HaoTech
HIGHLIGHTS
- Large language models (LLMs) present engaging use cases for businesses ranging from translation and transcription to malware analysis, market research, and customer service.
- Large language models (LLMs) are swiftly rising to become one of the most buzzed-about tech innovations in the digital era.
- Indeed, experts project that generative AI could evolve into a $1.3 trillion industry by 2032 as an increasing number of users experiment with generative AI tools like ChatGPT, Google Bard, and Bing Chat.
However, with the technology still in its nascent stages, the potential applications of LLMs in a corporate setting are yet to be fully explored.
At a glance, LLMs can be employed in any situation where a company needs to analyse, process, summarise, rephrase, edit, transcribe, or derive insights from a dataset or input text. As their adoption continues to grow, there are several practical uses of language models that are emerging as particularly promising.
12 Top-Notch Applications of Large Language Models (LLMs)
1. Language Translation Using Models
One of the primary practical uses for LLMs is translating written texts. For instance, a user can input text into a chatbot, ask for it to be translated into a different language, and the system will automatically initiate the translation.
Studies have indicated that LLMs like GPT-4 are competitive against commercial translation products, such as Google Translate. However, it’s worth noting that GPT-4 is most effective when translating European languages and may not be as precise when translating “low-resource” or “distant” languages.
2. Malware Analysis
The introduction of Google’s cybersecurity LLM, SecPaLM, in April 2023, showcased an intriguing use of language models for malware analysis. Google’s VirusTotal Code Insight uses Sec-PaLM LLM to scan and explain script behaviours, identifying whether they’re malicious.
This method of scanning files for malware means that human users don’t need to run them in a sandbox to ascertain their potential for harm.
3. Search
Many users may have first experimented with generative AI as an alternative search tool. Users can pose questions to a chatbot in natural language and receive instant responses with insights and facts on potentially any topic.
However, it’s important to be aware that not all content provided by solutions like Bard or ChatGPT is accurate. Language models can create facts and figures, leading to misinformation. Therefore, it’s crucial for users to double-check any factual information presented by LLMs.
4. Content Creation
Another emerging use case for language models is content creation. LLMs enable users to generate a variety of written content from blogs, articles, short stories, summaries, scripts, questionnaires, surveys, and even social media posts. The quality of these outputs is contingent on the details provided in the initial prompt.
If not used to generate content directly, LLMs can also aid in ideation. Hubspot reported that 33% of marketers who use AI employ it to generate ideas or inspiration for marketing content. The main advantage here is that AI can expedite the content creation process.
Tools like DALL-E, MidJourney, and Stable Diffusion also allow users to generate images based on written prompts.
5. Virtual Assistants and Customer Support
Generative AI is also promising in the realm of customer support as virtual assistants.
Research by McKinsey found that one company with 5,000 customer service agents experienced a 14% per hour increase in issue resolution and a 9% reduction in time spent handling an issue with the application of generative AI.
AI virtual assistants allow customers to ask questions about services and products, request refunds and report complaints instantly. This not only eliminates the need for customers to wait for a human support agent but also automates repetitive support tasks for employees.
6. Detecting and Preventing Cyber Attacks
Another interesting cybersecurity use case for language models is detecting cyberattacks. LLMs can process large data sets collected throughout an enterprise network, spot patterns indicating a malicious cyber attack, and generate an alert.
Several cybersecurity vendors have begun experimenting with the technology for threat detection. For example, earlier this year, SentinelOne released an LLM-driven solution that can automatically hunt for threats and initiate automated responses to malicious activity.
Microsoft Security Copilot, for instance, allows users to scan their environments for known vulnerabilities and exploits, and can generate reports on potential security events in minutes, equipping human defenders to respond.
7. Code Development
Generative AI tools can generate code in languages like JavaScript, Python, PHP, Java, and C#. This allows non-technical users to generate basic code.
However, it’s recommended that programmers double-check code for functionality and security issues during development to avoid disruption post-deployment.
LLMs can also help debug existing code or even generate accompanying documentation, thereby saving
manual effort.
8. Transcription
LLMs are also gaining attention due to their ability to transcribe audio or video files into written text with high accuracy. Providers like Sonix use generative AI to create and summarise transcripts from audio and video files.
One of the advantages that LLMs have over traditional transcription software is that natural language processing (NLP) allows these tools to infer the context and meaning of statements supplied via audio.
9. Sales Automation
Generative AI tools like ChatGPT can also automate certain segments of the sales process, from lead generation to nurturing, personalization, qualification, lead scoring, and forecasting.
For example, an LLM can analyse a dataset, identify potential leads, understand their preferences, and create personalized recommendations.
10. Market Research
Generative AI’s ability to summarize and infer from large data sets makes it a useful tool for conducting market research to gain insights into products, services, markets, competitors, and customers.
11. Keyword Research
AI assistants also play a valuable role in streamlining the keyword research process. For instance, users can ask for the best keywords for a potential topic, alongside relevant subterms.
However, it’s advisable to use LLMs like ChatGPT to identify potential keywords and then cross-check them with a tool from a third-party provider like Ahrefs or Wordstream to ensure significant traffic.
12. Sentiment Analysis
LLMs can be used as a qualitative analysis tool to analyse the sentiment of text to infer a writer’s attitude toward a given topic.
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: Transforming Inactive Data into Actionable Insights
In any scenario where there’s a need to summarize or extract insights from a dataset, language models have a crucial role. As LLMs continue to evolve and more enterprises explore potential uses, organizations will gain a deeper understanding of risks and learn to mitigate certain drawbacks such as fact hallucination. As these models are further integrated into business operations, we can expect to see a transformation in how data is utilized, making inactive data actionable and driving informed decision making.
