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What Is LLM Alignment? | IBM

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Home Think Topics 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 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 LLM training Overview 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 Dave Bergmann large language model (LLM)aligned pretraining fine-tuning agentic AI artificial intelligence AI safety responsibly artificial general intelligence (AGI)risks train self-supervised training data dataset scraping AI hallucinations guardrails provoke nuclear war IBM Privacy Statement training process neural network fine-tuning instruction tuning System prompts supervised learning reinforcement learning Grade School Math 8K (GSM8K)trained entirely on enterprise-safe data neural network “abliteration.”experimental LLM architecture from Guide Labs reasoning models reasoning models aren’t always “honest” when verbalizing their chain of thought Go to episode Anthropic’s Claude AI API chatbot prompt injection attacks instruction tuning context length model parameters loss function knowledge distillation synthetic data jailbreaking reinforcement learning from human feedback (RLHF)algorithms Reinforcement learning from human feedback (RLHF)ChatGPT proximal policy optimization (PPO).It can also lead to sycophancy constitutional AI “Safety Pretraining: Toward the Next Generation of Safe AI,”classifier inference beam search TruthfulQA HarmBench ChatbotArena Red teaming Dave Bergmann Upcoming Webinar | March 31 Achieve continuous compliance in a hybrid data world with IBM Guardium Data Protection Register for this webinar to learn how AI governance helps organizations manage risk, meet evolving regulations and build trusted, responsible AI at scale. Register now Report IBM X-Force Threat Intelligence Index 2026 Gain insights to prepare and respond to cyberattacks with greater speed and effectiveness with the IBM X-Force® Threat Intelligence Index. Read the report Report AI governance imperative: evolving regulations and emergence of agentic AI Learn how evolving regulations and the emergence of AI agents are reshaping the need for robust AI governance frameworks. Read the report Webinar recording Agent Ops and Responsible AI Join this webinar to explore practical strategies for operating and governing AI agents responsibly at scale, with expert insights on observability, risk management and accountable AI operations. Watch now Guide Building a strong data foundation for trustworthy AI Explore the Data Matters hub to see how strong data practices and governance lay the foundation for scalable AI success. See why Data Matters Ebook Maximize AI ROI through smarter governance Learn ways to maximize AI ROI—prioritizing high-impact use cases, governing risks, optimizing costs and accelerating adoption with watsonx®. Read the ebook Report IBM named a Leader in the Gartner® Magic Quadrant™ for GRC Unlock insights into IBM's OpenPages and learn why we were named a Leader. Get the Report Report Why AI governance is a business imperative for scaling enterprise artificial intelligence Learn about the new challenges of generative AI, the need for governing AI and ML models and steps to build a trusted, transparent and explainable AI framework. Read the report Report Getting ready for the EU AI Act, Phase 2: Risk-Assess and Categorize Understand the importance of establishing a defensible assessment process and consistently categorizing each use case into the appropriate risk tier. Read the report Insight AI lifecycle governance Read about driving ethical and compliant practices with a portfolio of AI products for generative AI models. Read the insight 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 Discover watsonx.governance Discover AI governance solutions Discover AI governance services Explore watsonx.governance Book a live demo “A General Language Assistant as a Laboratory for Alignment,”“Ethical Issues in Advanced Artificial Intelligence,”“Safety Pretraining: Toward the Next Generation of Safe AI,”“Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs,”“Safety Alignment Should Be made More Than Just a Few Tokens Deep,”“Refusal in LLMs is mediated by a single direction,”“Unpacking Claude’s System Prompt,”“Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study,”

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