Role of a Large Language Model

Large Language Models (LLMs) may look similar on the surface, but their architectures define their strengths, trade-offs, and use cases. Understanding these differences is key to making the right choices in research and real-world applications. Here’s a deeper look at the four foundational LLM architectures 1. Decoder-Only Models (GPT, LLaMA) -Autoregressive design: predict the next token step by step. -Powering generative applications like chatbots, assistants, and content creation. Strength: fluent, creative text generation. Limitation: struggles with tasks requiring bidirectional context understanding. 2. Encoder-Only Models (BERT, RoBERTa) -Built to understand rather than generate. -Capture deep contextual meaning using bidirectional self-attention. -Perfect for classification, search relevance, and embeddings. Strength: strong semantic understanding. Limitation: cannot generate coherent long-form text. 3. Encoder–Decoder Models (T5, BART) -Combine the understanding power of encoders with the generative power of decoders. -Suited for sequence-to-sequence tasks: summarization, translation, Q&A. Strength: flexible and powerful across diverse NLP tasks. Limitation: computationally more expensive compared to single-stack models. 4. Mixture of Experts (MoE: Mixtral, GLaM) -Leverages a gating network to activate only a subset of parameters (experts) per input. -Provides scalability without proportional compute cost. Strength: massive capacity + efficiency. Limitation: complexity in training, routing, and stability. Decoder-only models dominate today’s consumer AI (e.g., ChatGPT), but MoE architectures hint at the future — scaling models efficiently without exploding costs. Encoder-only and encoder–decoder models remain critical in enterprise AI pipelines where accuracy, context understanding, and structured outputs matter more than freeform generation. The next decade of AI may not be about “bigger is better,” but about choosing the right architecture for the right job — balancing efficiency, accuracy, and scalability. Which architecture do you believe will shape enterprise AI adoption at scale — GPT-style generalists or MoE-driven specialists?

Large Language Models (LLMs) are trained with a fixed set of parameters, creating a vast yet unchanging knowledge base after training. Their memory capacity, defined by architecture and context window size, is also fixed. Unlike Turing machines, which can theoretically have unlimited memory via an expandable tape, LLMs lack this flexibility. This limits them from computations needing unbounded memory, distinguishing them from Turing-complete systems. Instead, LLMs function as powerful pattern recognisers, approximating functions based on large but finite datasets. The context window, however, provides a way to temporarily extend an LLM’s memory. This is why Retrieval-Augmented Generation (RAG) has become so popular: it dynamically feeds relevant information into the model, though it remains read-only. As we explore more “agentic” uses of LLMs, though, we start considering the need for read-write memory. In this setup, the LLM functions as the “read/write head” of a Turing machine, reading from memory into its context window and writing key information back. The question is: if the LLM is the "read/write head," what serves as the "tape"? A simple solution is plain text, as used in tools like ChatGPT. This works to a degree, but plain text alone is imprecise, lacks mechanisms for compression, generalisation, and interlinking of information, and may not integrate well with the structured "memory" already present in organisational documents and databases. A fully neural architecture with slowly evolving 'background' neurons and faster-changing, inference-time ones—capable of integrating new information incrementally—could indeed be the end goal. However, a more immediate and pragmatic solution for organisations today is to build a Semantic Layer. Anchored in an "Ontological Core" that defines the organisation’s key concepts, this Semantic Layer interfaces with LLMs, allowing them to read from an ontology that links back to the data in underlying systems. With human oversight, LLMs can also write missing classes into the ontology and add missing facts back into these systems, creating a dynamic Virtual Knowledge Graph for the organisation. In this setup, the Virtual Knowledge Graph effectively becomes the Turing Machine’s “tape”—a dynamic, interconnected memory that the LLM can read from and write back to. By linking facts with URLs and using ontology for generalisation and compression, this graph provides an ideal read/write memory structure for the LLM. Combined in a neural-symbolic loop, this LLM+Graph system could even be Turing complete. This may sound like a far-future concept, but the future is arriving fast. I’m not saying it will be easy, but any organisation transitioning to Data Products can begin this journey today by simply adopting DPROD as their Data Product specification. Often, the first step in a journey turns out to be the most important one! ⭕ Ontologies + LLMs : https://lnkd.in/eACrNUbf

There’s been a lot of discussion about how Large Language Models (LLMs) power customer-facing features like chatbots. But their impact goes beyond that—LLMs can also enhance the backend of machine learning systems in significant ways. In this tech blog, Coupang’s machine learning engineers share how the team leverages LLMs to advance existing ML products. They first categorized Coupang’s ML models into three key areas: recommendation models that personalize shopping experiences and optimize recommendation surfaces, content understanding models that enhance product, customer, and merchant representation to improve shopping interactions, and forecasting models that support pricing, logistics, and delivery operations. With these existing ML models in place, the team integrates LLMs and multimodal models to develop Foundation Models, which can handle multiple tasks rather than being trained for specific use cases. These models improve customer experience in several ways. Vision-language models enhance product embeddings by jointly modeling image and text data; weak labels generated by LLMs serve as weak supervision signals to train other models. Additionally, LLMs also enable a deeper understanding of product data, including titles, descriptions, reviews, and seller information, resulting in a single LLM-powered categorizer that classifies all product categories with greater precision. The blog also dives into best practices for integrating LLMs, covering technical challenges, development patterns, and optimization strategies. For those looking to elevate ML performance with LLMs, this serves as a valuable reference. #MachineLearning #DataScience #LLM #LargeLanguageModel #AI #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gvaUuF4G

Exploring the Advancements in Large Language Models: A Comprehensive Survey ... Large Language Models (LLMs) have emerged as pivotal tools, revolutionizing natural language processing and beyond. A new research paper titled "Survey of Different Large Language Model Architectures: Trends, Benchmarks, and Challenges" sheds light on recent developments in LLMs and their multimodal counterparts (MLLMs). Here are some key insights worth sharing: 👉 The Evolution of LLMs The journey of LLMs began with foundational architectures like BERT and GPT, culminating in today’s sophisticated models. Central to this evolution is the **Transformer architecture**, introduced in 2017, which has fundamentally changed how we approach language tasks. 👉 Understanding Model Architectures The paper categorizes LLMs into three primary architectures: - "Auto-Encoding Models" (e.g., BERT): Focused on understanding context but limited in generating text. - "Auto-Regressive Models" (e.g., GPT): Excellent for generation tasks but may lack contextual awareness. - "Encoder-Decoder Models" (e.g., T5): Combine strengths of both types, applicable for complex input-output scenarios. This classification is crucial for selecting the appropriate model for specific tasks in real-world applications. 👉 Unleashing Multimodal Capabilities A significant focus of the paper is on "Multimodal Large Language Models (MLLMs)". These models can process and integrate multiple data formats—text, images, audio, and video—expanding the horizons for applications such as image captioning and video analysis. The ability to leverage diverse data sources represents a substantial shift in how we think about AI applications. 👉 Importance of Benchmarking Effective benchmarking is vital for assessing the performance of LLMs. The paper outlines several benchmarks used to measure various capabilities, ensuring that researchers and industry professionals can evaluate model efficiency and effectiveness reliably. This aspect is crucial for advancing LLM technology and aligning it with industry needs. 👉 Navigating Challenges and Future Directions While LLMs have made remarkable strides, the paper also highlights ongoing challenges, such as data limitations, model compression, and the complexities of prompt engineering. Addressing these issues will be essential for developing more robust and effective models in the future. The insights gathered from this research underscore the necessity of understanding not just the capabilities of LLMs but also the underlying architecture and challenges to fully leverage their potential in professional contexts. For those interested in delving deeper, I encourage you to read the full paper.

🤖 𝗔𝗜 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱: 𝗪𝗵𝗮𝘁 𝗜𝘀 𝗮 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 𝗮𝗻𝗱 𝗪𝗵𝘆 𝗦𝗵𝗼𝘂𝗹𝗱 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝗖𝗮𝗿𝗲? Think of a large language model (LLM) as your company's most knowledgeable employee who never sleeps. But what exactly is it, minus the tech jargon? At its core, an LLM is AI software that understands and generates human language with remarkable sophistication. It's like having a universal translator that not only speaks your language but can write, analyze, and even code. Why should your business pay attention? Here are three game-changing capabilities: 1. Supercharged Customer Service: LLMs can handle customer inquiries 24/7, understand context, and provide personalized responses that sound natural – not robotic. One of our clients reduced response times by 80% while maintaining high satisfaction scores. 2. Knowledge Unlocked: Imagine instantly analyzing thousands of documents, contracts, or market reports. LLMs can summarize key insights, spot patterns, and answer specific questions about your data in seconds. 3. Productivity Amplified: From drafting emails to writing code to creating marketing content, LLMs act as an intelligent assistant that helps your team work smarter, not harder. Think of the time saved on routine tasks that could be redirected to strategic thinking. But here's what many miss: 𝘛𝘩𝘦 𝘳𝘦𝘢𝘭 𝘱𝘰𝘸𝘦𝘳 𝘰𝘧 𝘓𝘓𝘔𝘴 𝘪𝘴𝘯'𝘵 𝘪𝘯 𝘳𝘦𝘱𝘭𝘢𝘤𝘪𝘯𝘨 𝘩𝘶𝘮𝘢𝘯𝘴 – 𝘪𝘵'𝘴 𝘪𝘯 𝘢𝘶𝘨𝘮𝘦𝘯𝘵𝘪𝘯𝘨 𝘩𝘶𝘮𝘢𝘯 𝘤𝘢𝘱𝘢𝘣𝘪𝘭𝘪𝘵𝘪𝘦𝘴. When implemented thoughtfully, they free up your team to focus on what humans do best: creative problem-solving, relationship building, and strategic decision-making. 𝘛𝘩𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯 𝘪𝘴𝘯'𝘵 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 𝘵𝘰 𝘢𝘥𝘰𝘱𝘵 𝘓𝘓𝘔𝘴, 𝘣𝘶𝘵 𝘩𝘰𝘸 𝘵𝘰 𝘪𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘦 𝘵𝘩𝘦𝘮 𝘦𝘧𝘧𝘦𝘤𝘵𝘪𝘷𝘦𝘭𝘺 𝘪𝘯𝘵𝘰 𝘺𝘰𝘶𝘳 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘰𝘱𝘦𝘳𝘢𝘵𝘪𝘰𝘯𝘴. Those who move early and wisely will have a significant competitive advantage. What are your thoughts on LLMs? How do you see them transforming your industry? #ArtificialIntelligence #BusinessInnovation #DigitalTransformation #FutureOfWork #AI #Technology

Most people think LLMs are just another AI model. 𝑻𝒉𝒂𝒕’𝒔 𝒂 𝒎𝒊𝒔𝒕𝒂𝒌𝒆. LLMs don’t just 𝗽𝗿𝗲𝗱𝗶𝗰𝘁—they 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲. They don’t 𝗰𝗹𝗮𝘀𝘀𝗶𝗳𝘆—they 𝗰𝗿𝗲𝗮𝘁𝗲. They 𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲 𝗵𝗼𝘄 𝘄𝗲 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁 𝘄𝗶𝘁𝗵 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. So, what exactly is an LLM? Let’s break it down. ➤ 𝑾𝒉𝒂𝒕 𝒊𝒔 𝒂 𝑴𝒐𝒅𝒆𝒍? A model is a 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗱 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗿𝗲𝗮𝗹𝗶𝘁𝘆, helping us understand or predict how something works. ➤ 𝑴𝒂𝒄𝒉𝒊𝒏𝒆 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝑴𝒐𝒅𝒆𝒍𝒔 (𝑴𝑳 𝑴𝒐𝒅𝒆𝒍𝒔) ML models learn patterns from data to make decisions. These fall into two main categories: ⇲ 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 – Forecast outcomes using past data. ✓ Classification Models ✓ Regression Models ✓ Time-Series Forecasting ⇲ 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃�� 𝗠𝗼𝗱𝗲𝗹𝘀 – Create 𝗻𝗲𝘄 data based on learned patterns. ✓ Text Generation ✓ Audio Generation ✓ Video & Image Generation → 𝑳𝒂𝒏𝒈𝒖𝒂𝒈𝒆 𝑴𝒐𝒅𝒆𝒍𝒔 A 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹 is a 𝘁𝘆𝗽𝗲 𝗼𝗳 𝗠𝗟 𝗺𝗼𝗱𝗲𝗹 that generates, understands, and manipulates text. → 𝑳𝑳𝑴𝒔 A 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 (𝗟𝗟𝗠) 𝗶𝘀 𝗮 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹—but at a scale never seen before. 𝗧𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝗻 𝗺𝗮𝘀𝘀𝗶𝘃𝗲 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀, LLMs power: ↗ AI assistants like ChatGPT ↗ Automated content creation ↗ Enterprise AI solutions 🚫 𝑾𝒉𝒂𝒕 𝑳𝑳𝑴𝒔 𝑨𝒓𝒆 𝑵𝑶𝑻 LLMs 𝗱𝗼 𝗻𝗼𝘁 predict future outcomes like classification or regression models. Instead, they 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲 human-like text using deep learning and context understanding. ⇲ 𝑻𝒚𝒑𝒆𝒔 𝒐𝒇 𝑳𝑳𝑴𝒔 ↗ 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀 – Large, general-purpose models used as a base for AI applications. ↗ 𝗗𝗼𝗺𝗮𝗶𝗻-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗠𝗼𝗱𝗲𝗹𝘀 – Fine-tuned for specialised industries. ↗ 𝗦𝗺𝗮𝗹𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 – Lightweight models for simpler tasks. LLMs 𝗮𝗿𝗲𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮𝗻 𝘂𝗽𝗴𝗿𝗮𝗱𝗲—𝘁𝗵𝗲𝘆’𝗿𝗲 𝗮 𝗽𝗮𝗿𝗮𝗱𝗶𝗴𝗺 𝘀𝗵𝗶𝗳𝘁. 💡 𝗪𝗵𝗲𝗿𝗲 𝗱𝗼 𝘆𝗼𝘂 𝘀𝗲𝗲 𝗟𝗟𝗠𝘀 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗮𝗰𝘁? Drop your thoughts in the comments. [#AI #LLMs #MachineLearning #GenerativeAI #ArtificialIntelligence #DataScience]

𝐄𝐯𝐞𝐫 𝐖𝐨𝐧𝐝𝐞𝐫𝐞𝐝 𝐇𝐨𝐰 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐓𝐡𝐢𝐧𝐤𝐬?  Here is the Answer Large Language Models (LLMs) like GPT are revolutionizing how we interact with AI.  But how do they actually work? Here is a breakdown of the process: 𝟏. 𝐈𝐧𝐩𝐮𝐭: • The journey starts with training text from books, websites, and conversations.  • The model processes your user prompt alongside context memory (previous interactions) to understand the task. • Tokenization breaks text into smaller, manageable pieces (tokens), and settings & rules ensure the model operates within safe boundaries. 𝟐. 𝐋𝐋𝐌 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: • The model generates embeddings, turning tokens into meaningful numbers it can work with. • Self-attention lets the model understand the relationships between words. • Transformer layers handle deep neural network reasoning, with pre-training knowledge from vast data sources and fine-tuning to ensure relevant, safe answers. 𝟑. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧: • The core of LLMs is next-token guessing, predicting the next word based on context. • Decoding methods control how the output is structured, while sentence building ensures coherence. • Safety checks are in place to filter harmful responses, and the repeat loop ensures the model keeps working until the answer is complete. 𝟒. 𝐎𝐮𝐭𝐩𝐮𝐭: • The model produces a final response-an answer, code snippet, or summary-with clean formatting to make it easy to read. • Optional tools like retrieval systems or external APIs enhance the response, while user feedback helps the model learn and improve. LLMs are built on a complex, multi-step process that combines cutting-edge technology with continuous learning to deliver meaningful, human-like responses. ♻️ Repost this to help your network ➕ Follow Prashant Rathi for more insights on Enterprise AI PS. Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently McKinsey & Company) or affiliates. #GenAI #LLM #AgenticAI

𝗘𝗮𝘀𝘆 𝗕𝘆𝘁𝗲𝘀 𝗳𝗼𝗿 𝗖𝘂𝗿𝗶𝗼𝘂𝘀 𝗠𝗶𝗻𝗱𝘀 #𝟭𝟬: 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 – 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗕𝗲𝗵𝗶𝗻𝗱 𝗔𝗜 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀 Ever wondered how AI chatbots generate human-like responses or summarize complex topics in seconds? The secret lies in Large Language Models (LLMs)—AI models that have been trained on large amounts of textual data to understand, generate, and manipulate language in astonishing ways. 𝗛𝗼𝘄 𝗟𝗟𝗠𝘀 𝗪𝗼𝗿𝗸: 𝗣𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗼𝗻 𝗠𝗮𝘀𝘀𝗶𝘃𝗲 𝗧𝗲𝘅𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀 – LLMs like GPT, Claude, and Gemini learn language patterns, grammar, facts, and reasoning by analyzing billions of words from books, articles, and the internet. 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 (a type of AI model designed for understanding relationships between words in a sentence, unlike older models that processed text one word at a time) – The core innovation behind transformers is self-attention. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗦𝗲𝗹𝗳-𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 & 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀? Attention is the mechanism that allows an AI model to focus on the most relevant words in a sentence while processing it. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Consider the sentence: "The dog sat on the floor because it was tired." A traditional AI model might struggle to understand what "it" refers to. But self-attention helps an LLM recognize that "it" refers to "the dog", improving comprehension. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 & 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 (additional training on specific tasks like customer service, coding, or writing, to make the AI more useful and accurate) – After pretraining, models undergo further optimization to align with human expectations, ensuring relevant and responsible responses. 𝗪𝗵𝗮𝘁’𝘀 𝗡𝗲𝘅𝘁? While LLMs are incredible at recognizing patterns and mimicking language, they still struggle with reasoning (making logical deductions and solving problems like a human would). That’s where the next evolution comes in: 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀—AI systems designed to think, deduce, and make complex decisions. Stay tuned for the next Easy Bytes for Curious Minds post, where we dive into Reasoning Models. #EasyBytesforCuriousMinds #FunWithTech #TechForEveryone #AIForBeginners #MBA #SFMBA #EMBA #AI #ML #HR MIT Sloan School of Management Massachusetts Institute of Technology