Bài viết 1: Top 10 Real-Life Applications of Large Language Models \n Human or AI? Who can tell? Large language models can write stories, answer questions, and even make jokes, almost like a real person. But it's their mix of creativity and data that really makes us wonder, as we enter a new era where technology feels more and more human.

Large language models (LLMs) are advanced artificial intelligence systems designed to understand, interpret, and generate human-like text. They are a type of deep learning model, to be exact, that have been trained on a wide variety of internet texts. LLM applications can perform numerous tasks including writing essays, creating poetry, coding, and even engaging in general conversation.

Probably the most famous example of a large language model to date is OpenAI’s GPT-4, which powers ChatGPT. It has demonstrated a remarkable understanding of complex topics and the ability to generate detailed, nuanced text in various styles and languages. This showcased the potential of large language models to transform industries, enhance productivity, and even entertain.

As the technology continues to improve every single day, LLM use cases are also becoming more sophisticated and diverse. Let’s take a closer look at the most trending applications of large language models and see how they are utilized in real life.

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10 most popular applications of large language models
Top 10 LLM applications

Large language models’ ability to generate text in real-time has made them invaluable in enhancing search engines, powering virtual assistants, and improving language translation services. While these are just three examples, there are many more LLM use cases.

In this section, we will dive deep into the 10 most widespread applications.

Content generation
LLM applications are especially good at content generation. They can be used to automatically create texts for various purposes, including articles, blog posts, marketing copy, video scripts, and social media updates. Moreover, LLM-backed generative AI apps can adapt to different writing styles and tones, making them versatile for generating content that resonates with specific target audiences.
Businesses and content creators harness these models to streamline content production, saving time and effort in the writing process.

Real-world apps
Among the myriad applications of large language models, content generation stands out as one of the most popular and widely adopted use cases. This is why there are already numerous AI-powered apps that serve this particular purpose. Claude and ChatGPT are two of them.

Claude

Claude is an AI assistant developed by Anthropic. It has rapidly evolved since its first release in March 2023.

Claude is great at sophisticated dialogues, creative content generation, complex reasoning, and detailed instruction. Its unique feature is the industry-leading 100,000 token context window, which allows it to process an extensive amount of information equivalent to the length of an average novel in just a minute.

ChatGPT

When it comes to content generation, it’s impossible not to bring up this well-known AI tool. Its main purpose is to assist users in generating coherent text based on the prompts it receives.

Digital marketers, for example, turn to ChatGPT to craft compelling ad copy, blog posts, and social media content that captivates their specific target audience. Similarly, educators find ChatGPT helpful in creating instructional materials, quizzes, and interactive learning modules, making the educational content both informative and engaging for students.

In the creative industry, authors and script writers use ChatGPT to break through writer’s block, develop plot ideas, or even write entire sections of their manuscripts. Programmers and developers also utilize ChatGPT to generate code snippets, debug existing code, or learn new programming languages.

The possibilities are quite vast. As ChatGPT continues to evolve, its applications across different fields are only expected to grow.

Despite its numerous benefits and advancements, AI still exhibits biases. Discover the common types of biases that may occur and learn how to address them

Translation and localization
LLM applications can provide accurate, context-aware translations across numerous language pairs. These models are trained on vast collections of bilingual or multilingual text, allowing them to understand nuances, idioms, and grammatical structures of different languages. They can maintain the intent and style of the original text, which is crucial for literary translations, business communications, and legal documents.
As for localization, LLMs help adapt content culturally and contextually for different target audiences, ensuring that the translated material is culturally appropriate and resonant. They consider local customs, measurements, date formats, and cultural references, making the content relevant and accessible. This capability is particularly important in marketing and entertainment industries, where engagement heavily depends on cultural nuance.

In addition to this, large language models facilitate real-time translation and localization, enabling businesses to communicate effectively with international partners and customers.

Real-world apps
Let’s take a look at two large language model applications — Falcon LLM and NLLB-200.

Falcon LLM

Falcon LLM, developed by the Technology Innovation Institute (TII), is an open-source AI model. Its Falcon 40B version was trained on 1 trillion tokens and has 40 billion parameters, while Falcon 180B boasts 180 billion parameters, having been trained on 3.5 trillion tokens.

In general, Falcon LLM excels across a spectrum of activities, including reasoning, programming, skill assessments, and knowledge evaluations.

What’s noteworthy is that its multilingual capabilities extend across numerous languages, including English, German, Spanish, French, Italian, Polish, Romanian, Swedish, and more, making it adept at translation and localization tasks. This allows for seamless integration into various applications, especially those requiring a deep understanding of multiple languages.

NLLB-200

NLLB-200 is an artificial intelligence model introduced by Meta AI. It translates across 200 different languages, incorporating many that were previously unsupported or poorly served by existing translation tools, and notably includes support for 55 African languages.

This model is not just a technical achievement but also a step towards a more inclusive technology, allowing for better communication and understanding across a diverse range of languages and cultures.

Search and recommendation
LLMs are capable of understanding and processing natural language queries with unprecedented accuracy and context. When integrated into search engines, these models can interpret the intent behind a user’s query and deliver more relevant and precise results. They can also generate summaries of content, making it easier for users to find the information they need quickly.
In recommendation systems, LLMs analyze user preferences, search history, and interaction data to personalize content suggestions. They can predict user needs, thereby enhancing the entire user experience.

Real-world apps
Let’s explore Bard — a compelling example of how LLM applications are enhancing search systems.

Bard

Developed by Google and launched in March 2023, Bard is a good example of an LLM application in search. Originating from the LaMDA family and subsequently upgraded to PaLM and Gemini, Bard was introduced as a response to the rise of OpenAI’s ChatGPT.

As a research LLM, Bard leverages Google’s extensive knowledge base and predictive capabilities to generate responses, offering creative and flexible answers to user prompts.

In essence, Bard improves Google Search by offering a “Google it” feature, which lets users confirm its responses or delve deeper. This opens up access to reliable sources and fosters a more integrated search experience.

Are you ready to develop your own AI strategy for your company? Find out how to effectively plan and implement it

Virtual assistants
LLMs for virtual assistants
At the core of AI-powered virtual assistants are LLMs that understand and process natural language. When a user asks a question or gives a command, the LLM interprets the intent and context of the request. Once the intent is understood, the LLM generates an appropriate response.

Modern virtual assistants also learn from interactions to provide personalized responses and improve over time. They analyze feedback, remember users’ preferences, and adapt to their unique way of communication.

Here is what LLM-based virtual assistants do specifically:

Performing tasks: Virtual assistants can perform a variety of tasks, from setting alarms and reminders to booking appointments, sending messages, or even ordering groceries. They interact with other applications and services to execute these tasks on behalf of the user.
Providing information: They can answer questions and provide information on a wide range of topics like weather forecasts, news, and traffic updates. LLMs enable them to pull and generate information from various sources quickly and reliably.
Facilitating conversations: Virtual assistants can engage in conversations, providing a more human-like interaction.
Automating customer support: Many businesses use virtual assistants to handle customer inquiries, guide users through troubleshooting, or provide detailed product information. This automation can lead to faster response times and 24/7 support availability.
Enhancing accessibility: For individuals with disabilities or those needing hands-free support, virtual assistants offer a valuable tool for interacting with technology and accessing information effortlessly.
Real-world apps
Large language models are the driving force behind some of the most advanced and widely-used virtual assistants today, including Amazon’s Alexa and Google Assistant. Let’s see how they apply this technology to boost their services.

Alexa

Alexa is Amazon’s voice-controlled virtual assistant based on a cloud service. It is capable of voice interaction, music playback, setting alarms, streaming podcasts, and providing real-time information such as news or weather. Alexa can also control several smart devices, functioning as a home automation system.

In September 2023, Amazon announced a significant upgrade to Alexa, introducing a new, custom-built LLM to enhance the assistant’s capabilities and conversational qualities. The revamped Alexa aims to surpass ChatGPT by providing more “real-world” functionalities, including real-time information and personalized interactions, with a more realistic and casual voice.

Google Assistant

Google Assistant is an AI-powered virtual assistant predominantly available on mobile and smart home devices. It can engage in two-way conversations, utilizing Google’s deep learning neural network, known as Google Neural Machine Translation, as part of its AI.

Users can ask Google Assistant to carry out tasks like sending texts, playing music, or providing weather updates. It’s also integrated with various smart devices and can control smart home appliances.

Learn more about AI/ML-powered chatbot development and how it can help your particular business

Code development
Large language models can assist programmers in writing, reviewing, and debugging code. These models can understand and generate code snippets, suggest completions, and even write entire functions based on brief descriptions. For instance, a developer might input a comment like “sort a list of numbers in ascending order,” and the LLM can provide the corresponding code.
Furthermore, LLMs can translate code between different programming languages, making it easier for developers to work with unfamiliar syntax or migrate projects to a new language.

Real-world apps
StarCoder is among the most popular LLM applications designed to make developers’ lives a little bit easier. Let’s dive deeper into it.

StarCoder

StarCoder is a collaborative effort between Hugging Face and ServiceNow. This open-source LLM is trained on a diverse and extensive dataset sourced from GitHub, which includes a wide array of programming languages, Git commits, GitHub issues, and Jupyter notebooks. The model itself is substantial, with approximately 15 billion parameters and training on 1 trillion tokens, with further fine-tuning on 35 billion Python tokens.

In terms of performance, StarCoder has been demonstrating strong capabilities in various coding tasks. It can handle a large context length of over 8,000 tokens, which is particularly useful for understanding and generating extensive code sequences. This makes it suitable for code autocompletion, modification, and providing explanations in natural language.

One of the notable features of StarCoder is its multilingual support, allowing it to understand and generate code in over 80 languages.

Sentiment analysis
Large language models applications can be utilized for sentiment analysis, thanks to their deep understanding of language nuances and context. Trained on extensive datasets, they can quite accurately determine the sentiment behind texts, ranging from social media posts to customer reviews.
LLM applications work by classifying the text into categories such as positive, negative, or neutral, often accompanied by associated confidence scores. For instance, in customer feedback analysis, large language models can discern specific emotions or attitudes towards products or services. This enables businesses to gain valuable insights into customer satisfaction and tailor their strategies accordingly.

Customer sentiment analysis from A to Z is right here, offering comprehensive insights to build your own strategy and drive your business forward

Real-world apps
As we delve into the real-world LLM use cases for sentiment analysis, notable examples include Grammarly and its tone detector feature.

Grammarly

Grammarly is a widely used writing enhancement tool and a browser extension. It provides grammar and spell checking as well as plagiarism detection services, among others, to ensure that the text is clear and mistake-free. At its core, Grammarly uses powerful large language models to understand the context and offer suggestions to enhance writing style, tone, and clarity.

One of the innovative features of Grammarly is its tone detector. It analyzes the text for sentiment and emotional cues, categorizing the tone into various labels such as disheartening, curious, formal, accusatory, disapproving, assertive, joyful, or optimistic, and others. It helps you understand how your message might be perceived by the reader and allows you to adjust the tone accordingly.

For businesses, this feature is invaluable in maintaining brand voice consistency, enhancing customer communication, and making sure that the messages align with the intended sentiment, whether it’s professional, supportive, or friendly.

Question answering
Question answering is among typical and very widespread LLM applications. These models easily understand and generate human-like text, making them ideal for providing accurate and contextually relevant answers to a wide array of questions.
Users can interact with large language models through search engines, virtual assistants, customer service bots, or educational platforms.

Real-world apps
Let’s explore an example of how LLMs are utilized for question answering from Meta.

LLaMA

LLaMA, short for Large Language Model Meta AI, is trained on a vast corpus of 1.4 trillion tokens, enabling it to predict and generate text by taking a sequence of words as input. This is why LLaMA is particularly good at answering questions across various domains, understanding context, and providing accurate, relevant information.

Its versatility allows it to be fine-tuned for specific tasks, especially sophisticated question answering scenarios. By offering LLaMA in various sizes and sharing its code, Meta aims to make AI research more accessible and encourage further work on enhancing large language models for better problem-solving and question-answering.

Market research
Large language models are able to provide deep insights into consumer behavior, trends, and preferences. They can analyze customer feedback, identify patterns and sentiments, predict market trends, and generate reports summarizing complex data into actionable insights.
For instance, an LLM can evaluate thousands of product reviews to determine the most appreciated features or common complaints, guiding companies in product development and marketing strategies.

LLMs are capable of performing extensive research on competition surrounding specific products or services. They can track the evolution of trends, benchmark against competitors, and provide strategic insights for positioning and innovation.

Explore machine learning pattern recognition for market research and beyond, unlocking new possibilities in data analysis and strategic decision-making

Real-world apps
Let’s see how real-world applications like Brandwatch and Talkwalker are leveraging large language models to transform data into useful business insights.

Brandwatch

Brandwatch is a digital consumer intelligence platform that utilizes AI and machine learning developments to analyze online conversations and provide insights for market research.

Brandwatch provides access to an extensive pool of online consumer discussions, encompassing social media, blogs, forums, and news sites. This vast dataset enables the platform to conduct thorough sentiment analysis, trend spotting, and brand perception studies. By harnessing this data, businesses can rapidly respond to market shifts, understand consumer needs and opinions, and identify emerging trends.

Talkwalker

Talkwalker is a market research tool that provides real-time, data-backed responses to critical management questions. It combines existing customer data with social intelligence to understand which products consumers love or dislike, creating a holistic view of a company’s performance and identifying key factors about products.

Leveraging social intelligence, Talkwalker allows companies to tap into a vast pool of consumer opinions and trends, helping them stay ahead in a fast-moving market environment. This tool is particularly useful for conducting product listening and collecting customer product feedback, both crucial for maintaining a competitive edge and fostering product development.

Education & research
LLMs for education
LLM applications are increasingly being used in education to personalize learning and provide tutoring.

LLMs can adapt to individual student’s learning styles and pace, offering customized explanations and feedback. For instance, a model can generate interactive reading materials that adjust complexity based on the student’s comprehension level or provide real-time language translation to aid foreign students.

Just like being virtual tutors, LLMs can answer students’ questions, guide them through problem-solving steps, and even motivate them with encouraging messages.

Furthermore, LLM applications are well-suited for research, offering new ways to analyze data and explore theories.

Real-world apps
Unsurprisingly (or surprisingly for some?), the ubiquitous Duolingo owl has made a savvy move by enhancing its educational services through LLMs.

Duolingo

Duolingo has introduced Duolingo Max, a new subscription plan that leverages the power of GPT-4. It offers two new features: “Explain My Answer” and “Roleplay.”

“Explain My Answer” allows learners to receive detailed explanations about their responses in lessons, helping them understand why an answer was right or wrong and giving further clarification and examples.

On the other hand, “Roleplay” enables learners to practice real-world conversation skills with virtual characters, offering a variety of scenarios to engage in. This interactive feature provides AI-powered feedback on the accuracy and complexity of responses, along with tips for future conversations. It can effectively mimic real-life interactions and improve students’ conversational skills.

These innovations, powered by LLMs, represent a significant step forward in personalized and interactive language learning, making Duolingo an even more powerful tool for students worldwide.

MNIST-1D

MINIST-1D is a low-memory and low-compute alternative to traditional deep learning benchmarks. Even though it has only 40 dimensions and a dataset of 4,000 entries, MINIST-1D is incredibly versatile.

It’s used to explore inductive biases of different architectures, study phenomena like deep double descent, uncover “lottery ticket” network configurations, metalearn an activation function, and illustrate guillotine regularization in self-supervised learning scenarios.

You can run all these experiments on a GPU or even just a CPU in just a few minutes, making it quick and easy to prototype, teach, and conduct cutting-edge research without breaking the bank.

Classification
LLM applications are highly effective in classification tasks across various domains. By converting raw text into categories, LLMs streamline processes, enhance decision-making, and provide valuable insights in fields ranging from content moderation and customer service to medical research and legal analysis.
In text classification, for example, they can sort documents into categories such as spam or not spam, perform sentiment analysis (positive, negative, neutral), or identify topics (sports, politics, technology). In healthcare, they can classify medical notes into relevant categories for diagnosis or treatment plans.

Real-world apps
Let’s examine Cohere Classify, a real-life LLM application, revolutionizing how businesses handle their text data.

Cohere Classify

Cohere Classify leverages machine learning techniques and large language models to analyze and categorize text, which, for instance, can simplify the process of responding to customer support requests.

Another possible application of such classification is sentiment analysis. Cohere Classify can identify positive and negative social media posts and reviews, and thereby help businesses understand and respond to customer sentiment effectively.

As for content moderation, it identifies and filters out hate speech, abusive language, spam, and profanity, maintaining the quality and safety of online platforms.

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Conclusion
From enhancing search capabilities with Bard and generating content with ChatGPT to revolutionizing language learning with Duolingo, the applications of large language models are not just futuristic concepts but practical tools already driving business innovation.

At PixelPlex, we truly understand the potential of these technologies and, moreover, already have extensive experience in building LLM-powered platforms. Turn to our artificial intelligence development services, tailored to meet the unique needs of your business.

Whether you’re aiming to integrate advanced AI/ML-driven strategies, optimize workflows with intelligent bots, or gain competitive insights with data analytics and computer vision solutions, our team of experts is ready to guide you through your transformation journey. Let’s embark on it together!

Bài viết 2: 12 Practical Large Language Model (LLM) Applications \n \n Skip to the content

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Artificial Intelligence
Machine Learning
12 Practical Large Language Model (LLM) Applications

by Technology SpecialistTim Keary
Updated on 19 January 2024
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KEY TAKEAWAYS
Large language models (LLMs) offer some compelling use cases for enterprises from translation and transcription to malware analysis, market research, and customer support.

Large language models (LLMs) are quickly becoming one of the most-hyped technological innovations in the Internet age.

In fact, researchers estimate that generative AI will become a $1.3 trillion market by 2032 as more users experiment with generative AI solutions like ChatGPT, Google Bard, and Bing Chat.

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

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.

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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.

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.

It’s worth noting that there are also tools like DALL-E, MidJourney, and Stable Diffusion that allow users to generate images based on a written prompt.

Search
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.

Virtual Assistants and Customer Support
Generative AI also appears promising in the realm of customer support as virtual assistants.
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.

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.

Code Development
Generative AI tools not only have the ability to generate natural language, but can generate code in languages like JavaScript, Python, PHP, Java and C#.
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.

Transcription
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.

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.

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.

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.

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.

Bài viết 3: 7 LLM use cases and applications in 2024 \n \n Learn about the top LLM use cases and applications in 2024 for streamlining business operations, automating mundane tasks, and tackling challenges.

7 LLM use cases and applications in 2024
Jesse Sumrak's picture
Jesse Sumrak
Featured writer

Mar 11, 2024
Large Language Models (LLMs) are powerful models reshaping how we interact with machines—streamlining business operations, automating mundane tasks, and uncovering deep insights faster than ever.

Gone are the days when AI's responses were rigid and predictable. Now, LLMs can create content, write stories, solve complex problems, and even mimic human-like conversations with uncanny accuracy.

Below, we'll walk you through all the top LLM use cases and applications in 2024.

#What is an LLM (and what do you use it for)?
Large Language Models decipher and generate human language on a massive scale. These sophisticated algorithms process and understand text in ways that mirror human cognition.

LLMs operate on neural networks designed to mimic the human brain's way of learning. These models undergo extensive training processes, absorbing vast datasets of text to learn the nuances of language. Through this process, one of the common tasks LLMs are trained to achieve is predicting the next word in a sentence. Equipped with this skill, LLMs can understand natural language well enough to comprehend context from natural language and generate coherent responses to prompts.

LLMs have shifted how we approach information, communication, and creativity. Their development from basic models to complex systems like GPT-4 illustrates a growing capacity that stretches beyond mere text processing to encompass a broad spectrum of applications:

Versatility of applications: Apart from being good at simple tasks, LLMs can achieve varied tasks on natural language like text generation and summarization and eventually be used for applications in medical or legal areas.
Human productivity boost: LLMs free up valuable time for creative and strategic thinking by streamlining tasks like content creation, coding, data analysis, and research, as well as more tedious, repetitive types of everyday tasks.
Accessibility: LLMs serve as a bridge between the vast amounts of data available and actionable insights (making information more accessible and manageable).
Interactivity at scale: LLMs lend themselves well to creating interactive tools and have given rise to advanced chatbots, virtual assistants, and educational tools. These applications leverage the models' language understanding to provide users with personalized and engaging experiences.
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However, accessing and using these LLMs for practical application can be challenging.

LeMUR (Leveraging Large Language Models to Understand Recognized Speech) is a cutting-edge platform that acts as a bridge between users and a wide array of LLMs, making it easier for users and businesses to build with LLMs. This tool provides a unified interface to enable developers and businesses to leverage the power of LLMs without the need for extensive AI expertise or resources.

With a single API call and a few lines of code, you can use LeMUR to accomplish complicated tasks for your audio-driven LLM apps:

Summarize video meetings
Ask questions about phone calls or podcasts
Extract valuable insights
Generate a list of action items from virtual meetings
Discern call outcomes like sales, appointments, or call purposes
Define your own tasks and prompts
#Top LLM use cases and applications
From transforming how we interact with digital content to revolutionizing customer service, LLMs are reshaping how businesses operate and how we go about our daily lives. Their versatility enables everything from brand-new products and services to improved operational efficiencies.

Below, we look at some of the most compelling LLM use cases and applications in 2024 (and beyond):

Audio data analysis
LLMs are redefining how we handle audio data, turning hours of recordings into actionable insights we can use. These models unlock new levels of efficiency by generating summaries, extracting key points, and answering queries about meetings, phone calls, videos, or podcasts.
Corporations can use audio data analysis to streamline decision-making processes based on comprehensive meeting insights. They can examine massive amounts of sales calls to learn what's working (and what's not) and quickly learn about real customer concerns and questions through support channels.

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2. Content creation
LLMs empower writers, marketers, and creatives by generating initial drafts, suggesting edits, or producing complete articles, reports, and creative works. This functionality accelerates the content creation process and allows humans to focus on strategic and creative aspects rather than the mechanical elements of writing.

It's a game-changer for content-heavy industries, boosting productivity without compromising quality.

Customer support
LLMs are transforming customer support by providing automated (yet deeply personalized) responses to inquiries. This technology enables businesses to offer 24/7 support without extensive human resources, improving customer satisfaction and operational efficiency.
Beyond handling common queries, LLMs can understand context, analyze sentiment, and escalate complex issues. This ensures a seamless support experience without a massive investment.

Language translation and localization
LLMs help break down language barriers and empower your business to reach a global audience. These models offer real-time, accurate translation and localization services, making websites, applications, and digital content universally accessible.
LLMs try to understand cultural nuances to provide localized content that's both translated and contextually appropriate. This enables businesses to facilitate global expansion and cross-cultural communication at scale.

Customer sentiment analysis
LLMs can be trained to understand textual and voice sentiment to better respond to customer concerns and needs. They can also be used to analyze customer feedback, reviews, and social media mentions at scale to gain insights into public perception and emerging trends.
This LLM use case helps with everything from reputation management to product development and marketing strategies—helping your business stay ahead of the curve.

Education and training
LLMs can be used to provide personalized education and training by adapting content to meet the individual needs of learners. They can offer tutoring, generate practice questions, and offer explanations tailored to what students are learning (or struggling with).
This LLM use case makes education more accessible and practical. It helps create everything from textbooks to interactive online courses, democratizing education for learners worldwide.

Cybersecurity
Businesses can use LLMs to analyze and interpret large volumes of cybersecurity data to better anticipate, identify, and respond to potential security threats. LLMs learn the context and nuances of cybersecurity alerts to enable faster, more accurate threat detection and response—strengthening the security posture of your business.
#How to drive operational efficiency with LLMs
Operational efficiency isn't just a goal—it's a necessity. Fortunately, LLMs can help you streamline operations, enhance productivity, and foster innovation without sacrificing your budget or other to-dos.

Your business can seamlessly integrate LLMs into its workflows to automate routine tasks, improve decision-making processes, and stimulate creativity. Here's how:

Automate routine tasks
Your business can leverage these models to manage emails efficiently, auto-generate reports, and handle customer service inquiries without human intervention. This automation extends beyond simple tasks to allow for the dynamic generation of content that meets specific guidelines or answers complex customer queries with high accuracy.

Your company can significantly enhance its productivity and operational efficiency by using LLMs to reallocate human resources from routine tasks to more strategic roles.

Improve decision-making
Data is the lifeblood of modern business strategy, yet the sheer volume of information can overwhelm even the most robust analytics teams. LLMs excel in digesting, summarizing, and analyzing large datasets, extracting actionable insights to inform business decisions.

Whether summarizing market research, conducting sentiment analysis on customer feedback, or synthesizing financial reports, LLMs can provide your team with a distilled view of vast data landscapes. This capability supports better-informed decision-making to ensure your strategies are grounded in comprehensive data analysis.

Boost creativity and innovation
One of the most exciting applications of LLMs is their ability to augment human creativity and innovation. LLMs can suggest novel ideas, propose alternative solutions to problems, and inspire creative content generation. This process isn't just about generating content or ideas out of thin air—it's about enhancing the creative process with diverse, AI-driven perspectives.

For businesses, this means the potential to uncover innovative solutions to challenges, create captivating marketing content, or even devise entirely new products or services that could redefine the market.

#Start building LLM applications on your voice data
LLMs are crucial to unlocking unprecedented operational efficiencies, enhancing decision-making, and stimulating creativity across various sectors. While we've covered a few of the most prevalent LLM use cases and applications, it's just scratching the surface—there are many ways (explored and unexplored) to use these advanced AI models to streamline and transform your business.

Bài viết 4: 35 real-world LLM applications from top companies \n \n In the past few years, top companies have invested in integrating large language models (LLMs) into their products. As LLMs become more affordable and their performance improves, we’ve witnessed some astonishing use cases, from generative coding assistants to content creation and product ideation.

Indeed, LLMs are powerful. But transforming their “magic” into reliable and effective production-grade systems is easier said than done.

In this blog, we compiled 35 real-world examples of how companies apply LLMs and their learnings from building LLM systems in production.

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We put together and regularly update a database of 450 use cases from 100+ companies that detail real-world applications and insights from ML and LLM system design.

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E-commerce and retail
🛒 Instacart builds an internal AI assistant called Ava
Teams in Instacart use Ava to write, review, and debug code, improve communications, and build AI-driven internal tools using the company's APIs.

Explore the use case:
Scaling Productivity with Ava — Instacart’s Internal AI Assistant

🛍 Whatnot identifies marketplace spam
A social marketplace Whatnot employs LLMs to enhance multimodal content moderation, fulfillment, bidding irregularities, and fraud protection.

Explore the use case:
How Whatnot Utilizes Generative AI to Enhance Trust and Safety

🗞 OLX extracts details from job listings
Online marketplace OLX created Prosus AI Assistant, a generative AI model, to identify job roles in ads, ensuring better alignment between job seekers and relevant listings.

Explore the use case:
Extracting Job Roles in Job Ads: A Journey with Generative AI

🪡 StitchFix creates ad headlines and product descriptions
An online personal styling service StitchFix combines algorithm-generated text with human oversight to streamline the creation of engaging ad headlines and high-quality product descriptions.

Explore the use case:
A New Era of Creativity: Expert-in-the-loop Generative AI at Stitch Fix

🏠 Zillow identifies discriminatory content in real estate listings
Zillow uses LLMs to detect proxies for race and other historical inequalities in real estate listings on their marketplace.

Explore the use case:
Using AI to Understand the Complexities and Pitfalls of Real Estate Data

📺 Leboncoin improves search relevance
French marketplace Leboncoin uses LLMs to improve search relevance by sorting ads in the optimal order regarding a user’s query.

Explore the use case:
Serving Large Language Models to improve Search Relevance at leboncoin

🛠 Mercado Libre builds an internal tool that answers technical queries
Mercado Libre uses LLMs in a variety of use cases: from an internal tool that answers questions about the company’s technical stack to documentation generation. The company shares a behind-the-scenes look at the practical aspects of developing LLM-based applications and the insights they gained along the way.

Explore the use case:
Beyond the Hype: Real-World Lessons and Insights from Working with Large Language Models

🏬 Faire helps retailers discover products for their stores
Online wholesale marketplace Faire shares how they improve search relevance with LLMs: define semantic relevance, build visibility of relevance in their search ecosystem, and make relevance an actionable dimension to consider in search.

Explore the use case:
Fine-tuning Llama3 to measure semantic relevance in search

👗 Amazon improves product recommendations
The Amazon Store uses LLMs to discern commonsense relationships and provide product recommendations most relevant to customers’ queries.

Explore the use case:
Building commonsense knowledge graphs to aid product recommendation

📖 Wayfair provides live chat response recommendations
E-commerce company Wayfair developed Agent Co-pilot, a Gen-AI assistant for digital sales agents. Co-pilot provides live, contextually relevant chat response recommendations that sales agents can use while chatting with customers.

Explore the use case:
Agent Co-Pilot: Wayfair's Gen-AI Assistant for Digital Sales Agents

Fintech and banking
🔢 Grab tags sensitive data
Asian super-app Grab uses LLMs in data governance: to classify data entities, identify sensitive information, and assign appropriate tags to each entity.

Explore the use case:
LLM-powered data classification for data entities at scale

⚡️ SumUp generates financial crime report narratives
Card payments processing company SumUp shares how they evaluate the performance of an LLM application that generates unstructured data – free-text narratives in the context of financial fraud and money laundering.

Explore the use case:
Evaluating the performance of an LLM application that generates free-text narratives in the context of financial crime

💳 Digits suggests queries about banking transactions
Digits uses generative models to help accountants by suggesting transaction-related questions to ask a client. The questions can be sent right away or edited before sending.

Explore the use case:
How Digits is using Generative Machine Learning to transform finance

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Tech
🗝 Google enhances workflows for security and privacy incidents
Google leverages LLMs to provide incident summaries for various audiences, including executives, leads, and partner teams. This saves responders’ time and enhances the quality of incident summaries.

Explore the use case:
Accelerating incident response using generative AI

🤖 GoDaddy classifies support inquiries
GoDaddy utilizes LLMs to improve customer experience by classifying support inquiries in their messaging channels. They wrote a blog on operationalizing these models.

Explore the use case:
LLM From the Trenches: 10 Lessons Learned Operationalizing Models at GoDaddy

🔢 Honeycomb assists users in writing data queries
Software observability company Honeycomb developed Query Assistant to help users craft queries by describing their needs in plain English, which the assistant then translates into relevant Honeycomb queries.

Explore the use case:
So We Shipped an AI Product. Did it Work?

⚠️ Incident.io generates software incident summaries
Incident.io facilitates collaboration on software incidents by suggesting and updating incident summaries. LLM-powered suggestions take into account the latest incident updates, Slack channel conversations, and previous incident summaries.

Explore the use case:
Lessons learned from building our first AI product

💻 GitHub’s Copilot code suggestions and autocompletions
The team behind GitHub Copilot shares their learnings from working with OpenAI’s LLM and how it guided the development of Copilot, an AI-powered code completion tool.

Explore the use case:
How to build an enterprise LLM application: Lessons from GitHub Copilot
Inside GitHub: Working with the LLMs behind GitHub Copilot

✅ Microsoft diagnoses production incidents
Microsoft also uses LLMs to help manage cloud incidents: generate recommendations for incident root cause analysis and mitigation plans.

Explore the use case:
Large-language models for automatic cloud incident management

🧭 Thoughtworks builds AI copilot for strategy
Thoughtworks built an experimental AI co-pilot for product strategy and generative ideation called “Boba”. They share some lessons and patterns learned in building an LLM-powered generative application.

Explore the use case:
Building Boba AI

💬 Salesforce summarizes Slack conversations
Salesforce introduced AI Summarist, a conversational AI-powered tool, which summarizes Slack conversations and helps users manage their information consumption based on their work preferences.

Explore the use case:
AI Summarist: Get Your Time Back on Slack, Boost Productivity & Focus, Personalize Information Consumption

🕵️ NVIDIA detects software vulnerabilities
NVIDIA developed a generative AI application that determines if a software vulnerability exists and generates a checklist of tasks to thoroughly investigate it.

Explore the use case:
Applying Generative AI for CVE Analysis at an Enterprise Scale

🏗 GitLab transforms internal engineering workflows
GitLab leverages a suite of AI-powered features – GitLab Duo – to streamline development processes, reduce manual effort, and enhance developer productivity. The company shares real-world examples of how it integrates AI throughout the software development lifecycle and measures success.

Explore the use case:
Developing GitLab Duo: How we are dogfooding our AI features
Developing GitLab Duo: How we validate and test AI models at scale

🧑‍🔧 Replit builds LLM for code repair
Replit, a company developing AI-powered software development tools, finetunes LLMs to help developers fix bugs in software.

Explore the use case:
Building LLMs for Code Repair

Delivery and mobility
🚗 Uber tests mobile applications
Ubers uses LLMs in software testing. They developed DragonCrawl, a system that utilizes LLMs to perform mobile tests with human-like intuition, saving developer hours and reducing test maintenance costs.

Explore the use case:
DragonCrawl: Generative AI for High-Quality Mobile Testing

🏪 Picnic boosts search relevance for product listings
Picnic online supermarket app usess LLMs to refine product and recipe search results for users in three countries, each with distinct languages and culinary tastes.

Explore the use case:
Enhancing Search Retrieval with Large Language Models (LLMs)

📦 DoorDash extracts product details from SKU data
Food delivery company DoorDash uses LLMs to identify and tag product attributes from raw merchant data. This helps improve the matching of customer queries with relevant items and aids delivery drivers in locating products.

Explore the use case:
Building DoorDash’s Product Knowledge Graph with Large Language Models

🥙 Swiggy builds conversational search
Online food ordering and delivery company Swiggy implements AI-powered neural search. This helps users discover food and groceries in a conversational manner and receive custom recommendations.

Explore the use case:
Swiggy’s Generative AI Journey: A Peek Into the Future

🎯 Delivery Hero matches their inventory with competitors’ products
Delivery Hero, a multinational online food ordering and food delivery company, solves the product-matching problem using LLMs. Identifying products in their inventory that are similar to those offered by competitors allows the company to develop more informed pricing strategies and better understand the differences in product variety on the market.

Explore the use case:
Semantic Product Matching

Social, media and B2C
👷 LinkedIn extracts skill data from texts
Linkedin analyzes various content across the platform to extract skill information. Skill data then goes to LinkedIn’s Skills Graph, which dynamically maps the relationships between skills, people, and companies, ensuring relevant job and learning matches.

Explore the use case:
Extracting skills from content to fuel the LinkedIn Skills Graph

🙅 Yelp detects inappropriate language in reviews
Yelp, an online reviews platform, upgraded its content moderation system with LLMs to detect threats, harassment, lewdness, personal attacks, or hate speech.

Explore the use case:
Yelp’s AI pipeline for inappropriate language detection in reviews

💌 Nextdoor crafts engaging email subject lines
Nextdoor, the neighborhood network app, uses LLMs in marketing content creation. They use LLM to generate informative and captivating subject lines and boost email opens, clicks, and subsequent platform sessions.

Explore the use case:
Let AI Entertain You: Increasing User Engagement with Generative AI and Rejection Sampling

🍿 Vimeo builds a customer support AI assistant
Vimeo, a video hosting platform, prototyped a help desk chatbot that provides immediate, accurate, and personalized responses to customer inquiries.

Explore the use case:
From idea to reality: Elevating our customer support through generative AI

🧑‍🏫 Duolingo creates content for lessons
Education technology company Duolingo uses LLMs to help learning designers generate relevant exercises. Human experts outline the theme, grammar, vocabulary, and exercise types, while the model produces suitable exercises.

Explore the use case:
How Duolingo uses AI to create lessons faster

📝 Grammarly detects delicate text
Creators of AI-powered writing assistant Grammarly use LLMs to protect users from harmful conversations. The company goes beyond toxicity and detects delicate text—text that is emotionally charged or potentially triggering and poses a risk for users or LLM agents, such that engaging with it can result in harm. \n \n Bài viết 5: 15 Real-World Examples of LLM Applications Across Different Industries

In the dynamic world of technology, Large Language Models (LLMs) have become pivotal across various industries. Their adeptness at natural language processing, content generation, and data analysis has paved the way for numerous applications. Let’s explore 15 detailed examples of how companies harness LLMs in real-world scenarios.

Netflix: Evolving Big Data Job Remediation
Netflix has shifted from traditional rule-based classifiers to machine learning-powered auto-remediation systems for handling failed big data jobs. This transition has enabled Netflix to automatically detect, diagnose, and fix issues in its data pipelines, significantly reducing downtime and ensuring seamless streaming services. The LLMs help understand log data, identify failure patterns, and suggest or implement fixes, thus enhancing operational efficiency and reliability.

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Picnic: Enhancing Search Retrieval
Picnic, an online grocery delivery service, has integrated LLMs to improve the relevance of search results for product listings. Using large language models allows Picnic to better understand user queries and context, delivering more accurate and personalized search outcomes. This improvement enhances the customer experience and increases conversion rates by helping customers.

Uber: Personalized Out-of-App Communications
Uber’s advanced recommendation system personalizes out-of-app communications to enhance user engagement. Uber can tailor notifications and suggestions to individual user preferences and behaviors using sophisticated LLMs-powered recommender algorithms. This personalization extends beyond the app, ensuring that users receive relevant updates and offers via email, SMS, and other channels, thus improving user retention and satisfaction.

GitLab: Validating and Testing AI Models
GitLab has developed GitLab Duo, a platform that validates and tests AI-generated outputs. This initiative uses LLMs to assess the quality, accuracy, and reliability of AI models at scale. GitLab Duo helps identify potential biases, errors, and areas for improvement in AI models, ensuring that the deployed models meet high performance and reliability standards. This rigorous testing process is crucial for maintaining trust in AI-driven features.

LinkedIn: Matching Members with Premium Products
LinkedIn employs LLMs to recommend relevant premium products to its users. LinkedIn’s recommendation system can match members with the premium services and products that best suit their needs by analyzing user data, including professional history, interests, and activity patterns. This targeted approach helps LinkedIn enhance user satisfaction and drive subscriptions to its premium offerings.

Swiggy: New-User Product Recommendations
Swiggy, a leading food delivery platform, utilizes hierarchical cross-domain learning to provide product recommendations to new users. By analyzing data from various domains and learning from user interactions, Swiggy’s recommendation system can offer personalized suggestions that cater to new users’ preferences. This approach effectively onboards new customers, increasing engagement and retention rates.

Careem: Reducing Fraud with Pre-Authorization
Careem, a ride-hailing service, leverages machine learning models to mitigate fraud risks through pre-authorization techniques. Careem can analyze transaction patterns and flag suspicious activities in real-time by implementing temporary holds on transactions. This proactive fraud detection mechanism, powered by LLMs, helps reduce fraudulent incidents, protecting the company and its users from potential losses.

Slack: AI for Secure Corporate Messaging
Slack has developed AI capabilities that enhance secure and private corporate messaging. Utilizing LLMs, Slack’s AI features can process and analyze messages while ensuring high standards of security and privacy. These features include automated message summarization, smart replies, and context-aware suggestions designed to improve communication efficiency without compromising data protection.

Picnic: Supporting Customer Requests
Picnic has broken customer support language barriers using natural language processing (NLP). By routing support requests to the most suitable agents and providing real-time language translation, Picnic ensures that customers receive timely and accurate assistance regardless of language. This NLP-driven support system enhances customer service quality and helps Picnic cater to a diverse customer base.

Foodpanda: Optimizing Demand and Supply
Foodpanda employs machine learning to balance demand and supply for food delivery services. Using predictive analytics and advanced algorithms, Foodpanda can forecast demand patterns and allocate resources. This optimization helps manage delivery times, reduce operational costs, and ensure a better customer and delivery partner experience.

Etsy: Visual Search and Recommendations
Etsy has implemented visual representation learning and evaluation techniques for visual search and similar recommendations. By leveraging computer vision and LLMs, Etsy’s system can analyze product images and provide users with visually identical items. This feature enhances the shopping experience by making finding products that match users’ preferences based on visual attributes easier.

LinkedIn: Detecting AI-Generated Images
LinkedIn has developed systems to detect AI-generated (deepfake) images. Utilizing advanced image recognition algorithms and LLMs, LinkedIn can identify and flag deepfake content, ensuring the integrity and trustworthiness of user-profiles and content on the platform. This capability is crucial in maintaining a secure and authentic user environment.

Discord: Generative AI Use Cases
Discord, a popular communication platform, has explored various generative AI use cases to enhance user engagement. Discord can offer users creative tools such as AI-generated avatars, content moderation, and automated responses by rapidly developing and integrating generative AI features. These features leverage LLMs to improve user experience and foster a more interactive community.

Pinterest: Improving Ads Performance
Pinterest has evolved its ad conversion optimization models to enhance ad performance. By leveraging LLMs, Pinterest can analyze user behavior and preferences to deliver highly targeted & relevant ads. This optimization leads to higher conversion rates, better user experiences, and increased revenue for advertisers on the platform.

Expedia: Semantic Search for Travel
Expedia uses embeddings for lodging travel concepts to enhance its semantic search capabilities. By understanding the contextual meaning of user queries, Expedia’s search system can provide more accurate and relevant results for hotels and travel accommodations. This semantic search functionality, powered by LLMs, improves the booking experience by helping users find the best options based on their needs &preferences.

In conclusion, these examples illustrate the transformative impact of LLMs across various sectors, driving innovation and efficiency. As LLM technology advances, its applications are expected to expand, offering even more sophisticated solutions to industry challenges. Companies should consider leveraging dedicated platforms like AI Drive Pro to manage and optimize their LLM implementations for optimal results. \n \n Bạn là một giáo sư về Xử lý ngôn ngữ tự nhiên, đặc biệt là Mô hình ngôn ngữ lớn. Hãy tổng hợp toàn bộ thông tin trong 5 bài viết trên rồi viết 1 blog về Mô hình ngôn ngữ lớn đầy đủ, chi tiết, dễ hiểu, chính xác, thú vị trên 3000 từ bằng Tiếng Việt. Bổ sung thêm chi tiết chính xác để làm sáng tỏ vấn đề. Let's think step by step!