Large language models (LLMs) are quickly becoming one of the most-hyped technological innovations in the Internet age.
However, with the technology in its infancy, the use cases for LLMs in the enterprise are still to be defined.
At first glance, LLMs can be used in any scenario where an organization needs to analyze, process, summarize, rewrite, edit, transcribe or extract insights from a dataset or input text. With adoption increasing, there are some practical applications of language models that appear to be promising.
12 Best LLM Applications
1. Translation With Language Models
One of the simplest practical applications for LLMs is to translate written texts. A user can enter text into a chatbot and ask it to translate into another language, and the solution will automatically begin translating the text.
Some studies have suggested that LLMs like GPT-4 perform competitively against commercial translation products, such as Google Translate. That being said, researchers also note that GPT-4 is most effective when translating European languages, but isn’t as accurate at translating “low-resource” or “distant” languages.
2. Malware Analysis
The launch of Google’s cybersecurity LLM SecPaLM in April 2023 highlighted an interesting use for language models to conduct malware analysis. For instance, the Google VirusTotal Code Insight uses Sec-PaLM LLM to scan and explain the behavior of scripts to tell the user whether they’re malicious or not.
Scanning files for malware in this way means that human users don’t need to run them in a sandbox to find out if they’re destructive.
3. Content Creation
Another increasingly common use case for language models is content creation. LLMS enables users to generate a range of written content from blogs and articles to short stories, summaries, scripts, questionnaires, surveys, and social media posts. The quality of these outputs depends on the details provided in the initial prompt.
If LLMs aren’t used to generate content directly, they can also be used to help with ideation. According to Hubspot, 33% of marketers who use AI use it to generate ideas or inspiration for marketing content. The main value here, is that AI can speed up the content generation process.
Many users will first have experimented with generative AI as an alternative search tool. Users can ask a chatbot questions in natural language and will receive an instant response with insights and facts on potentially any topic.
While using solutions like Bard or ChatGPT as a search tool provides access to a wide-range of information, it’s important to be aware that not all of this content is accurate.
Language models are prone to hallucination, and have a tendency to invent facts and figures. For this reason, it’s a good idea for users to double-check any factual information presented by LLMs so they can avoid being misled by misinformation.
5. Virtual Assistants and Customer Support
McKinsey research found that at one company with 5,000 customer service agents, the application of generative AI increased issue resolution by 14% an hour and reduced the time spent handling an issue by 9%.
AI virtual assistants allow customers to ask questions about services and products, request refunds and report complaints instantly. For end users, it eliminates the need to wait for a human support agent, and for employees it automates repetitive support tasks.
6. Detecting and Preventing Cyber Attacks
Another interesting cybersecurity use case for language models is detecting cyberattacks. This is because LLMs have the ability to process large data sets collected throughout an enterprise network and can spot patterns that indicate a malicious cyber attack and generate an alert.
So far a number of cybersecurity vendors have begun experimenting with the technology for threat detection. For example, early this year SentinelOne released an LLM-driven solution that can automatically hunt for threats and initiate automated responses to malicious activity.
Another approach demonstrated by Microsoft Security Copilot, allows users to scan their environments for known vulnerabilities and exploits, and can generate reports on potential security events in minutes to equip human defenders to respond.
7. Code Development
The code generation capabilities of language models allow non-technical users to generate basic code. Although it can write code for simple projects that solve basic challenges, it struggles to address more complex tasks that are bigger in scope and scale.
Thus programmers should double-check code for functionality and security issues during development to avoid disruption post-deployment.
They can also be used to help debug existing code or even generate accompanying documentation so that users don’t have to spend time doing it manually.
LLMs are also gaining lots of attention due to their ability to take audio or video files and transcribe them into written text with high accuracy. Providers like Sonix use generative AI to create and summarize transcripts from audio and video files.
This means that human users don’t need to spend time transcribing audio manually, which can save a significant amount of time and eliminate the need to invest in a transcriptionist.
One of the advantages that LLMs have over traditional transcription software is that natural language processing (NLP) enables these tools to infer the context and meaning of statements supplied via audio.
9. Market Research
Generative AI’s ability to summarize and make inferences from large data sets makes it a useful tool for conducting market research to gain insights into products, services, markets, competitors and customers.
Language models can process a user’s text input or dataset and develop a written summary of trends and provide insights into buyer persona’s, competitive differentiation, market gaps, and other information you can use to grow the business long term.
10. Keyword Research
AI assistants also have a valuable role to play in streamlining the keyword research process. For instance, users can ask what the best keywords are for a potential topic, alongside relevant sub terms.
For example, you could ask for a list of some SEO-friendly titles for a website’s blog. For the best results, it’s a good idea to use LLMs like ChatGPT to identify potential keywords and then to cross-check them with a tool from a third party provider like Ahrefs or Wordstream to make sure there’s significant traffic.
11. Sales Automation
Generative AI tools like ChatGPT can also be used to automate certain segments of the sales process from lead generation to nurturing, personalization, qualification, to lead scoring and forecasting, .
For instance, an LLM can analyze a dataset and identify potential leads, while developing an understanding of their preferences and creating personalized recommendations.
Likewise, if used to forecast sales, the solution can process a dataset, identify potential patterns and estimate future sales and the amount of revenue that will be coming in.
12. Sentiment Analysis
LLMs can be used as a qualitative analysis tool, to analyze the sentiment of text to infer a writer’s attitude toward a given topic.
This enables an organization to gauge customer opinion taken from sources including social media comments, customer reviews to help an organization discover insights that can help them to better manage their brands.
For example, sentiment analysis can highlight key words that customers use to describe a brand or product, and highlight what features or capabilities they feel are most important for products to have, which can help to inform future marketing efforts.
Language Models: Making Inactionable Data Actionable
In any situation where you want to summarize or take insights from a dataset, language models have a role to play. As LLMs develop further and more enterprises experiment with potential use cases, organizations will have a better understanding of risk and mitigating some of the drawbacks like fact hallucination.