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What Are LLM Benchmarks? | IBM

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Welcome Overview Machine learning types Machine learning algorithms Statistical machine learning Linear algebra for machine learning Uncertainty quantification Bias variance tradeoff Bayesian Statistics Singular value decomposition Overview Feature selection Feature extraction Vector embedding Latent space Principal component analysis Linear discriminant analysis Upsampling Downsampling Synthetic data Data leakage Overview Linear regression Lasso regression Ridge regression State space model Time series Autoregressive model Overview Decision trees K-nearest neighbors (KNNs)Naive bayes Random forest Support vector machine Logistic regression Overview Boosting Bagging Gradient boosting Gradient boosting classifier Overview Transfer learning Overview Overview K means clustering Hierarchical clustering A priori algorithm Gaussian mixture model Anomaly detection Overview Collaborative filtering Content based filtering Overview Reinforcement learning human feedback Deep reinforcement learning Overview Overview Backpropagation Encoder-decoder model Recurrent neural networks Long short-term memory (LSTM)Convolutional neural networks Overview Attention mechanism Grouped query attention Positional encoding Autoencoder Mamba model Graph neural network Overview Generative model Generative AI vs. predictive AI Overview Reasoning models Small language models Instruction tuning LLM parameters LLM temperature LLM benchmarks LLM customization LLM alignment Diffusion models Variational autoencoder (VAE)Generative adversarial networks (GANs)Overview Vision language models Tutorial: Build an AI stylist Tutorial: Multimodal AI queries using Llama Tutorial: Multimodal AI queries using Pixtral Tutorial: Automatic podcast transcription with Granite Tutorial: PPT AI image analysis answering system Overview GraphRAG Tutorial: Build a multimodal RAG system with Docling and Granite Tutorial: Evaluate RAG pipline using Ragas Tutorial: RAG chunking strategies Tutorial: Graph RAG using knowledge graphs Tutorial: Inference scaling to improve multimodal RAG Overview Vibe coding Visit the 2025 Guide to AI Agents Overview LLM training Loss function Training data Model parameters Gradient descent Stochastic gradient descent Hyperparameter tuning Learning rate Overview Parameter efficient fine tuning (PEFT)LoRA Tutorial: Fine tuning Granite model with LoRA Regularization Foundation models Overfitting Underfitting Few shot learning Zero shot learning Knowledge distillation Meta learning Data augmentation Catastrophic forgetting Overview Scikit-learn XGboost PyTorch Overview AI lifecyle AI inference Model deployment Machine learning pipeline Data labeling Model risk management Model drift AutoML Model selection Federated learning Distributed machine learning AI stack Overview Natural language understanding Overview Sentiment analysis Tutorial: Spam text classifier with PyTorch Machine translation Overview Information retrieval Information extraction Topic modeling Latent semantic analysis Latent Dirichlet Allocation Named entity recognition Word embeddings Bag of words Intelligent search Speech recognition Stemming and lemmatization Text summarization Conversational AI Conversational analytics Natural language generation Overview Image classification Object detection Instance segmentation Semantic segmentation Optical character recognition Image recognition Visual inspection Rina Diane Caballar Cole Stryker large language models (LLMs)natural language processing text summarization fine-tuning Few-shot Zero-shot IBM Privacy Statement Go to episode overfitting code generation natural language understanding code generation hallucinate Ebook How to choose the right foundation model Learn how to choose the right approach in preparing datasets and employing foundation models. Read the ebook IBV Report The enterprise in 2030: Engineered for perpetual innovation Discover our five predictions about what will define the most successful enterprises in 2030 and the steps leaders can take to gain an AI-first advantage. Read the report AI models Explore IBM Granite Discover IBM Granite®, our family of open, performant and trusted AI models, tailored for business and optimized to scale your AI applications. Explore language, code, time series and guardrail options. Meet Granite Techsplainers podcast Large language models explained Techsplainers by IBM breaks down the essentials of LLMs, from key concepts to real‑world use cases. Clear, quick episodes help you learn the fundamentals fast. Listen now Ebook How to choose the right foundation model Learn how to select the most suitable AI foundation model for your use case. Read the ebook Article Discover the power of LLMs Dive into IBM Developer articles, blogs and tutorials to deepen your knowledge of LLMs. Explore the articles Guide The CEO’s guide to model optimization Learn how to continually push teams to improve model performance and outpace the competition by using the latest AI techniques and infrastructure. Read the guide Report A differentiated approach to AI foundation models Explore the value of enterprise-grade foundation models that provide trust, performance and cost-effective benefits to all industries. Read the report Ebook Unlock the power of generative AI and ML Learn how to incorporate generative AI, machine learning and foundation models into your business operations for improved performance. Read the ebook Discover watsonx.ai Explore AI development tools Explore AI services Explore watsonx.ai Explore AI development tools Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge Training Verifiers to Solve Math Word Problems HellaSwag: Can a Machine Really Finish Your Sentence?Evaluating Large Language Models Trained on Code Measuring Massive Multitask Language Understanding Program Synthesis with Large Language Models Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena SWE-bench: Can Language Models Resolve Real-World GitHub Issues?TruthfulQA: Measuring How Models Mimic Human Falsehoods WinoGrande: An Adversarial Winograd Schema Challenge at Scale

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