Large Language Model (LLM): Everything You Need to Know

What is an LLM?

A large language model (LLM) is an advanced type of artificial intelligence (AI) designed to process and generate human-like text. LLMs are built using deep learning techniques, specifically transformer-based architectures, and are trained on massive amounts of text data. Examples of LLMs include OpenAI’s ChatGPT, Google’s Gemini, and Meta’s LLaMA.

LLM Architecture Explained

LLM architectures are determined by a number of factors, like available computational resources, the specific LLM design objective, and the type of language processing tasks that are the goal. The general architecture of LLM models consists of many layers such as the feed forward layers, embedding layers, and attention layers. The layers collaboratively generate predictions and new text based on input.

Several factors influence LLM application architecture:

• decoding and output generation
• computational efficiency
• input representations
• model size and parameter count
• training objectives
• self-attention mechanisms

The specifics of LLM model architecture are influenced by the three main steps of the text analysis, prediction, and generation functions:

The transformer is the core of an LLM. LLM transformer architecture has multiple layers that analyze and generate text, in two main parts: the encoder layers used in tasks like translation and the decoder layers used in text generation. LLMs like ChatGPT primarily use the decoder layers.

During input embedding, the LLM converts text into tokens and maps them to numbers so the model can process them. The LLM represents the input as a continuous vector and captures its semantic and syntactic information.

The AI model adds positional encoding to input embeddings. This is a small mathematical adjustment that helps the system keep track of word order, since transformers don’t process text one word at a time like humans do and instead analyze all words at once.

Next, the inputs reach the two fundamental sub-components of each encoder layer: the self-attention mechanism and the feed-forward neural network.

The self-attention layer allows the model to compare the importance of different tokens in the input sequence and consider relationships and dependencies between different tokens in a context-aware manner. This allows the LLM to look at all words in a sentence and decide how they relate to each other and which ones are most important.

For example, where the inputs are, “She fed the hens some corn and vegetables. They were hungry.” The LLM understands that “They” in the sentence refers to the hens that were hungry, not the corn and vegetables.

The LLM applies a feed-forward neural network to each token independently after the self-attention step. This network of fully connected layers with non-linear activation functions allows the LLM model to capture complex interactions between tokens to make predictions.

For example, now that the model understands the context, it can predict the next words based on patterns it has learned. If it is starting with “The weather today is,” it will choose the most likely answer, such as “clear and warm.”

Some transformer-based models include a decoder component that enables autoregressive generation. This allows the LLM to generate sequential outputs based on previously generated tokens.

LLMs apply layer normalization after each layer or sub-component of transformer architecture. This ensures stable learning and improves generalization across different inputs, preventing drastic fluctuations in word importance.

The output layers of the transformer model vary based on the specific task. For example, a linear projection followed by SoftMax activation is commonly used in language modeling. The SoftMax model assigns probabilities to possible next words and picks the most likely one.

How LLMs Work

LLMs rely on neural networks, particularly transformer models, to analyze and predict text. Several steps make up the process of how large language models work at a high level:

Training. First, LLMs are trained on massive, diverse datasets, including books, articles, code, and web pages. As they train, they learn patterns, grammar, facts, and relationships between words and concepts.

Tokenization. During this step, LLMs break down text into smaller pieces called tokens which can be words, subwords, or characters. They then convert the tokens into numerical representations called embeddings that the model can process.

Prediction and generation. When the LLM receives input in the form of a sequence of words, the model predicts the most likely next word using probabilities. It then generates coherent and contextually relevant text based on context and inputs it has received in the past.

Fine-tuning and alignment. Some specialized LLMs receive additional fine-tuning for specific tasks. For example, they might be trained for coding, processing and providing medical knowledge, or producing legal text. LLMs might also be subjected to alignment techniques that are designed to help them respond in more ethical and useful ways. An example of this kind of training is reinforcement learning from human feedback (RLHF).

What is GPT (Generative Pre-Trained Transformer)?

A GPT meaning a generative pre-trained transformer is a specific type of large language model that is based on the transformer architecture and follows a distinct pretraining and fine-tuning process. While all GPTs are LLMs, not all LLMs are GPTs.

GPTs can be differentiated from LLMs more generally in a few ways:

Transformer-based architecture. GPTs specifically use decoder-only transformers trained using causal (autoregressive) language modeling. Other LLMs might use encoder-only transformers; for example bidirectional encoder representations from transformers (BERT), deploys encoder-only architecture for masked language modeling. Other models use encoder-decoder architectures, such as T5 for text-to-text transformations.

Generative and autoregressive nature. GPT models are generative, meaning they produce text sequentially, predicting the next token based on prior tokens. Other LLMs are trained for other tasks. For example, BERT is trained for text classification and T5 can handle sequence-to-sequence transformations.

Pretraining and fine-tuning approach. GPT models are pre-trained on a massive body of text using unsupervised learning, typically by predicting the next word in a sentence. They are then fine-tuned on specific datasets for applications like conversation, coding, or reasoning. Other LLMs may use different pre-training objectives such as masked word prediction in BERT.

Specialized in open-ended language generation. GPT models excel at generating human-like text in a free-form, open-ended way. Some LLMs (like BERT) are better suited for understanding and classification tasks rather than generative AI tasks.

LLM Benchmarks

LLMs are evaluated using benchmarks—standardized tests that measure their performance across different tasks. These LLM benchmarks help users compare different models and track improvements over time:

General language understanding benchmarks test how well an LLM model understands and processes language across multiple domains. There are two of these:

• General language understanding evaluation (GLUE). GLUE tests sentence-level tasks like sentiment analysis, text similarity, and question answering. For example, GLUE benchmarks can determine whether a sentence is expressing positive or negative sentiment: “Given the input: ‘Actually, the movie was not that amusing, and the jokes weren’t super witty, either.’, is the sentiment expressed positive or negative? The GLUE benchmark is best used for BERT, RoBERTa, and T5.
  
• Advanced Language Understanding (SuperGLUE). This benchmark is a more advanced version of GLUE with complex reasoning tasks. It can answer commonsense and cause and effect questions. For example, “Given the premise: ‘The tires screeched loudly, and the dog jumped with a start and scampered away.’, which of the following is the most plausible effect? A) The dog chased the car. B) The car braked to avoid the dog.”

The SuperGLUE benchmark is best used for advanced models like GPT-4, Claude, and Gemini. Both GLUE and SuperGLUE are critical for testing language understanding.

Knowledge and reasoning benchmarks test an LLM’s ability to recall facts and reason logically:

• Massive multitask language understanding (MMLU). This LLM benchmark covers 57 subjects, including science, history, math, and law. It is best for evaluating general knowledge and reasoning ability for tasks such as multiple-choice tests like SAT questions.
  
• Beyond the imitation game (BIG-bench). This is a large collection of creative and reasoning challenges designed to test high-level reasoning and AI alignment. For example, it can assess whether the AL model understands cause-and-effect relationships. Along with the massive multimodal understanding benchmark (MMMU), BIG-bench is among the most important tests for general intelligence.

Math and coding benchmarks test how well an LLM solves math problems and writes code:

• Grade school math 8K (GSM8K). This benchmark designed to test numerical reasoning contains basic to intermediate math problems for elementary school levels. For example, it might test whether the model can answer: “If a train moves at 60 mph, how long does it take to travel 120 miles?”
  
• Mathematical problem solving (MATH). This benchmark tests symbolic reasoning using advanced math problems from high school and competition-level exams on algebra, calculus, or geometry.
  
• Code generation (HumanEval). This LLM benchmark measures how well models like Codex (GPT-4 Turbo), StarCoder, and Gemini Code write and complete code. For example, it might ask the model, “Write a Python function to find prime numbers.”

Both GSM8K and MATH are the most important benchmarks for testing math and logic, while HumanEval is the most critical for testing coding ability.

Open-ended generation and chatbot performance benchmarks test how well an LLM generates text and interacts conversationally:

• Holistic evaluation of language models (HELm). This benchmark designed to assess ethical AI behavior evaluates models for bias, fairness, and misinformation in the context of real-world scenarios. It asks models to complete tasks such as generating responses to controversial topics without bias.
  
• Multi-turn chatbot benchmark (MT-Bench). MT-Bench tests multi-turn conversations for chatbots like ChatGPT, Claude, and Gemini to ensure the conversation is realistic and maintains the right context to compare chatbot performance.

Both MT-Bench and HELM are important for evaluating chatbot quality.

Multimodal benchmarks test LLMs that can process both text and images:

• Massive multimodal understanding benchmark (MMMU). This benchmark used to evaluate models like Gemini and GPT-4V tests models on image-text comprehension across multiple subjects. It might ask these models to identify objects in images with word based reasoning.
  
• Visual question answering (VQAv2). This benchmark designed for AI that combines vision and language measures how well models answer questions about images. For example, it might ask, “What is the color of the car in the picture?”

Along with the BIG-bench benchmark, MMMU is among the most important tests for general intelligence. Both the MMMU benchmark and VQAv2 are among the most important tests for multimodal AI models.

LLM Inference Explained

LLM inference refers to the process of running a large language model to generate responses based on a given input. In simpler terms, it’s the phase where the trained model is used (rather than trained) to make predictions, generate text, or complete tasks.

For example, the user offers a text prompt: “Explain quantum mechanics.” The LLM tokenizes the input into numerical representations and passes the tokens through multiple layers of transformer architecture.

It then processes each token using the self-attention and feedforward layers to predict the most likely next token. Models like ChatGPT use autoregressive generation to generate each successive next token at a time in sequence until a stopping condition is met—for example, until they reach a token limit or a stop word.

During post-processing, the output tokens are converted back into human-readable text. Additional steps like filtering, formatting, or temperature adjustments (to control randomness) can be applied.

LLM inference faces several key challenges. Generating long responses token-by-token comes with an inherent latency and the process can be slow. There is also a high computational cost to the process. Unlike simple machine learning models, LLMs are huge, which makes inference slow and resource-intensive because the model needs multiple layers of computation per token, processing happens in parallel across GPUs/TPUs, and large models consume a lot of memory and power.

Furthermore, the memory constraints presented by LLM inference are notable. Large models require high RAM and VRAM to store parameters. In addition, scalability is a challenge, in that serving many users simultaneously requires optimizations like batching, model quantization, or distillation.

To make LLM inference faster and cheaper, AI engineers can optimize the process in several ways:

• Quantization. Reducing model precision, for example from 32-bit to 8-bit.
• Distillation. Training smaller models that mimic larger ones.
• Efficient architectures. Using optimized transformer variants.
• Hardware acceleration. Running models on high-speed GPUs or TPUs.

Types of Large Language Models

While all large language models generate and process text, they can be classified into different types based on their architecture and training approach. The three most common types of LLM models are decoder-only transformers (such as ChatGPT), encoder-only transformers (such as BERT), and encoder-decoder transformers (such as T5).

Decoder-only transformer models
Decoder-only transformer models such as GPT-3, GPT-4, LLaMA, and Falcon use only the decoder part of the transformer to generate text by predicting the next word in a sequence. They are great at open-ended tasks like writing, coding, and answering questions, they are highly scalable, and they handle long conversations well.

However, these kinds of LLM models can also “hallucinate,” generating text that is incorrect even though it sounds confident. They also don’t explicitly learn bidirectional context, although they can infer it.

Encoder-only transformer models
Encoder-only transformer models such as BERT, RoBERTa, and DistilBERT use only the encoder part of the transformer to process text bidirectionally—meaning they read the whole sentence before making any predictions. This means they are much better for analysis and understanding language rather than generating text.

These encoder transformers are well-suited for sentiment analysis, search engines, and summarization. They are better at understanding context than models like GPT because they read and process the entire input at once. However, they are not designed to have open-ended conversations or generate text like GPT.

Encoder-decoder transformer models
Encoder-decoder transformer models such as T5, FLAN-T5, and mT5 (the multilingual version) use both the encoder and decoder components of the transformer. They can summarize, translate, and answer questions by converting all tasks into a “text-to-text” format. For example, such a model can translate:

“English to French: Hello → Bonjour”

or answer specific questions:

“Solve: 2+2 → 4”

However, this also means they cannot handle free-flowing conversations as well as structured tasks.

Hybrid models
Hybrid models combine the approaches and architectures of the three types of LLMs described above; Gemini, Claude, and Mistral are examples of hybrid LLM models.

They use multimodal training, processing not only text but also images and video, and are designed for better reasoning and accuracy than traditional LLMs such as pure GPT models. However, hybrid LLMs are also more computationally expensive compared to single-architecture models, and less evolved.

There are also other types of LLM models such as BLOOM, the first multilingual LLM, which has an architecture similar to GPT3.

Below we describe a few other kinds of LLMs and talk about how they can be used.

LLM transformers

LLM deep learning transformer architectures enable large language models like GPT-4, BERT, and Claude to understand, process, and generate human-like text efficiently. Transformer models process text using the three key components we discussed above: tokenization, self-attention, and feedforward layers.

Key characteristics of the LLM transformer explained:

Self-attention. This mechanism allows transformers in LLMs to weigh the importance of different words in a sentence when making predictions. This is crucial for understanding context, especially in long sentences.

Parallelization. Unlike traditional recurrent neural networks (RNNs), which process data sequentially, LLM neural networks can process multiple words at once, making them faster and more efficient.

Versatility. Transformers are not confined to tasks involving language. They can be applied to any problem involving sequential data, including tasks such as time-series forecasting or image recognition.

How do LLM transformers work compared to LLMs more broadly?
Transformers and LLMs generally have slightly different purposes, architecture design, applications, training, and output.

LLMs are built on various architectures, including transformers, and focus mainly on generating and understanding natural language. Transformers use a neural network architecture for a wider range of tasks, including language modeling, but also many other things.

LLMs may be based on different architectures, although many modern LLMs achieve state-of-the-art performance using transformer architecture. On the other hand, transformer architecture is a specific design based on self-attention.

LLMs are deployed for a wide range of NLP tasks, including text generation, summarization, sentiment analysis, and translation. Transformers are used for NLP, and many other tasks that demand sequential data processing, such as computer vision and speech recognition.

Multimodal LLM

A multimodal large language model is an AI system that can process and generate multiple types of data—not just text, but also images, audio, video, and other types of data. Traditional LLMs such as GPT-3 or BERT only work with text.

Multimodal LLMs can understand and connect different types of information, making them more versatile. The difference starts with tokenization and embedding.

Multimodal models combine separate AI components for different types of data. They break text into tokens just like traditional LLMs, but they convert other kinds of data in different ways. For example, multimodal LLMs convert images into pixel embeddings, and audio into spectrograms (visual representations of sound).

From this point, the model encodes embeddings for all data, and aligns them across data formats so similar ideas will match regardless of the data type. Transformer architecture allows for cross-modal attention, so the model can learn relationships between text, images, and other data types.

Depending on the task, a multimodal LLM can generate text, captions, images, or audio as output.

For example, a model designed to analyze images to create alt text and metadata might be given inputs like, “Describe this image” alongside various photos and generate outputs like, “A young woman sits next to a computer with her face in her hands, rubbing her temples as if she is in pain.”

Multimodal LLMs offer a more human-like understanding of context, because people process sights and sounds, too, not just text. They also make better AI assistants than other models because they can read documents with images, analyze charts, or describe photos. They are particularly well-suited for newer applications such as medical AI (X-ray analysis) or creative AI (image generation from text).

Autoregressive LLM

An autoregressive LLM generates text one token at a time, predicting the next token based on the previous ones. It follows a left-to-right sequence generation process, meaning it builds sentences step by step instead of looking at the entire context at once.

For example, with the prompt, “The dress is,” an autoregressive LLM splits the input text into tokens (“the,” “dress,” and “is”) and predicts what comes next based on training and the previous tokens.

Based on this example, the model might make these predictions:

• “black” (80%)
• “slinky” (10%)
• “on fleek” (7%)
• “hideous” (3%)

The model chooses “black”: “The dress is black,” and moves forward to the next token. The process repeats until the full sentence or response is generated.

This is ideal for open-ended text generation and it works well for chatbots, writing, and coding. The scalable flexibility of autoregressive LLMs allow them to generate anything from short responses to long documents, and this is why popular models like GPT-4, GPT-3, and LLaMA all use this method.

Encoder and Decoder LLM

As discussed above, large language models come in three main architectures, each optimized for different applications: encoder-only LLMs for understanding text, decoder-only LLMs for generating text, and encoder-decoder LLMs for tasks requiring both functions.

Encoder-only LLMs process the entire input text at one time, engaging in bidirectional reading. They extract the meaning of the text, and detect and classify patterns in it. They do not generate text, however.

Encoder vs decoder LLMs work by consuming the entire sentence simultaneously and understanding relationships between words. They rely on self-attention to capture context from both past and future words.

Use cases for encoder-only LLMs include search engines and answering questions generally; sentiment analysis; information retrieval; and text classification such as document analysis or spam detection. Examples of these LLMs include Google’s BERT; RoBERTa, a stronger version of BERT; and DeBERTa, which focuses on deeper contextual understanding.

Decoder-only LLMs, intuitively, are text generation models. They are autoregressive, and as described above, they generate text one token at a time. They do not process full context at once like encoders.

Decoder-only LLMs are used for chatbots, story and article generation, AI writing assistants, text-based gaming NPCs, and code generation. Examples of this kind of LLM include OpenAI’s GPT-3 and GPT-4; Meta’s LLaMA, an open-source alternative to GPT, and Anthropic’s Claude, which is optimized for long conversations.

Hybrid encoder and decoder LLMs both understand and generate text. They are bidirectional and autoregressive. They are generally used for translation, summarization, and structured text tasks. First the encoder processes the input and extracts meaning, and then the decoder responds, generating output based on the encoded meaning.

Encoder-decoder LLMs are used for machine translation, text summarization, paraphrasing and text rewriting, and similarly structured tasks. Examples of encoder-decoder LLMs include Google’s T5, which is designed for summarization, translation, and text transformation; FLAN-T5, which is fine-tuned for reasoning; and BART, a hybrid model for text generation and summarization.

Fine-Tuned LLM

A fine-tuned LLM is a pre-trained large language model that has been further trained—fine-tuned—to improve its performance for a particular task, domain, or application.

LLM fine tuning involves re-training a model that has already been trained on a large, general dataset (such as books, websites, or other public texts) using a specialized dataset. This allows the model to adapt to the nuances of the more specific use case.

There are several steps to fine tune LLMs:

Start with a model that is already trained on a broad dataset and learned general knowledge of language, grammar, and facts.

Next, train the model with a new, smaller dataset that is domain-specific, such as medical texts, legal documents, or customer service conversations. This allows it to adjust its parameters to better understand the jargon, tone, and requirements of that specific field.

The next step is the fine-tuning phase, which uses supervised learning to compare the model’s output to expected answers and gradually adjusts the model’s answers to minimize errors. Fine-tuning is typically a shorter training period compared to the initial pre-training phase.

Last, the fine-tuned model is evaluated on tasks specific to its new domain to ensure the process was successful.

Common use cases for fine-tuned LLMs include:

• Customer support chatbots trained to answer customer service inquiries and fine-tuned on customer service dialogues
• Medical diagnosis GPT or BERT models that analyze medical texts to predict diagnoses fine-tuned on medical papers and doctor-patient dialogues
• Legal analysis models trained to understand legal documents and fine-tuned with court cases, legal textbooks, or contracts
• Code generation LLMs trained to debug or write code fine-tuned on coding forums, Stack Overflow, or open-source repositories
• Sentiment analysis models trained to determine customer sentiment and fine-tuned on customer feedback or product reviews

Pre Trained LLM

Pre-trained LLM models are language models that have been trained on a vast amount of general data (such as text from books, websites, articles, etc.) before being applied to specific tasks or fine-tuned for particular use cases. LLM pre-training gives models a general understanding of language, grammar, facts, and world knowledge, which they can later apply in various applications, like chatbots, text generation, or question answering.

LLM model training typically involves a massive corpus of text (like the entire internet) to learn patterns and relationships in the language. It doesn’t focus on a specific task, but rather on developing general language skills, such as:

• Understanding grammar and syntax
• Recognizing relationships between words and concepts
• Learning world knowledge (basic facts, common knowledge)

This initial LLM training phase is computationally expensive and takes weeks to months to complete using massive datasets and high-performance computing resources. However, it is an essential first step because it provides the foundational knowledge that allows an LLM to understand general language across many domains, generate coherent text or responses for a wide variety of tasks, and be used in a wide range of applications out-of-the-box, without needing to retrain from scratch.

What Are Open Source Large Language Models?

Open-source LLMs feature architecture, weights, training data, and/or inference code that are publicly available. Unlike proprietary models such as OpenAI’s GPT-4, open-source LLMs allow users to study, modify, and deploy them on their own infrastructure.

Open-source LLMs allow researchers and developers to inspect the model’s internals and customize them for specific use cases. Running locally avoids API fees, and open-source models benefit from collective, community-driven improvements.

Examples of popular open-source LLMs include Meta’s LLaMA 2 and LLaMA 3; Mistral and Mixtral; Falcon; OpenLLaMA; and Pythia.

SLM vs LLM

Small language model (SLM) and large language model (LLM) differ primarily in size, training data, and capabilities.

An SLM is designed to be smaller and typically uses less training data compared to larger models like LLMs. These models have fewer parameters—a few million or hundred million—and they are trained on smaller datasets. They might only capture basic language patterns.

SLMs require significantly less computational power and memory, making them easier and cheaper to run. While they are capable of handling simpler language tasks such as basic sentence generation and text classification, they tend to perform worse than LLMs on complex tasks that require deeper understanding and reasoning.

SLMs are appropriate when a lighter, faster model is needed for simple tasks like keyword extraction, basic sentiment analysis, or simple text classification. They are also ideal in resource-constrained environments where computational power or memory is limited.

In contrast, an LLM is a much larger and more powerful language model with billions to trillions of parameters, trained on massive datasets. They are trained on enormous datasets with text from the web, books, research papers, and more, enabling them to learn a wide variety of language patterns and world knowledge.

LLMs require substantial computational power, storage, and memory, making them more resource-intensive and expensive to deploy. They excel in handling complex language tasks, including nuanced conversation, text generation, summarization, translation, code generation, and more. They have the ability to understand deeper contexts and reason across a wide range of domains.

Use LLMs when you need a model that can handle advanced tasks, such as conversational AI, creative writing, code generation, medical diagnosis, or any task requiring sophisticated understanding. They are also ideal for applications that demand scalability, such as high-quality chatbots or systems that need to generate coherent and contextually appropriate responses across various topics.

NLP vs LLM

Natural language processing (NLP) and large language models are closely related concepts, but they refer to different things.

NLP refers to the field of technology focused on enabling machines to understand, interpret, and generate human language in ways that are meaningful and useful. NLP is a subset of artificial intelligence focused on a wide range of language-related tasks, including:

• Text classification. Categorizing text into predefined categories for use in applications such as spam detection.
• Sentiment analysis. Determining whether text expresses positive, negative, or neutral sentiments.
• Named entity recognition (NER). Identifying people, places, organizations, and other entities in text.
• Machine translation. Translating text between languages.
• Part-of-speech tagging. Identifying the grammatical components of a sentence.
• Speech recognition. Converting spoken language into text.
• Text summarization. Automatically shortening long texts.
• Question answering. Extracting answers from text.

Traditional NLP used rule-based systems and shallow machine learning models. More modern NLP uses deep learning and transformer models (like LLMs) which have dramatically improved the accuracy and performance of these systems.

In contrast, LLMs are a subset of NLP models that are particularly focused on large-scale language understanding and generation. 

How Does Retrieval Augmented Generation (RAG) Relate to LLMs?

Retrieval augmented generation or RAG is an architectural approach that can leverage custom data to improve the efficacy of large language model applications. It works by retrieving data and documents that are relevant to a task or question and providing them as context for the LLM. LLM RAG can support Q&A systems and chatbots, allowing them to more easily access domain-specific knowledge and maintain up-to-date information.

Common LLM Applications and Use Cases

Here are some common examples of LLM applications and use cases:

Text generation and content creation. LLMs excel at generating coherent and contextually relevant text, making them invaluable for creating blog posts, articles, marketing materials, novels, short stories, poetry, scripts for movies and TV shows, social media content that is tailored to specific audiences, and product descriptions for e-commerce websites.

Conversational AI/chatbots. LLMs are at the core of chatbots and virtual assistants that provide natural, engaging conversations. This allows for a range of applications, including 24/7 customer service, personal assistants for everyday tasks like Alexa and Siri, and supportive conversational agents for emotional wellness and mental health.

Text summarization. LLMs are excellent at summarizing long documents, extracting the most important information, and presenting it concisely. Use cases for this LLM application include summaries of news articles, press releases, academic papers, contracts, agreements, case law, and meeting transcripts or notes.

Translation and language localization. LLMs like Google Translate leverage powerful models for cross-lingual understanding and translation between different languages. This enables use cases such as machine translation of text, documents, and even websites; real-time, multilingual customer support using chatbots or agents; and adapting content like marketing materials to specific cultures or regions.

Sentiment analysis. LLMs can be fine-tuned to help businesses understand customer feedback, social media sentiment, or public opinion. These kinds of LLM use cases include the analysis of customer sentiment across social media platforms and customer reviews to gauge brand perception; market research into consumer feelings and attitudes toward products, services, or campaigns; and analysis of public opinion on political candidates, issues, or events.

Question answering and knowledge retrieval. LLMs conduct advanced question-answering tasks by processing large amounts of text and returning specific answers to user queries. This allows for a number of use cases, including automating answers to frequently asked questions on websites or in applications; allowing healthcare professionals to access quick answers to medical queries from within a vast medical knowledge database; helping researchers answer specific questions or extract information from academic journals or papers; and offering quick legal advice by answering questions based on case law, legal texts, or other relevant sources.

Code generation and software development. LLMs can partially automate software development by generating code or suggesting code completions. Use cases for this include generating code snippets or entire programs based on high-level descriptions or user input; suggesting fixes or identifying issues in existing code; and automatically generating documentation for codebases, making them more accessible to other developers.

Personalization and recommendation systems. LLMs can help personalize experiences by understanding user preferences and providing tailored recommendations. This enables use cases such as personalized e-commerce product recommendations based on browsing history and preferences; streaming recommendations for movies, TV shows, or music based on user tastes; and personalized learning paths or content based on student progress and interests.

Knowledge extraction and data mining. LLMs can extract valuable information from unstructured data and transform it into structured knowledge. This allows LLMs to automatically extract key facts and figures from reports, contracts, or other unstructured documents; analyze competitor information and market trends from publicly available data; and extract relevant financial data from earnings reports, news articles, or financial statements.

Image and video captioning. Some multimodal LLMs are designed to handle not just text but also images and videos. This allows for use cases such as automatically generating captions or descriptions for images in social media platforms or content management systems; providing summaries or key insights from video content, such as YouTube videos or corporate training material; and answering questions based on the content of images or videos.

Benefits of Large Language Models

Advantages of large language models include advanced language capabilities, automation of repetitive tasks, increased productivity, and the ability to personalize user experiences. Here’s a closer look at some benefits of LLMs:

Advanced language understanding. LLMs are highly proficient at understanding complex language nuances, enabling them to perform language-related tasks such as machine translation, summarization, and question-answering with high accuracy.

Automation of repetitive tasks. LLMs can automate many repetitive and time-consuming tasks, such as generating content, answering common questions, or creating product descriptions. Customer service chatbots powered by LLMs can automate 24/7 support, answering frequently asked questions without human intervention.

Improved productivity. By automating tasks that require language understanding, LLMs can dramatically increase productivity in industries like content creation, customer service, and software development. For example, developers can use code generation tools such as GitHub Copilot to generate function templates or even whole code blocks.

Contextual text generation. LLMs can generate context-aware and coherent text, making them useful for tasks such as writing essays, creative stories, advertising copy, or even formal documents—all with a deep understanding of context and tone.

Multilingual capabilities. Many LLMs are multilingual and can translate text and understand languages beyond English, making them useful for global applications such as automatic translation services for websites or customer support across regions.

Personalization. LLMs can generate customized content based on user preferences, improving user engagement and satisfaction. For example, e-commerce websites can recommend products based on browsing history or serve up personalized advertisements tailored to user interests.

Real-time insights. LLMs can process large amounts of text and provide real-time insights, assisting with decision-making and business intelligence. For example, financial institutions can conduct market trend analysis or sentiment analysis using LLMs to inform investment strategies.

Challenges of Large Language Models

Although there are many LLM advantages, the challenges of LLMs include resource demands, bias in outputs, a lack of true understanding, data privacy concerns, and the potential for misuse:

Resource-intensive. Training LLMs requires massive computational power and vast amounts of data, making them extremely resource-heavy. This can be costly and energy-consuming, limiting access to organizations with the necessary infrastructure and making them difficult to replicate for smaller businesses.

Lack of true understanding. While LLMs excel at generating contextual responses, are essentially pattern-matchers and lack true understanding of the content. This is why LLMs can provide answers that sound correct but are factually inaccurate; they rely on probabilities, not reasoning. For example, an LLM might generate a confidently incorrect medical diagnosis or a misleading answer to a complex question because it lacks real understanding.

Hallucination. An issue related to the lack of true understanding is that LLMs sometimes generate information that is plausible-sounding but false—a phenomenon known as hallucination. Unlike a misfire based on failing to understand the deeper context and going with probabilities instead, this is a case of the model creating entirely fictitious responses or facts that appear convincing. For example, an LLM might answer a question about a historical event with false but realistic details or generate entirely fabricated quotes from famous individuals.

Data privacy and security risks. LLMs trained on vast amounts of publicly available data might inadvertently expose sensitive or personal information contained in training data and inadvertently reveal details from private conversations or documents in generated content, violating user privacy.

Dependency on large datasets. The quality and diversity of the training data directly affect the performance of LLMs, making them sensitive to data quality. If the data is too homogenous, outdated, or low-quality, the model may underperform or generate biased responses. For example, a model trained primarily on English-language data might struggle with non-English text or fail to understand regional dialects.

Limited long-term memory. Most LLMs have short-term memory and struggle with reasoning over the long-term across extended conversations or documents.  This makes them less reliable for tasks requiring deep, multi-turn conversation or long-form content generation. For example, during an ongoing conversation, the model may forget context or fail to reference earlier parts of the dialogue correctly.

Bias and fairness issues. LLMs can inadvertently learn biases present in their training data, leading to unintended consequences in their outputs. A language model trained on biased data might generate sexist or racist content or make discriminatory decisions in applications like hiring or loan approvals.

Ethical concerns and misuse. On a related note, LLMs can be used for malicious purposes such as generating misinformation, deepfakes, or harmful content—and this is very difficult to regulate and prevent. Malicious actors could use LLMs to generate fake reviews, political propaganda, or even phishing emails that are indistinguishable from legitimate messages.

Outlook on the Future of LLMs

There are several key trends emerging that will affect the future of LLMs:

Efficiency and accessibility will improve, making LLMs more accessible and cost-effective
As LLMs grow in size, the computational resources required to train them also increase. Yet there’s a growing focus on making these models more efficient, so they can offer similar capabilities without requiring massive resources.

Research into more efficient transformer architectures (like sparse transformers) and techniques like model pruning could significantly reduce the computational costs. Techniques such as distillation (training smaller models to mimic larger ones) could lead to smaller, faster, and more cost-effective models that are just as capable as their larger counterparts. And improved training techniques could reduce the environmental impact of training such massive models.

Multimodal capabilities will create richer, more dynamic AI systems that combine text, image, and video understanding
LLMs are already being used in multimodal applications, for example in models like CLIP, DALL·E, and Flamingo. In the near future, we will see more integrated multimodal systems, where models seamlessly understand and generate content across text, images, audio, and video, providing a richer and more dynamic understanding of the world.

These models will enhance use cases in areas like content creation, virtual assistants, autonomous systems, and interactive media—AI systems that can understand a video, summarize it, and provide relevant information or actions.

Ethics will continue to be a priority, with efforts to mitigate bias and ensure responsible deployment
As LLMs become more powerful and widespread, an increasing focus on ethics will ensure that these models are developed and deployed in a responsible manner. Efforts to develop fairer models that minimize bias and discrimination will involve improving data curation practices and creating tools for detecting and mitigating harmful biases. We are also likely to see more regulatory frameworks and ethical standards surrounding the deployment of LLMs, particularly in sectors like healthcare, finance, and law, to ensure transparency and accountability.

As LLMs become more complex, there will be a push toward making these models more interpretable and explainable, so their decisions can be better understood by users and regulators. Legal and ethical challenges around LLM security and deployment will require appropriate frameworks to prevent misuse, such as the generation of misinformation, disinformation, or deepfakes.

Personalization and adaptation to specific domains and individual needs will lead to even smarter AI tools
While pre-trained LLMs are already highly capable, fine-tuning for specialized industries like healthcare, legal, finance, and entertainment will become even more crucial in the future, enabling more personalization and more accurate and contextually aware responses. Future LLMs will be better at adapting to individual users’ preferences, writing styles, and personal needs, creating an environment for highly personalized AI interactions.

Autonomous AI agents will empower individuals and businesses to manage complex tasks more effectively
As LLMs advance, autonomous AI agents that can handle complex tasks with minimal human input will become more feasible. LLM-powered agents could autonomously manage tasks like email management, scheduling, researching, and even creative production on behalf of users, drastically improving personal and professional productivity. We can also expect autonomous LLM-driven systems could play a role in industries like robotics, self-driving vehicles, and smart cities, where they make real-time decisions based on text, voice, and environmental data.

WEKA for LLM Inferencing

The WEKA® Data Platform is purpose-built to meet the extreme performance, scalability, and efficiency demands of large-scale AI and machine learning workloads—making it an ideal foundation for Large Language Model (LLM) training and inferencing. LLMs require access to vast amounts of data, with high throughput and low latency across both small and large files, to accelerate the iterative process of training massive neural networks. WEKA delivers a modern, software-defined architecture that seamlessly scales across flash and cloud storage, providing consistent performance from terabytes to exabytes. Its POSIX-compliant, distributed file system eliminates traditional infrastructure bottlenecks, allowing compute clusters—whether CPU- or GPU-based—to operate at maximum efficiency without being gated by data I/O constraints.

For inferencing and production deployment of LLMs, where responsiveness and scalability are key, WEKA’s architecture shines. Its native S3 object capabilities allow seamless integration with AI frameworks and tools that increasingly rely on object-based data lakes, enabling efficient storage and retrieval of model weights, embeddings, and user prompts. At the same time, its POSIX support allows for direct mountable access by GPU nodes, enabling hybrid workflows that span both file and object paradigms. Whether in a private data center or across hybrid and multicloud environments, WEKA ensures LLM workloads can scale elastically and operate at peak performance, delivering low-latency inference at scale while supporting the agility and flexibility modern AI teams need.