1. Large Language Model (LLM)

• A type of artificial intelligence model designed to understand, generate, and manipulate human language by predicting the next word in a sequence.

2. Transformer Architecture

• A neural network architecture that uses self-attention mechanisms to process sequences of data, foundational for many LLMs.

3. Self-Attention Mechanism

• A technique within transformers where each word in a sentence is weighted by its relevance to other words, allowing the model to capture context more effectively.

4. Tokenization

• The process of converting text into tokens (words, subwords, or characters) that the model can process.

5. GPT (Generative Pre-trained Transformer)

• A series of LLMs developed by OpenAI that are pre-trained on a large corpus of text and fine-tuned for specific tasks.

6. Pre-training

• The initial phase where a model is trained on a vast dataset to learn general language patterns.

7. Fine-tuning

• The process of further training a pre-trained model on a smaller, task-specific dataset to adapt it for specific tasks.

8. Context Window

• The amount of text the model can consider at once, typically defined by the number of tokens.

9. BERT (Bidirectional Encoder Representations from Transformers)

• A type of LLM designed to understand the context of a word in a sentence by looking at the words before and after it.

10. Masked Language Model (MLM)

• A training approach used in models like BERT where certain words are masked and the model is trained to predict them.

11. Zero-Shot Learning

• The ability of a model to perform a task without having been explicitly trained on examples of that task.

12. Few-Shot Learning

• The model's ability to perform a task after being given only a few examples.

13. Prompt Engineering

• The process of designing inputs (prompts) to guide the behavior of an LLM to generate desired outputs.

14. Natural Language Processing (NLP)

• The field of AI focused on the interaction between computers and human language.

15. Sequence-to-Sequence Model (Seq2Seq)

• A model architecture used for tasks where the input is a sequence of tokens and the output is another sequence, like translation.

16. Decoder

• The part of a transformer model responsible for generating output sequences from encoded inputs.

17. Encoder

• The component of a transformer that processes the input sequence and encodes it into a format suitable for decoding.

18. Attention Head

• One of the multiple sub-units within the self-attention mechanism, each capturing different aspects of the input context.

19. Multi-Head Attention

• A process where multiple attention heads work in parallel to capture various types of relationships in the data.

20. Position Embedding

• The technique used to add information about the position of words in a sequence to the model, since transformers don’t inherently understand word order.

21. Embedding

• A dense vector representation of words, sentences, or other data types that captures semantic meaning.

22. Transfer Learning

• The process of using a pre-trained model on a new, related task, taking advantage of the knowledge the model has already learned.

23. Backpropagation

• The algorithm used to adjust the weights in a neural network during training by propagating the error backward through the network.

24. Gradient Descent

• An optimization algorithm used to minimize the error in the model by iteratively adjusting the model's parameters.

25. Overfitting

• A situation where a model performs well on the training data but poorly on unseen data due to excessive complexity.

26. Underfitting

• When a model is too simple to capture the underlying patterns in the data, leading to poor performance on both training and test data.

27. Hyperparameters

• The parameters of a model that are set before training begins, such as learning rate, batch size, and the number of layers.

28. Epoch

• One complete pass through the entire training dataset.

29. Batch Size

• The number of training examples used in one iteration of training.

30. Learning Rate

• A hyperparameter that controls how much to change the model in response to the estimated error each time the model's weights are updated.

31. Regularization

• Techniques used to prevent overfitting by penalizing complex models, such as L2 regularization.

32. Dropout

• A regularization technique where random units in the network are ignored during training to prevent overfitting.

33. Activation Function

• A function applied to the output of a neural network layer, introducing non-linearity into the model (e.g., ReLU, Sigmoid).

34. Softmax

• An activation function often used in the output layer of a classifier to convert logits to probabilities.

35. Logits

• The raw, unnormalized scores output by a model before applying a softmax function.

36. Language Model

• A statistical model that assigns probabilities to sequences of words or tokens, predicting the likelihood of a given sequence.

37. Beam Search

• A search algorithm used to generate sequences by keeping track of multiple sequences at each step and only keeping the best ones.

38. Perplexity

• A measurement of how well a language model predicts a sample; lower perplexity indicates better performance.

39. Latent Space

• The abstract, multi-dimensional space where the model represents different features or concepts learned during training.

40. Neural Network

• A series of algorithms that mimic the human brain to recognize patterns and make decisions.

41. Parameters

• The weights and biases within a model that are learned from data during training.

42. Evaluation

• The process of evaluating the capabilities and quality of the LLM, typically against a benchmark dataset or by humans.

43. Attention Score

• The value calculated during the self-attention process that determines the importance of one word to another in a sequence.

44. Language Understanding

• The ability of an AI model to comprehend and make sense of human language, beyond just processing it.

45. Inference

• The process of an AI model taking an input and generating an output.

46. Natural Language Generation (NLG)

• The process of generating human-like text from a model, often used in chatbots, translation, and summarization.

47. Reinforcement Learning from Human Feedback (RLHF)

• A technique where a model is fine-tuned using feedback from humans to improve its performance on tasks.

48. Autoencoder

• A type of neural network used to learn efficient representations (codings) of data, typically for dimensionality reduction or noise reduction.

49. Cross-Entropy Loss

• A common loss function used in classification problems that measures the difference between the predicted and actual distributions.

50. Knowledge Distillation

• A technique where a smaller model (student) is trained to mimic the behavior of a larger model (teacher) to reduce complexity while maintaining performance.

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