The value of Large Language Models for enterprise Part.2
Featuring "The Value of Large-Scale Language Models in the Enterprise” which is a four-part series. Part.2 includes “How I can use Large Language models in my business?”, “1. Accelerating Knowledge work” and “2. Processing unstructured data”
How I can use Large Language models in my business?
Now that we have discussed the potential of large language models (LLMs) and how they are likely to become ubiquitous in the business world, you may be wondering how you can actually use these technologies to improve your own operations.
To help you get started, we have put together a summary of the various applications of LLMs in the enterprise context. This overview will give you a sense of the types of problems that LLMs can help you solve, and the ways in which they can be integrated into your existing workflows.
1. Accelerating Knowledge work
Large language models can be used to accelerate the typical office worker workflow by being used as a "calculator for writing." This refers to the ability of large language models to quickly generate text based on a prompt provided by the user.
For example, imagine a typical office worker needs to write a report on a complex topic. Instead of spending hours researching and writing the report, the worker can use a large language model to generate a first draft. The worker can simply provide the model with a prompt, such as "Write a report on the benefits of using artificial intelligence in business," and the model will generate a complete report that the worker can then edit and refine.
Using a large language model as a "calculator for writing" can save office workers a significant amount of time and improve their productivity. Instead of spending hours researching and writing, workers can focus on other tasks that require their attention. In addition, using a large language model can also improve the quality of the writing, as the model can generate text that is well-written and coherent.
It's important to note that using a large language model as a "calculator for writing" does not replace the need for human input and editing. While the model can generate a first draft, it's up to the user to edit and refine the text to ensure that it meets their specific needs and requirements.
In addition to providing a prompt for the large language model, users can also provide structured data from another program and let the model incorporate the language into the report. This can be especially useful for generating reports that require a combination of structured data and natural language.
For example, imagine a sales team needs to generate a report on the performance of their products. They can use a spreadsheet program to generate the data and then provide that data to a large language model. The model can then use that data to generate a report that incorporates the data in a natural language format. The report might include insights on which products are performing well, which are underperforming, and what actions the team can take to improve performance.
By using structured data in conjunction with a large language model, users can generate reports that are both accurate and easy to read. The structured data provides the necessary information, while the language model helps to generate the narrative around that data.
2. Processing unstructured data
Unstructured data is information that is not organized in a specific way. For example, a long paragraph of text is unstructured data because it doesn't have any specific format or organization.
On the other hand, structured data is information that is organized in a specific way. For example, a table with columns and rows is structured data because it has a specific format and organization.
Large language models can be used to "read" unstructured data and extract the relevant information in a structured format. For example, if you have a long document with information about sales, a large language model can be trained to identify the relevant sales data and extract it into a structured format such as a table. This can make it much easier to analyze and work with the data.
This is particularly useful in supply chain operations, where there is often a large amount of unstructured data in the form of emails, invoices, and other documents. By analyzing this unstructured data, large language models can help to identify patterns and insights that might not be apparent from traditional data analysis techniques.
For example, a large language model could be used to analyze emails and other communication between suppliers and buyers to identify potential bottlenecks or delays in the supply chain. It could also be used to analyze customer feedback and social media data to identify trends and patterns in consumer demand.
In addition, large language models can be used to improve the accuracy of demand forecasting models by incorporating additional sources of data. For example, a large language model could be used to analyze news articles and social media data to identify trends and events that might impact consumer demand.
Continue to Part.3
Co-founder and CEO
Tiago holds a Master's degree in Theoretical/Mathematical Physics and a PhD in Biophysics from Ludwig-Maximilians University Munich. After graduation, he joined Google DeepMind as a research engineer. There he worked on a number cutting-edge research projects which led to publications in international machine learning conferences and scientific journals such as Nature. He then joined Cogent Labs, a multinational Tokyo based AI start-up, as a lead research scientist. In August 2020 co-founded Recursive Inc, and is currently CEO.