data. SingleStoreDB is a distributed, real-time analytic and transactional database whose powerful performance ensures that your large, proprietary language models respond faster than anyone.
As AI becomes more widely available, enterprises will demand real-time new data to provide the right context for the underlying model. Large language models and other multi-structured underlying models need to respond to requests in real time, and in turn, they need the ability of the data plane to process and analyze data in different formats in real time.
To achieve real-time AI, enterprises must continuously vectorize data streams as they are ingested and use them for AI applications. I think it's critical to ensuring that the business is ready for the future ahead of you.
If you're interested in getting as deep as possible into captivated, large language models, join SingleStore Now on October 17, where I'll cover how developers can build and scale compelling generative AI apps for the enterprise. For more information and registration, visit singlestore.com/now.