What is the Role of Vector Embeddings in Training ChatGPT Models?

What is the Role of Vector Embeddings in Training ChatGPT Models?
Learn how vector embeddings and a related concept known as semantic distance play a key role in properly training ChatGPT models.

Even as ChatGPT increases in adoption across the business world, many users still worry about the accuracy of its underlying language models. A thorough machine learning model training process remains a critical aspect of any AI-powered tool like ChatGPT. However, specific natural language processing use-cases rely on a concept known as vector embeddings to improve model accuracy and efficiency. 

In a natural language processor, embeddings essentially define the weight or probability a certain word is going to appear next in a sentence or phrase. This helps natural language processors generate or understand text that ultimately reads like human language. It uses the massive amount of text data on the web to calculate these weightings. For example, in the phrase "Float like a butterfly, sting like a…” the word “bee” boasts a strong probability of appearing next. 

Today, ChatGPT largely depends on these previously-written human language examples as an input during its training. It effectively has no filter, which leads to the sometimes embarrassing or inaccurate responses it makes. Vector embeddings and a related concept known as semantic distance play a key role in properly training ChatGPT models. In essence they make ChatGPT usable in the corporate world without embarrassing a business and/or their clients and customer base. Let’s take a closer look. 

Vector Embeddings Explained 

Any blog post, tweet, or human language phrase can be defined with its own vector embedding. When comparing two chunks of text, if both express a similar idea or underlying concept, their semantic distance is smaller than two vector embeddings for unrelated ideas.

This is the case despite the individual words used in both examples. This somewhat abstract concept lies at the heart of properly training ChatGPT or other similar large language models to be used in a competitive business world. 

For example, a customer service chatbot used by one beer producer shouldn’t tell customers that another company’s beer tastes better. Vector embeddings inform the model training process to filter out the kind of language that embarrasses a business using advanced chatbots. It remains an essential piece of the puzzle in properly preparing ChatGPT or a similar tool to serve on the front lines of a business’s customer service or communications role. 

Taken another way, vector embedding takes the concept of keyword matching used in internet searching and blasts it well into the outer realms of the universe. Essentially, it allows ideas or concepts to be mathematically represented, making it possible to perform computations on them in a variety of ways. While it currently provides promise in making advanced chatbots usable in the business world, other intriguing use-cases abound. 

Examples of potential applications powered by vector embeddings and semantic distance span everything from matching potential dating partners to finding a perfect candidate for a company’s open position. It’s a major reason why many tech pundits feel this type of advanced AI is the most important technology innovation since the internet and the web itself.

Why Vector Embeddings Matter to Companies Interested in Using ChatGPT 

ChatGPT in its current form remains easy to use. Many business leaders and entrepreneurs feel it already provides the means to let their companies quickly reinvent a variety of business processes, especially in the areas of customer service and marketing. However, there’s nothing that currently stops ChatGPT from sharing corporate secrets other than focused training that relies on vector embeddings. 

At its core, ChatGPT essentially functions as a random word generator. So businesses obviously want to ensure it speaks highly of their own products as opposed to the product line of their competition. 

They also need to feel comfortable it won’t mention any trade secrets when prompted by someone from another organization. This scenario is why vector embeddings and semantic distance matter for this final level of language model training. It needs to effectively function as a smart filter, ensuring any AI-superpowered chatbot stays on message. 

Training Language Models How to Filter Their Output

When we work with a company on a project using natural language processing or leveraging OpenAI’s ChatGPT API, this advanced level of training happens over two stages. First, we create two data silos containing massive amounts of human language in an unstructured format. The first silo contains the company product data, including any proprietary or otherwise secret information. A negative word or phrase database makes up the second silo, with data about competitors or other language the chatbot must never mention. 

The second stage involves prompt engineering, where users simulate different personas to query the new chatbot. These users need to take note of any instances where the language model generates any text containing private information from the first silo or anything from the second silo. It’s an iterative process depending on a feedback loop that results in a chatbot your business can trust!

If you have a great idea for leveraging ChatGPT, but need help from experts, connect with the team at NineTwoThree. We boast significant experience in machine learning model development and ChatGPT development – making us the right fit for your organization. Reach out to us at your earliest convenience to talk about the possibilities of a partnership. 

Tim Ludy
Tim Ludy
Articles From Tim
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