Results for "data → model"

Requirement to preserve relevant data.

Finding mathematical equations from data.

A model that assigns probabilities to sequences of tokens; often trained by next-token prediction.

A high-capacity language model trained on massive corpora, exhibiting broad generalization and emergent behaviors.

Model trained to predict human preferences (or utility) for candidate outputs; used in RLHF-style pipelines.

Central system to store model versions, metadata, approvals, and deployment state.

Reconstructing a model or its capabilities via API queries or leaked artifacts.

Framework for identifying, measuring, and mitigating model risks.

Embedding signals to prove model ownership.

Generative model that learns to reverse a gradual noise process.

Diffusion model trained to remove noise step by step.

Formal model linking causal mechanisms and variables.

Required descriptions of model behavior and limits.

Optimizes future actions using a model of dynamics.

RL without explicit dynamics model.

Learned model of environment dynamics.

Training one model on multiple tasks simultaneously to improve generalization through shared structure.

The relationship between inputs and outputs changes over time, requiring monitoring and model updates.

A measurable property or attribute used as model input (raw or engineered), such as age, pixel intensity, or token ID.

The learned numeric values of a model adjusted during training to minimize a loss function.

The set of tokens a model can represent; impacts efficiency, multilinguality, and handling of rare strings.

Training objective where the model predicts the next token given previous tokens (causal modeling).

Maximum number of tokens the model can attend to in one forward pass; constrains long-document reasoning.

A high-priority instruction layer setting overarching behavior constraints for a chat model.

Model-generated content that is fluent but unsupported by evidence or incorrect; mitigated by grounding and verification.

Fine-tuning on (prompt, response) pairs to align a model with instruction-following behaviors.

Ensuring model behavior matches human goals, norms, and constraints, including reducing harmful or deceptive outputs.

Techniques to understand model decisions (global or local), important in high-stakes and regulated settings.

Local surrogate explanation method approximating model behavior near a specific input.

Observing model inputs/outputs, latency, cost, and quality over time to catch regressions and drift.