Language Model: A statistical model that learns patterns and relationships in text data to generate human-like text.
Transformer: A neural network architecture that uses self-attention mechanisms to process sequential data.
GPT (Generative Pre-trained Transformer): A type of language model that generates text based on patterns learned from pre-training on large text datasets.
Fine-tuning: The process of adapting a pre-trained language model to a specific task or domain by training it on a smaller dataset.
Few-shot Learning: A learning approach where a model can learn from a small number of examples.
Zero-shot Learning: A learning approach where a model can perform a task without any task-specific training examples.
Prompt Engineering: The process of designing effective prompts to guide the language model in generating desired outputs.
Tokenization: The process of breaking down text into smaller units called tokens, such as words or subwords.
Embeddings: Dense vector representations of words or tokens that capture their semantic meaning.
Attention: A mechanism that allows the model to focus on relevant parts of the input when generating output.
Self-attention: A type of attention where the model attends to different parts of its own input.
Multi-head Attention: An extension of self-attention that allows the model to attend to information from different representation subspaces.
Positional Encoding: A technique used to inject information about the position of tokens in a sequence into the model.
Layer Normalization: A technique used to normalize the activations of neurons in a layer to stabilize training.
Residual Connection: A skip connection that allows information to bypass one or more layers in the network.
Dropout: A regularization technique that randomly drops out neurons during training to prevent overfitting.
Beam Search: A decoding algorithm that explores multiple probable sequences and selects the best one based on a scoring function.
Nucleus Sampling: A decoding method that samples from the most probable tokens based on a probability threshold.
Top-k Sampling: A decoding method that samples from the top k most probable tokens at each step.
Perplexity: A metric that measures how well a language model predicts a sample of text.
BLEU Score: A metric used to evaluate the quality of machine-generated text by comparing it to reference text.
ROUGE Score: A set of metrics used to evaluate the quality of summarization models.
Fluency: The ability of a language model to generate grammatically correct and coherent text.
Coherence: The logical and consistent flow of ideas in the generated text.
Diversity: The variety and uniqueness of the generated text, avoiding repetition and dullness.
Hallucination: A phenomenon where the language model generates plausible but factually incorrect information.
Bias: The tendency of a language model to generate text that reflects societal biases present in the training data.
Toxicity: The presence of harmful, offensive, or discriminatory content in the generated text.
Controllability: The ability to guide the language model’s output based on specific attributes or constraints.
Style Transfer: The task of rewriting text in a different style while preserving its content.
Summarization: The task of generating a concise version of a longer text while retaining key information.
Translation: The task of converting text from one language to another.
Question Answering: The task of providing accurate answers to questions based on given context.
Named Entity Recognition (NER): The task of identifying and classifying named entities (e.g., person, organization, location) in text.
Sentiment Analysis: The task of determining the sentiment (positive, negative, or neutral) expressed in a piece of text.
Text Classification: The task of assigning predefined categories or labels to a given text.
Text Generation: The task of generating human-like text based on a given prompt or context.
Language Translation: The task of translating text from one language to another while preserving meaning.
Text-to-Speech (TTS): The task of converting written text into spoken words.
Speech-to-Text (STT): The task of converting spoken words into written text.
Image Captioning: The task of generating a textual description of an image.
Text-to-Image Generation: The task of generating an image based on a textual description.
Knowledge Distillation: The process of transferring knowledge from a larger model to a smaller one.
Quantization: The process of reducing the precision of model weights to reduce memory footprint and computational cost.
Pruning: The process of removing unimportant weights or connections from a model to reduce its size.
Federated Learning: A distributed learning approach where models are trained on decentralized data without sharing raw data.
Differential Privacy: A technique used to protect the privacy of individuals in the training data.
Adversarial Training: A technique used to improve a model’s robustness by training it on adversarial examples.
Transfer Learning: The process of leveraging knowledge learned from one task to improve performance on another related task.
Multitask Learning: The process of training a model to perform multiple tasks simultaneously.
Continual Learning: The ability of a model to learn new tasks without forgetting previously learned knowledge.
Few-shot Adaptation: The process of adapting a pre-trained model to a new task with only a few examples.
Meta-learning: The process of learning to learn, where a model learns a general strategy to adapt to new tasks quickly.
Reinforcement Learning: A learning approach where an agent learns to make decisions by interacting with an environment and receiving rewards.
Unsupervised Learning: A learning approach where the model learns patterns and structures from unlabeled data.
Semi-supervised Learning: A learning approach that combines a small amount of labeled data with a large amount of unlabeled data.
Self-supervised Learning: A learning approach where the model learns from automatically generated labels derived from the input data itself.
Contrastive Learning: A learning approach that trains a model to distinguish between similar and dissimilar examples.
Generative Adversarial Networks (GANs): A framework where two models, a generator and a discriminator, compete against each other to generate realistic data.
Variational Autoencoders (VAEs): A generative model that learns to encode data into a latent space and decode it back to the original space.
Autoregressive Models: A type of model that predicts the next token in a sequence based on the previous tokens.
Bidirectional Encoder Representations from Transformers (BERT): A pre-trained model that learns contextual representations of text using bidirectional training.
Robustness: The ability of a model to maintain performance under various perturbations or adversarial attacks.
Interpretability: The degree to which a model’s decisions and predictions can be understood and explained.
Explainability: The ability to provide human-understandable explanations for a model’s predictions or decisions.
Model Compression: Techniques used to reduce the size and computational requirements of a model while maintaining performance.
Knowledge Graphs: Structured representations of real-world entities and their relationships.
Entity Linking: The task of linking named entities in text to their corresponding entries in a knowledge base.
Commonsense Reasoning: The ability of a model to make inferences based on general world knowledge.
Multimodal Learning: The process of learning from multiple modalities, such as text, images, and audio.
Cross-lingual Transfer: The ability to transfer knowledge learned in one language to another language with limited resources.
Domain Adaptation: The process of adapting a model trained on one domain to perform well on a different but related domain.
Active Learning: A learning approach where the model actively selects informative examples for labeling to improve performance.
Curriculum Learning: A learning approach where the model is gradually exposed to more complex examples during training.
Lifelong Learning: The ability of a model to continuously learn and adapt to new tasks and environments over its lifetime.
Few-shot Generation: The task of generating new examples based on a small number of provided examples.
Data Augmentation: Techniques used to increase the size and diversity of the training data by applying transformations or generating synthetic examples.
Noisy Channel Modeling: A framework that models the generation process as a noisy channel and aims to recover the original input.
Masked Language Modeling: A pre-training objective where the model learns to predict masked tokens in a sequence.
Next Sentence Prediction: A pre-training objective where the model learns to predict whether two sentences follow each other in a coherent way.
Sequence-to-Sequence (Seq2Seq) Models: A type of model that maps an input sequence to an output sequence, commonly used for tasks like translation and summarization.
Attention Mechanisms: Techniques used to allow the model to focus on relevant parts of the input when generating the output.
Transformer-XL: An extension of the Transformer architecture that enables learning dependencies beyond a fixed-length context.
XLNet: A pre-trained model that combines the benefits of autoregressive and bidirectional training.
T5 (Text-to-Text Transfer Transformer): A pre-trained model that frames all tasks as text-to-text problems.
GPT-3 (Generative Pre-trained Transformer 3): A large-scale language model with 175 billion parameters, capable of performing various tasks with few-shot learning.
Few-shot Prompting: The technique of providing a small number of examples or demonstrations in the prompt to guide the model’s output.
In-context Learning: The ability of a model to learn from examples provided within the input context without explicit fine-tuning.
Prompt Tuning: A technique that optimizes continuous prompt embeddings while keeping the model parameters fixed.
Prefix-tuning: A technique that prepends a small number of trainable parameters to the input sequence for task-specific adaptation.
Adapter-based Tuning: A technique that inserts small trainable modules (adapters) between layers of a pre-trained model for task-specific adaptation.
Parameter-Efficient Fine-tuning: Techniques that fine-tune a small number of parameters while keeping most of the model fixed to reduce computational cost and memory footprint.
Low-rank Adaptation: A technique that learns low-rank updates to the model parameters for task-specific adaptation.
Sparse Fine-tuning: A technique that fine-tunes a sparse subset of the model parameters for task-specific adaptation.
Multilingual Models: Models that are trained on multiple languages and can handle tasks in different languages.
Code Generation: The task of generating programming code based on natural language descriptions or examples.
Dialogue Systems: Models that engage in conversational interactions with users, understanding context and generating appropriate responses.
Fact Checking: The task of verifying the accuracy of claims or statements against reliable sources of information.
Text Style Transfer: The task of rewriting text in a different style (e.g., formal to informal) while preserving its content.
Zero-shot Task Generalization: The ability of a model to perform tasks it was not explicitly trained on, based on its general language understanding capabilities.
Same Terminology, Even Simpler Terms
Language Model: A computer program that can understand and create human-like text.
Transformer: A type of language model that can process large amounts of text quickly.
GPT: A type of language model that can generate text that sounds like it was written by a human.
Fine-tuning: Teaching a language model to do a specific task, like writing stories or translating languages.
Few-shot Learning: Teaching a language model to do a task with only a few examples.
Prompt Engineering: Writing instructions that tell the language model what to do.
Tokenization: Breaking down text into smaller pieces, like words or letters.
Embeddings: Turning words into numbers that the language model can understand.
Attention: The language model’s ability to focus on important parts of the text.
Self-attention: The language model’s ability to pay attention to itself.
Positional Encoding: Telling the language model where each word is in the text.
Layer Normalization: Making sure the language model’s output is consistent.
Residual Connection: A shortcut that helps the language model learn faster.
Dropout: Randomly turning off parts of the language model to prevent it from overfitting.
Beam Search: A method for generating text that explores different possibilities.
Nucleus Sampling: A method for generating text that focuses on the most likely words.
Top-k Sampling: A method for generating text that chooses from the top k most likely words.
Perplexity: A measure of how well the language model predicts the next word in a text.
BLEU Score: A measure of how similar the language model’s output is to human-written text.
ROUGE Score: A measure of how well the language model summarizes text.
Fluency: How smoothly and naturally the language model’s output flows.
Coherence: How well the language model’s output makes sense.
Diversity: How varied and unique the language model’s output is.
Hallucination: When the language model makes up information that isn’t in the input text.
Bias: When the language model’s output reflects unfair or inaccurate stereotypes.
Toxicity: When the language model’s output is harmful or offensive.
Controllability: How well the language model can follow specific instructions.
Style Transfer: Changing the style of the language model’s output, like from formal to informal.
Summarization: Creating a shorter version of a text that captures the main points.
Translation: Converting text from one language to another.
Question Answering: Answering questions based on a given text.
Named Entity Recognition: Identifying and classifying important words in a text, like names and places.
Sentiment Analysis: Determining whether a text expresses positive or negative emotions.
Text Classification: Categorizing a text into different groups, like news or sports.
Text Generation: Creating new text based on a given prompt or context.
Language Translation: Converting text from one language to another.
Text-to-Speech: Converting written text into spoken words.
Speech-to-Text: Converting spoken words into written text.
Image Captioning: Describing an image with words.
Text-to-Image Generation: Creating an image based on a written description.
Knowledge Distillation: Transferring knowledge from a large language model to a smaller one.
Quantization: Reducing the size of a language model without losing accuracy.
Pruning: Removing unnecessary parts of a language model to make it smaller.
Federated Learning: Training a language model on data from different devices without sharing the data.
Differential Privacy: Protecting the privacy of individuals whose data is used to train a language model.
Adversarial Training: Making a language model more robust by training it on examples that are designed to fool it.
Transfer Learning: Using knowledge learned from one task to improve performance on a related task.
Multitask Learning: Training a language model to perform multiple tasks at the same time.
Continual Learning: Allowing a language model to learn new tasks without forgetting old ones.
Few-shot Adaptation: Adapting a language model to a new task with only a few examples.
Meta-learning: Teaching a language model how to learn new tasks quickly.
Reinforcement Learning: Training a language model by rewarding it for good behavior.
Unsupervised Learning: Training a language model on data that is not labeled.
Semi-supervised Learning: Training a language model on a mix of labeled and unlabeled data.
Self-supervised Learning: Training a language model on data that is automatically labeled.
Contrastive Learning: Training a language model to distinguish between similar and different examples.
Generative Adversarial Networks: Two language models that compete to create realistic data.
Variational Autoencoders: A language model that can generate new data from a learned distribution.
Autoregressive Models: Language models that predict the next word in a sequence based on the previous words.
Bidirectional Encoder Representations from Transformers: A language model that can understand the context of words in a sentence.
Robustness: How well a language model performs under different conditions.
Interpretability: How easy it is to understand why a language model makes certain predictions.
Explainability: How well a language model can explain its predictions to humans.
Model Compression: Reducing the size and computational requirements of a language model.
Knowledge Graphs: Structured databases of real-world knowledge.
Entity Linking: Connecting words in a text to entries in a knowledge graph.
Commonsense Reasoning: The ability of a language model to make logical inferences based on general knowledge.
Multimodal Learning: Training a language model on multiple types of data, like text, images, and audio.
Cross-lingual Transfer: Transferring knowledge learned in one language to another language.
Domain Adaptation: Adapting a language model to perform well on a different but related domain.
Active Learning: Selecting the most informative examples to train a language model.
Curriculum Learning: Gradually exposing a language model to more complex examples during training.
Lifelong Learning: Allowing a language model to continuously learn and adapt over its lifetime.
Few-shot Generation: Generating new examples based on a small number of provided examples.
Data Augmentation: Increasing the size and diversity of a training dataset by applying transformations or generating synthetic examples.
Noisy Channel Modeling: Modeling the generation process as a noisy channel and aiming to recover the original input.
Masked Language Modeling: Predicting masked words in a sequence.
Next Sentence Prediction: Predicting whether two sentences follow each other in a coherent way.
Sequence-to-Sequence Models: Mapping an input sequence to an output sequence.
Attention Mechanisms: Allowing the language model to focus on relevant parts of the input.
Transformer-XL: A Transformer architecture that can learn dependencies beyond a fixed-length context.
XLNet: A language model that combines autoregressive and bidirectional training.
T5: A language model that frames all tasks as text-to-text problems.
GPT-3: A large-scale language model with 175 billion parameters.
Few-shot Prompting: Providing a small number of examples or demonstrations in the prompt.
In-context Learning: Learning from examples provided within the input context.
Prompt Tuning: Optimizing continuous prompt embeddings.
Prefix-tuning: Prepending a small number of trainable parameters to the input sequence.
Adapter-based Tuning: Inserting small trainable modules between layers of a pre-trained model.
Parameter-Efficient Fine-tuning: Fine-tuning a small number of parameters.
Low-rank Adaptation: Learning low-rank updates to the model parameters.
Sparse Fine-tuning: Fine-tuning a sparse subset of the model parameters.
Multilingual Models: Models that can handle tasks in different languages.
Code Generation: Generating programming code based on natural language descriptions.
Dialogue Systems: Models that engage in conversational interactions.
Fact Checking: Verifying the accuracy of claims or statements.
Text Style Transfer: Rewriting text in a different style.
Zero-shot Task Generalization: Performing tasks without explicit training.