100 Complex LLM Terminology Explained in One Single & One Simple Sentence

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