How Prompt Formatting Affects LLM Performance

🚀 BERT vs GPT, Two Giants That Revolutionized NLP. Two names. Two legends. One shared mission: transforming how machines understand and generate human language. 🧠 BERT (by Google): The Master of Understanding Built on the Bidirectional Encoder Representations from Transformers, BERT reads text both left and right, just like humans do. It understands context, captures meaning, and excels at comprehension-driven tasks, such as sentiment analysis, question answering, and entity recognition. In short, BERT doesn’t just read words; it understands intent. 🧠 GPT (by OpenAI): The Genius of Generation The Generative Pre-trained Transformer predicts the next word in a sequence. It’s unidirectional moving forward, word by word but it creates magic. From essays to chatbots to creative storytelling, GPT writes, imagines, and communicates. It’s the brain behind modern conversational AI, including the model you’re reading this from. ⚖️ BERT understands the world. GPT creates it. One powers comprehension, the other fuels creativity. Together, they define the backbone of today’s AI language revolution. 🔥 The future of language is not human vs machine, it’s human + machine. #ArtificialIntelligence #MachineLearning #DeepLearning #DataScience #NLP #BERT #GPT #OpenAI #GoogleAI #AIRevolution #AIFuture #TechInnovation

 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝘄𝗶𝘁𝗵 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗧𝗲𝘅𝘁 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 Most embedding models create ONE representation for all tasks. This means, Good embedding for Task A Bad embedding for Task B. Embeddings should be task-specific. Enter 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗢𝗥! How INSTRUCTOR Works?? The same text gets different embeddings based on instructions. Example: "Who sings Love Story?"  • Duplicate question detection → Embedding A  • Information retrieval → Embedding B  • Topic classification → Embedding C  Key Results:  • Outperforms models 𝟭𝟰𝘅 𝗹𝗮𝗿𝗴𝗲𝗿 (335M vs 4.8B parameters)  • 𝟯.𝟰% 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 over SOTA across 70 diverse tasks  • Trained on 330 datasets with human-written instructions  • Works across retrieval, classification, clustering, and more Real-World Applications Perfect for Small Language Model (SLM) workflows:  • Retrieval tasks - fetch relevant context  • Reranking tasks - prioritize best results  • Multi-task systems - one model, many use cases  Check out this research paper: https://lnkd.in/gpgbQHKB The future of embeddings is instruction-aware and task-adaptive! #AI #MachineLearning #NLP #Embeddings #Research #LLM #RAG #DeepLearning

🚀 Understanding LLM Architectures: Encoder, Decoder, and Both 🔍 When we talk about Large Language Models (LLMs), they’re not all built the same way! Different architectures power different kinds of intelligence. Here’s a quick breakdown 👇 🧠 Encoder-Only Models Focus: Understanding context and meaning of the input. Best for: Classification, creating text embeddings, and semantic search. The Vibe: The reader that deeply comprehends a document. Examples: 🔹 BERT 🔹 RoBERTa 🔹 ALBERT 🔹 DistilBERT 💬 Decoder-Only Models Focus: Generating text — predicting the next word in a sequence. Best for: Chatbots, creative content generation, and complex reasoning tasks. The Vibe: The storyteller that fluently continues the narrative. Examples: 🔹 GPT (GPT-3, GPT-4, etc.) 🔹 LLaMA 🔹 Mistral 🔹 Falcon 🔄 Encoder–Decoder (Seq2Seq) Models Focus: Combine both worlds — understand first, then generate. Best for: Tasks requiring a distinct input and output structure, like translation and summarization. The Vibe: The translator or summarizer that transforms one text into another. Examples: 🔹 T5 🔹 BART 🔹 FLAN-T5 🔹 Pegasus 💡 TL;DR: 🧠 Encoder → Understands 💬 Decoder → Generates 🔄 Both → Understands & Generates Which architecture do you find most fascinating — deep understanding or fluent generation? 🤔 Let’s discuss in the comments! 💬 #AI #LLM #MachineLearning #NLP #DeepLearning #ArtificialIntelligence #TechLearning #GenerativeAI

🚀 Day 69 of #100DaysOfMachineLearning Today, I explored one of the core concepts behind modern NLP models — the Encoder–Decoder architecture. 🧠 Encoder: Understands the input text and converts it into a meaningful, context-rich numerical representation. 🗣️ Decoder: Takes that understanding and generates coherent output — like a summary, translation, or response — one word at a time. ⚙️ Architecture Overview The encoder captures relationships between words using self-attention, while the decoder focuses on the most relevant parts of the encoder’s output during generation. ⚠️ Common Challenges - Information loss in long sequences - Exposure bias between training and inference - High computational cost for large models - Overfitting or repetitive outputs 👉 In simple terms: The Encoder understands, and the Decoder expresses. #Day69 #NLP #DeepLearning #MachineLearning #Transformers #TextSummarization #AI #Learning #DataScientists

Here is a trick question 🤔: Does the computational complexity of a language model decrease if we increase the number of attention heads? You might assume “yes” because the dimensions of Q, K, and V per head shrink when you add more heads. But the subtle and often misunderstood trade-off in multi-head attention is this: When you increase the number of heads, each head gets a smaller subspace (dₖ = d_model / h). So, yes, each head becomes cheaper individually. 💡 But you also have more heads running in parallel, and when you multiply that out, the total cost becomes: h⋅n2⋅dk=n2⋅dmodel So, the overall computational complexity remains roughly the same. You don’t get a speed-up, you get something far more valuable. 🚀 So why does multi-head attention actually help? Because multiple smaller heads can learn different types of relationships at the same time: 🔍 One head learns positional patterns 🔗 Another captures long-range dependencies 🧩 Another model's syntax 🎯 Another focuses on entities or interactions Instead of one big attention mechanism doing everything, you get a team of specialists, each focusing on its own view of the data. The result? ✨ Richer representations ✨ Better contextual understanding ✨ Stronger model performance All without increasing computational cost. 💬 Follow me and let’s have deeper discussions on core ML and LLM concepts! #MachineLearning #DeepLearning #LLM #AttentionMechanism #Transformers #AI #NeuralNetworks #NLP #MLOps #DataScience #TechEducation #AITech #GenAI #ArtificialIntelligence

Demystifying BERT: The Backbone of Modern NLP Ever wondered how machines understand human language so well today? One of the key breakthroughs is BERT – Bidirectional Encoder Representations from Transformers – developed by Google. Unlike traditional models that read text left-to-right or right-to-left, BERT reads in both directions simultaneously, giving it a deeper understanding of context. For example, in the sentence “He went to the bank to deposit money,” BERT knows that “bank” refers to a financial institution, not a riverbank — thanks to its bidirectional nature. 💡 How BERT Works: 1. It’s based on the Transformer architecture, which uses attention mechanisms to weigh the importance of each word in a sentence. 2. BERT is pre-trained on massive text corpora (like Wikipedia) using tasks like: Masked Language Modeling: Predicting missing words in a sentence. Next Sentence Prediction: Understanding relationships between sentences. 3.Once pre-trained, BERT can be fine-tuned for specific tasks like sentiment analysis, question answering, or named entity recognition — often with state-of-the-art results. 🚀 BERT revolutionized NLP by enabling models to truly grasp context, not just keywords. It’s the foundation behind many AI applications we use daily — from search engines to chatbots. #AI #NLP #MachineLearning #BERT #DeepLearning #Transformers #TechExplained

The Secret Ingredient Behind Smarter RAG Systems — Reranking Models Your retriever brings you 50 chunks of data…but which 5 are truly relevant to your query? That’s where Rerankers quietly make the magic happen. Here are some of the best options to explore 1️⃣ Cohere Rerank (API) -A commercial powerhouse that tops MTEB benchmarks. -If you want accuracy, reliability, and easy API integration — this one’s hard to beat. 2️⃣ BGE-reranker-base / large (Open-Source) -From HuggingFace, built for multilingual reranking and strong generalization. -Perfect for RAG systems serving users across different languages. 3️⃣ Cross-Encoder MSMARCO / MiniLM (Open-Source) -Lightweight, efficient, and trained for relevance scoring. -Best suited for smaller deployments where latency matters. 4️⃣ ColBERT (Late Interaction) -A brilliant trade-off between retrieval speed and rerank precision. -Ideal when you need real-time performance without losing context quality. Reranking bridges the gap between “retrieved” and “relevant.” It’s the difference between a vague answer and a laser-focused insight. #GenerativeAI #RAG #RetrievalAugmentedGeneration #LLM #Cohere #HuggingFace #AI #MachineLearning #NLP #Reranker #ArtificialIntelligence

🎯 𝐇𝐨𝐰 𝐭𝐨 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐋𝐋𝐌 𝐎𝐮𝐭𝐩𝐮𝐭𝐬 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐥𝐲 As LLMs (Large Language Models) continue to revolutionize industries, their accuracy and reliability have become critical factors for adoption. But how do we measure these qualities? 🤔 I’ve put together a comprehensive guide that breaks down key techniques to evaluate LLM outputs, including: ✅ 𝐄𝐱𝐚𝐜𝐭 𝐌𝐚𝐭𝐜𝐡 (𝐄𝐌) for precision ✅ 𝐁𝐋𝐄𝐔 𝐚𝐧𝐝 𝐑𝐎𝐔𝐆𝐄 𝐬𝐜𝐨𝐫𝐞𝐬 for text similarity ✅ 𝐇𝐮𝐦𝐚𝐧 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤 for real-world insights ✅ 𝐂𝐚𝐥𝐢𝐛𝐫𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐑𝐨𝐛𝐮𝐬𝐭𝐧𝐞𝐬𝐬 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 to handle tricky or noisy inputs ✅ 𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 for edge cases 💡 𝐊𝐞𝐲 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲: No single metric can provide the full picture. Combining techniques is essential for meaningful evaluation and improvement. Whether you're building, fine-tuning, or using LLMs, these strategies can help ensure their outputs are trustworthy, relevant, and impactful. 📥 𝐂𝐡𝐞𝐜𝐤 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐟𝐨𝐫 𝐚𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬, 𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬, 𝐚𝐧𝐝 𝐚 𝐝𝐞𝐞𝐩𝐞𝐫 𝐝𝐢𝐯𝐞 Let's work together to unlock the true potential of AI! 𝐅𝐨𝐥𝐥𝐨𝐰 Sanjay Nandakumar 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬! 📚 #ArtificialIntelligence #LLM #MachineLearning #DataScience  #EvaluationMetrics #Statistics #DeepLearning #NaturalLanguageProcessing #NLP

Why Your RAG Model Keeps “Missing the Point” 🧠📄 Ever wonder why your Retrieval-Augmented Generation (RAG) system sometimes gives half-right answers—even when the data’s all there? It might not be your model at all…it could be your chunking strategy. Most projects start with fixed-size chunking—splitting text into equal blocks like 500 or 1,000 tokens. It’s easy and fast. But there’s a catch: it doesn’t care about meaning. Sentences get cut in half, context breaks, and retrieval becomes messy. Enter semantic chunking—where chunks follow the logic of language, not numbers. By splitting text based on coherence and context, you help your RAG system retrieve complete ideas instead of text fragments. Many effective setups now mix both: semantic segmentation first, then light size limits for efficiency.Because in RAG, sometimes the secret to smarter answers isn’t tuning the model—it’s feeding it context the way humans understand it. #RAG #LLMs #AI #SemanticChunking #FixedSizeChunking #MachineLearning #VectorDatabases #ArtificialIntelligence #NLP

🚀 Continuous Autoregressive Language Models (CALM) CALM introduces a paradigm shift in large language model design, replacing discrete next-token prediction with continuous next-vector prediction. By compressing multiple tokens into a single vector, CALM reduces generation steps and computational costs while maintaining high performance. ⚡🤖 Key Features: 🔹 Next-Vector Prediction Paradigm – Models language as sequences of continuous vectors instead of discrete tokens. 🔹 High-Fidelity Autoencoder – Compresses K tokens into one robust vector with 99.9% reconstruction accuracy. 🎯 🔹 Likelihood-Free Framework – Enables training and evaluation without explicit probability distributions. 🔹 BrierLM Metric – A novel likelihood-free alternative to perplexity for fair LM evaluation. 📊 🔹 Efficient Generative Head (Energy Transformer) – Achieves high-quality, single-step generation. ⚡ 🔹 Superior Compute-Performance Trade-off – Delivers Transformer-level results at significantly lower FLOPs. 💻⚙️ 📄 Read the full paper: https://lnkd.in/gx44Qxx7 💻 Github: https://lnkd.in/geZR7HJh 👉 Join our Telegram group for curated resources, learning materials, and updates! #AI #LanguageModels #NLP #MachineLearning #DeepLearning #Transformers #AIResearch #GenerativeAI Follow and Connect: Woongsik Dr. Su, MBA