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

1. Language Model: A statistical model that learns patterns and relationships in text data to generate human-like text.
2. Transformer: A neural network architecture that uses self-attention mechanisms to process sequential data.
3. GPT (Generative Pre-trained Transformer): A type of language model that generates text based on patterns learned from pre-training on large text datasets.
4. Fine-tuning: The process of adapting a pre-trained language model to a specific task or domain by training it on a smaller dataset.
5. Few-shot Learning: A learning approach where a model can learn from a small number of examples.
6. Zero-shot Learning: A learning approach where a model can perform a task without any task-specific training examples.
7. Prompt Engineering: The process of designing effective prompts to guide the language model in generating desired outputs.
8. Tokenization: The process of breaking down text into smaller units called tokens, such as words or subwords.
9. Embeddings: Dense vector representations of words or tokens that capture their semantic meaning.
10. Attention: A mechanism that allows the model to focus on relevant parts of the input when generating output.
11. Self-attention: A type of attention where the model attends to different parts of its own input.
12. Multi-head Attention: An extension of self-attention that allows the model to attend to information from different representation subspaces.
13. Positional Encoding: A technique used to inject information about the position of tokens in a sequence into the model.
14. Layer Normalization: A technique used to normalize the activations of neurons in a layer to stabilize training.
15. Residual Connection: A skip connection that allows information to bypass one or more layers in the network.
16. Dropout: A regularization technique that randomly drops out neurons during training to prevent overfitting.
17. Beam Search: A decoding algorithm that explores multiple probable sequences and selects the best one based on a scoring function.
18. Nucleus Sampling: A decoding method that samples from the most probable tokens based on a probability threshold.
19. Top-k Sampling: A decoding method that samples from the top k most probable tokens at each step.
20. Perplexity: A metric that measures how well a language model predicts a sample of text.
21. BLEU Score: A metric used to evaluate the quality of machine-generated text by comparing it to reference text.
22. ROUGE Score: A set of metrics used to evaluate the quality of summarization models.
23. Fluency: The ability of a language model to generate grammatically correct and coherent text.
24. Coherence: The logical and consistent flow of ideas in the generated text.
25. Diversity: The variety and uniqueness of the generated text, avoiding repetition and dullness.
26. Hallucination: A phenomenon where the language model generates plausible but factually incorrect information.
27. Bias: The tendency of a language model to generate text that reflects societal biases present in the training data.
28. Toxicity: The presence of harmful, offensive, or discriminatory content in the generated text.
29. Controllability: The ability to guide the language model's output based on specific attributes or constraints.
30. Style Transfer: The task of rewriting text in a different style while preserving its content.
31. Summarization: The task of generating a concise version of a longer text while retaining key information.
32. Translation: The task of converting text from one language to another.
33. Question Answering: The task of providing accurate answers to questions based on given context.
34. Named Entity Recognition (NER): The task of identifying and classifying named entities (e.g., person, organization, location) in text.
35. Sentiment Analysis: The task of determining the sentiment (positive, negative, or neutral) expressed in a piece of text.
36. Text Classification: The task of assigning predefined categories or labels to a given text.
37. Text Generation: The task of generating human-like text based on a given prompt or context.
38. Language Translation: The task of translating text from one language to another while preserving meaning.
39. Text-to-Speech (TTS): The task of converting written text into spoken words.
40. Speech-to-Text (STT): The task of converting spoken words into written text.
41. Image Captioning: The task of generating a textual description of an image.
42. Text-to-Image Generation: The task of generating an image based on a textual description.
43. Knowledge Distillation: The process of transferring knowledge from a larger model to a smaller one.
44. Quantization: The process of reducing the precision of model weights to reduce memory footprint and computational cost.
45. Pruning: The process of removing unimportant weights or connections from a model to reduce its size.
46. Federated Learning: A distributed learning approach where models are trained on decentralized data without sharing raw data.
47. Differential Privacy: A technique used to protect the privacy of individuals in the training data.
48. Adversarial Training: A technique used to improve a model's robustness by training it on adversarial examples.
49. Transfer Learning: The process of leveraging knowledge learned from one task to improve performance on another related task.
50. Multitask Learning: The process of training a model to perform multiple tasks simultaneously.
51. Continual Learning: The ability of a model to learn new tasks without forgetting previously learned knowledge.
52. Few-shot Adaptation: The process of adapting a pre-trained model to a new task with only a few examples.
53. Meta-learning: The process of learning to learn, where a model learns a general strategy to adapt to new tasks quickly.
54. Reinforcement Learning: A learning approach where an agent learns to make decisions by interacting with an environment and receiving rewards.
55. Unsupervised Learning: A learning approach where the model learns patterns and structures from unlabeled data.
56. Semi-supervised Learning: A learning approach that combines a small amount of labeled data with a large amount of unlabeled data.
57. Self-supervised Learning: A learning approach where the model learns from automatically generated labels derived from the input data itself.
58. Contrastive Learning: A learning approach that trains a model to distinguish between similar and dissimilar examples.
59. Generative Adversarial Networks (GANs): A framework where two models, a generator and a discriminator, compete against each other to generate realistic data.
60. Variational Autoencoders (VAEs): A generative model that learns to encode data into a latent space and decode it back to the original space.
61. Autoregressive Models: A type of model that predicts the next token in a sequence based on the previous tokens.
62. Bidirectional Encoder Representations from Transformers (BERT): A pre-trained model that learns contextual representations of text using bidirectional training.
63. Robustness: The ability of a model to maintain performance under various perturbations or adversarial attacks.
64. Interpretability: The degree to which a model's decisions and predictions can be understood and explained.
65. Explainability: The ability to provide human-understandable explanations for a model's predictions or decisions.
66. Model Compression: Techniques used to reduce the size and computational requirements of a model while maintaining performance.
67. Knowledge Graphs: Structured representations of real-world entities and their relationships.
68. Entity Linking: The task of linking named entities in text to their corresponding entries in a knowledge base.
69. Commonsense Reasoning: The ability of a model to make inferences based on general world knowledge.
70. Multimodal Learning: The process of learning from multiple modalities, such as text, images, and audio.
71. Cross-lingual Transfer: The ability to transfer knowledge learned in one language to another language with limited resources.
72. Domain Adaptation: The process of adapting a model trained on one domain to perform well on a different but related domain.
73. Active Learning: A learning approach where the model actively selects informative examples for labeling to improve performance.
74. Curriculum Learning: A learning approach where the model is gradually exposed to more complex examples during training.
75. Lifelong Learning: The ability of a model to continuously learn and adapt to new tasks and environments over its lifetime.
76. Few-shot Generation: The task of generating new examples based on a small number of provided examples.
77. Data Augmentation: Techniques used to increase the size and diversity of the training data by applying transformations or generating synthetic examples.
78. Noisy Channel Modeling: A framework that models the generation process as a noisy channel and aims to recover the original input.
79. Masked Language Modeling: A pre-training objective where the model learns to predict masked tokens in a sequence.
80. Next Sentence Prediction: A pre-training objective where the model learns to predict whether two sentences follow each other in a coherent way.
81. 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.
82. Attention Mechanisms: Techniques used to allow the model to focus on relevant parts of the input when generating the output.
83. Transformer-XL: An extension of the Transformer architecture that enables learning dependencies beyond a fixed-length context.
84. XLNet: A pre-trained model that combines the benefits of autoregressive and bidirectional training.
85. T5 (Text-to-Text Transfer Transformer): A pre-trained model that frames all tasks as text-to-text problems.
86. 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.
87. Few-shot Prompting: The technique of providing a small number of examples or demonstrations in the prompt to guide the model's output.
88. In-context Learning: The ability of a model to learn from examples provided within the input context without explicit fine-tuning.
89. Prompt Tuning: A technique that optimizes continuous prompt embeddings while keeping the model parameters fixed.
90. Prefix-tuning: A technique that prepends a small number of trainable parameters to the input sequence for task-specific adaptation.
91. Adapter-based Tuning: A technique that inserts small trainable modules (adapters) between layers of a pre-trained model for task-specific adaptation.
92. 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.
93. Low-rank Adaptation: A technique that learns low-rank updates to the model parameters for task-specific adaptation.
94. Sparse Fine-tuning: A technique that fine-tunes a sparse subset of the model parameters for task-specific adaptation.
95. Multilingual Models: Models that are trained on multiple languages and can handle tasks in different languages.
96. Code Generation: The task of generating programming code based on natural language descriptions or examples.
97. Dialogue Systems: Models that engage in conversational interactions with users, understanding context and generating appropriate responses.
98. Fact Checking: The task of verifying the accuracy of claims or statements against reliable sources of information.
99. Text Style Transfer: The task of rewriting text in a different style (e.g., formal to informal) while preserving its content.
100. 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

1. Language Model: A computer program that can understand and create human-like text.
2. Transformer: A type of language model that can process large amounts of text quickly.
3. GPT: A type of language model that can generate text that sounds like it was written by a human.
4. Fine-tuning: Teaching a language model to do a specific task, like writing stories or translating languages.
5. Few-shot Learning: Teaching a language model to do a task with only a few examples.
6. Prompt Engineering: Writing instructions that tell the language model what to do.
7. Tokenization: Breaking down text into smaller pieces, like words or letters.
8. Embeddings: Turning words into numbers that the language model can understand.
9. Attention: The language model's ability to focus on important parts of the text.
10. Self-attention: The language model's ability to pay attention to itself.
11. Positional Encoding: Telling the language model where each word is in the text.
12. Layer Normalization: Making sure the language model's output is consistent.
13. Residual Connection: A shortcut that helps the language model learn faster.
14. Dropout: Randomly turning off parts of the language model to prevent it from overfitting.
15. Beam Search: A method for generating text that explores different possibilities.
16. Nucleus Sampling: A method for generating text that focuses on the most likely words.
17. Top-k Sampling: A method for generating text that chooses from the top k most likely words.
18. Perplexity: A measure of how well the language model predicts the next word in a text.
19. BLEU Score: A measure of how similar the language model's output is to human-written text.
20. ROUGE Score: A measure of how well the language model summarizes text.
21. Fluency: How smoothly and naturally the language model's output flows.
22. Coherence: How well the language model's output makes sense.
23. Diversity: How varied and unique the language model's output is.
24. Hallucination: When the language model makes up information that isn't in the input text.
25. Bias: When the language model's output reflects unfair or inaccurate stereotypes.
26. Toxicity: When the language model's output is harmful or offensive.
27. Controllability: How well the language model can follow specific instructions.
28. Style Transfer: Changing the style of the language model's output, like from formal to informal.
29. Summarization: Creating a shorter version of a text that captures the main points.
30. Translation: Converting text from one language to another.
31. Question Answering: Answering questions based on a given text.
32. Named Entity Recognition: Identifying and classifying important words in a text, like names and places.
33. Sentiment Analysis: Determining whether a text expresses positive or negative emotions.
34. Text Classification: Categorizing a text into different groups, like news or sports.
35. Text Generation: Creating new text based on a given prompt or context.
36. Language Translation: Converting text from one language to another.
37. Text-to-Speech: Converting written text into spoken words.
38. Speech-to-Text: Converting spoken words into written text.
39. Image Captioning: Describing an image with words.
40. Text-to-Image Generation: Creating an image based on a written description.
41. Knowledge Distillation: Transferring knowledge from a large language model to a smaller one.
42. Quantization: Reducing the size of a language model without losing accuracy.
43. Pruning: Removing unnecessary parts of a language model to make it smaller.
44. Federated Learning: Training a language model on data from different devices without sharing the data.
45. Differential Privacy: Protecting the privacy of individuals whose data is used to train a language model.
46. Adversarial Training: Making a language model more robust by training it on examples that are designed to fool it.
47. Transfer Learning: Using knowledge learned from one task to improve performance on a related task.
48. Multitask Learning: Training a language model to perform multiple tasks at the same time.
49. Continual Learning: Allowing a language model to learn new tasks without forgetting old ones.
50. Few-shot Adaptation: Adapting a language model to a new task with only a few examples.
51. Meta-learning: Teaching a language model how to learn new tasks quickly.
52. Reinforcement Learning: Training a language model by rewarding it for good behavior.
53. Unsupervised Learning: Training a language model on data that is not labeled.
54. Semi-supervised Learning: Training a language model on a mix of labeled and unlabeled data.
55. Self-supervised Learning: Training a language model on data that is automatically labeled.
56. Contrastive Learning: Training a language model to distinguish between similar and different examples.
57. Generative Adversarial Networks: Two language models that compete to create realistic data.
58. Variational Autoencoders: A language model that can generate new data from a learned distribution.
59. Autoregressive Models: Language models that predict the next word in a sequence based on the previous words.
60. Bidirectional Encoder Representations from Transformers: A language model that can understand the context of words in a sentence.
61. Robustness: How well a language model performs under different conditions.
62. Interpretability: How easy it is to understand why a language model makes certain predictions.
63. Explainability: How well a language model can explain its predictions to humans.
64. Model Compression: Reducing the size and computational requirements of a language model.
65. Knowledge Graphs: Structured databases of real-world knowledge.
66. Entity Linking: Connecting words in a text to entries in a knowledge graph.
67. Commonsense Reasoning: The ability of a language model to make logical inferences based on general knowledge.
68. Multimodal Learning: Training a language model on multiple types of data, like text, images, and audio.
69. Cross-lingual Transfer: Transferring knowledge learned in one language to another language.
70. Domain Adaptation: Adapting a language model to perform well on a different but related domain.
71. Active Learning: Selecting the most informative examples to train a language model.
72. Curriculum Learning: Gradually exposing a language model to more complex examples during training.
73. Lifelong Learning: Allowing a language model to continuously learn and adapt over its lifetime.
74. Few-shot Generation: Generating new examples based on a small number of provided examples.
75. Data Augmentation: Increasing the size and diversity of a training dataset by applying transformations or generating synthetic examples.
76. Noisy Channel Modeling: Modeling the generation process as a noisy channel and aiming to recover the original input.
77. Masked Language Modeling: Predicting masked words in a sequence.
78. Next Sentence Prediction: Predicting whether two sentences follow each other in a coherent way.
79. Sequence-to-Sequence Models: Mapping an input sequence to an output sequence.
80. Attention Mechanisms: Allowing the language model to focus on relevant parts of the input.
81. Transformer-XL: A Transformer architecture that can learn dependencies beyond a fixed-length context.
82. XLNet: A language model that combines autoregressive and bidirectional training.
83. T5: A language model that frames all tasks as text-to-text problems.
84. GPT-3: A large-scale language model with 175 billion parameters.
85. Few-shot Prompting: Providing a small number of examples or demonstrations in the prompt.
86. In-context Learning: Learning from examples provided within the input context.
87. Prompt Tuning: Optimizing continuous prompt embeddings.
88. Prefix-tuning: Prepending a small number of trainable parameters to the input sequence.
89. Adapter-based Tuning: Inserting small trainable modules between layers of a pre-trained model.
90. Parameter-Efficient Fine-tuning: Fine-tuning a small number of parameters.
91. Low-rank Adaptation: Learning low-rank updates to the model parameters.
92. Sparse Fine-tuning: Fine-tuning a sparse subset of the model parameters.
93. Multilingual Models: Models that can handle tasks in different languages.
94. Code Generation: Generating programming code based on natural language descriptions.
95. Dialogue Systems: Models that engage in conversational interactions.
96. Fact Checking: Verifying the accuracy of claims or statements.
97. Text Style Transfer: Rewriting text in a different style.
98. Zero-shot Task Generalization: Performing tasks without explicit training.