Inference on English AI Terms Dictionary

Vllm

Sat, 18 Jul 2026 10:19:24 +0000

Definition

vLLM (Virtual Large Language Model) is an open-source library designed to accelerate LLM serving. It introduces PagedAttention, a memory management technique inspired by operating system virtual memory, which eliminates memory fragmentation and allows for efficient handling of KV caches. This results in significantly higher throughput and lower latency compared to other serving frameworks like HuggingFace Transformers, making it ideal for production deployments requiring high concurrency.

Summary

vLLM is a high-throughput and memory-efficient inference engine for Large Language Models, utilizing PagedAttention to optimize GPU memory usage.

Text Embeddings Inference

Sat, 18 Jul 2026 10:17:53 +0000

Definition

Text Embeddings Inference refers to the deployment and optimization of models that convert natural language into high-dimensional vectors. These embeddings capture semantic meaning, allowing systems to perform similarity searches, clustering, and retrieval-augmented generation (RAG). The process typically involves passing text through a transformer encoder, often with pooling layers, to produce fixed-size vectors that represent the input’s context and intent for downstream machine learning applications.

Summary

A specialized inference server designed to efficiently generate dense vector representations of text for semantic search and retrieval tasks.

Text Generation Inference

Sat, 18 Jul 2026 10:17:53 +0000

Definition

Text Generation Inference (TGI) is a dedicated software framework designed to serve large language models (LLMs) with low latency and high throughput. It optimizes the inference process for text generation tasks by implementing features like continuous batching, tensor parallelism, and optimized kernels. This allows developers to deploy powerful generative models in production environments, ensuring responsive interactions for end-users while managing computational resources effectively.

Summary

A high-performance serving engine optimized specifically for deploying large language models to generate text efficiently at scale.

Self-Consistency

Sat, 18 Jul 2026 10:14:51 +0000

Definition

Primarily used with Large Language Models (LLMs), this technique improves accuracy by generating several diverse responses to a prompt via sampling. Instead of relying on greedy decoding, it aggregates these outputs and applies majority voting to determine the most consistent result. This method effectively reduces hallucinations and enhances logical reasoning capabilities in complex tasks like mathematical problem-solving or code generation.

Summary

Self-consistency is a decoding strategy where multiple reasoning paths are sampled and the most frequent answer is selected as the final output.

PagedAttention

Sat, 18 Jul 2026 10:10:06 +0000

Definition

PagedAttention is a technique introduced by the vLLM project to improve the efficiency of Large Language Model inference. It addresses the fragmentation and overhead issues in managing the KV cache, which stores attention states for generating tokens. By treating the KV cache like virtual memory pages, PagedAttention allows for dynamic allocation and sharing of memory blocks between sequences. This results in significant reductions in memory waste and enables higher batch sizes and throughput without requiring hardware changes.

Inferential theory of learning

Sat, 18 Jul 2026 10:02:49 +0000

Definition

This theory posits that learning is essentially a process of probabilistic inference. Instead of memorizing data, the learner maintains a probability distribution over possible models or hypotheses. As new data arrives, Bayes’ theorem is used to update these probabilities, refining the model’s understanding of the underlying structure. It emphasizes generalization through uncertainty quantification rather than point estimates.

Summary

A framework where learning is viewed as Bayesian inference, updating beliefs about hypotheses based on observed data.

Expectation propagation

Sat, 18 Jul 2026 09:57:25 +0000

Definition

Expectation Propagation (EP) approximates intractable integrals by iteratively refining Gaussian approximations to the true posterior distribution. It minimizes the Kullback-Leibler divergence between the approximate and true distributions by matching moments. EP is widely used in Bayesian machine learning for tasks like classification and regression where exact inference is computationally prohibitive, offering a balance between accuracy and efficiency.

Summary

An approximate inference algorithm used to estimate posterior distributions in complex probabilistic graphical models.

Eager learning

Sat, 18 Jul 2026 09:56:25 +0000

Definition

In eager learning, the system constructs a general target function or model based on the training data before encountering new instances. This contrasts with lazy learning, which delays generalization until classification time. Because the computational effort is concentrated during the training phase, eager learners typically offer very fast inference speeds, making them suitable for real-time applications. However, they may require significant memory to store the trained model and can be sensitive to noisy data if the model overfits. Common examples include neural networks, decision trees, and support vector machines.

Bayesian programming

Sat, 18 Jul 2026 09:48:06 +0000

Definition

Bayesian programming is a mathematical framework that generalizes Bayes’ theorem to handle complex, multi-layered probabilistic dependencies. It allows developers to define hierarchical models where variables depend on other variables in a structured way. This approach is particularly useful for reasoning under uncertainty in dynamic environments, enabling systems to update beliefs as new evidence becomes available. It provides a rigorous foundation for building robust machine learning models that can manage incomplete or noisy data effectively.

Bayesian learning mechanisms

Sat, 18 Jul 2026 09:47:51 +0000

Definition

Bayesian learning mechanisms update beliefs about model parameters using Bayes’ theorem, combining prior knowledge with observed data to form a posterior distribution. Unlike frequentist approaches that seek point estimates, these methods provide a full distribution over possible parameter values, enabling natural regularization and uncertainty quantification. Common techniques include Variational Inference and Markov Chain Monte Carlo sampling, which approximate the posterior when exact computation is intractable.

Summary

Learning paradigms that treat model parameters as random variables with probability distributions rather than fixed values.

Monte

Sat, 18 Jul 2026 09:34:02 +0000

Definition

Monte Carlo techniques are a class of computational algorithms that rely on repeated random sampling to estimate mathematical quantities. They are particularly useful in high-dimensional integration, optimization, and probabilistic inference where closed-form solutions are unavailable. By generating thousands or millions of random scenarios, these methods approximate the expected value or distribution of outcomes. In AI, they are essential for Bayesian inference, reinforcement learning exploration strategies, and evaluating complex risk models in uncertain environments.

Causal

Sat, 18 Jul 2026 09:30:47 +0000

Definition

In artificial intelligence, causal modeling seeks to understand how interventions on one variable affect another. Unlike predictive models that rely on observed patterns, causal AI uses structural equations or directed acyclic graphs to simulate outcomes under hypothetical scenarios. This approach is critical for decision-making systems where understanding the underlying mechanism of an event is necessary to predict the impact of specific actions or policy changes.

Summary

Causal inference involves determining cause-and-effect relationships between variables rather than just identifying statistical correlations.