@mlresearcher

Large Language Model - A type of AI trained on massive text datasets to understand and generate human language. Examples include GPT, Claude, and Gemini.

“The LLM was able to write working code after just a brief description of the requirements.”

The maximum amount of text (measured in tokens) that a language model can process and "remember" in a single interaction. Information outside the context window is inaccessible to the model, making context management critical for long-form tasks.

“The model kept losing track of earlier instructions because the codebase exceeded its context window.”

The process of further training a pre-trained model on a smaller, task-specific dataset to adapt its behavior for a particular domain or style. Fine-tuning updates the model's weights to make it perform better on specific tasks without training from scratch.

“We fine-tuned the base model on our legal contracts corpus so it could draft clauses in the right style.”

Describing AI systems capable of processing and generating multiple types of data — such as text, images, audio, and video — in a unified model. Multimodal AI can answer questions about images, generate images from text, transcribe speech, and reason across modalities simultaneously.

“The multimodal model analyzed the chart image and provided a written summary of the trends.”

The ability of a model to perform a task it has never been explicitly trained or shown examples for. Zero-shot learning relies on the model's generalized understanding from pretraining to handle novel tasks based on instruction alone.

“The model classified customer sentiment zero-shot without any labeled training examples.”

The act of running a trained machine learning model on new input data to generate predictions or outputs. Inference is distinct from training — it is the "serving" phase where the model is used in production, and its speed and cost are critical for real-world applications.

“Inference latency dropped from 2 seconds to 200ms after switching to a quantized model.”

The process of converting raw text into discrete units called tokens that a language model can process. Tokens are typically subword units — common words become single tokens while rare words split into multiple tokens. All LLM pricing and context limits are measured in tokens, not characters or words.

“The word "unbelievable" tokenized into three pieces: "un", "believ", "able".”

A neural network architecture introduced in 2017 ("Attention Is All You Need") that underlies virtually all modern language models. Transformers use self-attention mechanisms to process entire sequences in parallel, capturing long-range dependencies that earlier recurrent architectures struggled with.

“Every major LLM from GPT to Claude is built on the transformer architecture.”

A computational model loosely inspired by biological neurons, consisting of interconnected layers of mathematical functions (nodes) that transform input data into output predictions. Neural networks learn by adjusting the weights of connections through exposure to training data.

“The neural network learned to recognize handwritten digits with over 99% accuracy.”

Artificially generated data that mimics the statistical properties of real-world data, used for training or testing AI models. Synthetic data can be created by generative models, rule-based systems, or simulations, and is especially valuable when real data is scarce, sensitive, or expensive to collect.

“We generated synthetic medical records to train the model without risking patient privacy.”