Large Language Models or LLMs are a hot topic in data science. Much of this is due to how well they’ve been able to understand and process human language in recent years. It’s likely you might even know a few. One in particular, ChatGPT, has become wildly popular both within the tech world and in greater society. If you’re unfamiliar, these models use neural network architectures and vast amounts of data to generate text, answer questions, and perform language-related tasks. This is just one of the many popular use cases for large language models.
Because of this, many businesses across industry lines can easily see their practical benefits and many have already moved in to integrate LLMs into their arsenal. So let’s take a look at a few current applications of LLMs in business and the reason for being.
Believe it or not, an LLM chatbot or virtual assistant can be extremely useful for a business because it can provide fast and efficient customer service, handle routine inquiries and tasks, and free up employees to focus on more complex and strategic work. In short, it provides businesses with the flexibility to more effectively utilize the labor hours of their human employees; which can also mean lower costs.
One example of this is the IBM Watson Assistant. Much like ChatGPT which is more familiar to the general public, Watson Assistant is a conversational AI platform with a focus on customer management. Using machine learning models, it can take care of inquiries and complete a user’s intended action through simulated conversations. It also understands customer context so it may transfer a customer to a human agent when required. Of course, another advantage is its ability to be service available 24/7 with an accurate rate – in terms of answering questions – of 95%.
As the world continues its ascent into the digital space, the need for greater security and fraud detection will only grow in scale. Many industries, particularly financial institutions, know this full and well. If customers cannot trust that their assets, information, and privacy aren’t safe, then their ability to attract customers will be severely hampered. This is why businesses are turning to LLMs to gain an edge against fraud. The reason why LLMs are being used is that businesses can automate the detection of fraud by recognizing patterns that can alert them. Their efficiency and costs to scale are also very enticing to businesses as is their ability to be more accurate thanks to machine learning models that can learn from patterns of human behavior and identify patterns of fraud transactions.
FICO’s Falcon Intelligence Network is a fraud detection and prevention system used by financial institutions around the world. It uses a combination of machine learning algorithms, data analytics, and human expertise to identify and prevent fraud across multiple channels and transactions. The Falcon Intelligence Network is constantly updated with the latest fraud trends and patterns, allowing it to stay ahead of emerging threats and adapt to changing fraud landscapes. By providing real-time insights and alerts, the system helps financial institutions to reduce fraud losses, improve customer satisfaction, and maintain regulatory compliance.
With hundreds of commonly spoken languages on Earth, the language barrier has always been a costly issue if you want to connect with a larger audience. Now, thanks to LLMs, this seems to be changing quickly. That’s because LLMs are able to help in the translation of human languages thanks to their ability to leverage learning vast amounts of multilingual text data. Then, the models can be trained on parallel corpora, in which sets of text in two or more languages are aligned at the sentence or phrase level. By learning the patterns and relationships between different languages in these corpora, large language models can generate translations that are more accurate and fluent than traditional rule-based translation systems.
Believe it or not, you may have used an LLM already and not even known. The Google Translate service, which uses an LLM, provides automated translations of text and speech in over 100 languages. It does this quickly, and if you’ve used it for a few years, you likely have noticed a jump in accuracy as time has gone on. That’s because Google Translate leverages vast databases of multilingual text data with ever-sophisticated neural network algorithms to provide translations to users.
This is where LLMs have found an entry in the public imagination, content creation, and research. For research, anyone who’s used ChatGPT for research purposes knows full well how powerful a tool it is. It’s like having a search engine which you can have a prolonged conversation with in order to research. Uses for LLMs are within the world of content creation, practically in the world of journalism. Tools have been created to generate news articles, summaries, headlines, and more based on real-time events, and input data from users.
The most popular use case for this is OpenAI’s GPT-3 LLM. The Associated Press uses the LLMs to generate earnings reports for publicly traded companies. By inputting key financial data into the system, GPT-3 is able to generate a summary of the company’s performance that can be used as the basis for a news article. But it’s not just the Associated Press, The Guardian also uses GPT-3 in order to generate summaries of complex news stories that can be displayed on the publication’s homepage and mobile app. The summaries provide readers with a quick overview of the article’s main points, making it easier for them to decide whether to read the full article.
For those who are interested in marketing and marketing analytics, LLMs in sentiment analysis can be a game changer. That’s because LLMs can be used in identifying and classifying subjective opinions expressed in text data. This is partially useful in marketing, social media monitoring, and customer service to gauge public opinion and sentiment about a particular brand, product, or service.
The way this works is by training the LLM on a large corpus of text data that has been labeled with sentiment categories such as positive, negative, or neutral. By analyzing the patterns and relationships between words and phrases in the training data, the model can learn to recognize and classify sentiment in new text data. Believe it or not, some LLMs can even go deeper and even be used for more nuanced sentiment analysis tasks such as identifying intensity, sarcasm, and more.
One great example of this is Sprinklr. It’s a social media management and customer engagement platform that uses large language models for sentiment analysis to help businesses monitor and respond to social media conversations about their brand or product. Sprinklr’s platform can analyze social media data in real time to identify sentiment and trends and provide insights into customer behavior and preferences.
Conclusion on use cases for large language models
Not bad right? Large language models are at the forefront of integrating AI and AI-powered tools across industry lines. As these models continue to learn and grow based on their data, so will their ability to improve the quality of services to their human users, making use cases for large language models commonplace.
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