用户提问: ai大模型术语
Ai回答: Here are some common terms related to large AI models:
1、Transformer: A type of neural network architecture introduced in 2017 that's widely used in large language models、It's particularly well-suited for natural language processing tasks.
2、Large Language Model (LLM): A type of artificial intelligence model that's trained on vast amounts of text data to generate human-like language、Examples include BERT, RoBERTa, and transformer-XL.
3、Pre-training: The process of training a model on a large corpus of text data before fine-tuning it for a specific task、This allows the model to learn general language representations that can be adapted to various applications.
4、Fine-tuning: The process of adjusting a pre-trained model's weights to fit a specific task or dataset、This is typically done with a smaller amount of task-specific data.
5、Zero-shot learning: A technique where a model is trained on a large corpus of text and then applied to a new task or domain without requiring additional training data.
6、Few-shot learning: A technique where a model is trained on a small amount of task-specific data to adapt to a new task or domain.
7、Prompt engineering: The process of designing and optimizing input prompts to elicit specific responses from a large language model.
8、Generative model: A type of AI model that generates text, images, or other types of data、Large language models can be used as generative models to create text, such as chatbot responses or text summaries.
9、Tokenization: The process of breaking down text into individual tokens, such as words or subwords, that can be processed by a model.
10、Self-attention: A mechanism used in transformer models to weigh the importance of different input tokens relative to each other.
11、Encoder-decoder architecture: A type of model architecture that consists of an encoder that takes in input text and a decoder that generates output text.
12、Masked language modeling: A training objective where some input tokens are randomly replaced with a [MASK] token, and the model is trained to predict the original token.
13、Next sentence prediction: A training objective where the model is trained to predict whether two input sentences are adjacent or not.
14、Perplexity: A measure of how well a model predicts a test set、Lower perplexity indicates better performance.
15、Downstream task: A specific task or application that a pre-trained model is fine-tuned for, such as sentiment analysis or question answering.
Some popular large AI models include:
1、BERT (Bidirectional Encoder Representations from Transformers): A pre-trained language model developed by Google that achieved state-of-the-art results on a wide range of natural language processing tasks.
2、RoBERTa: A variant of BERT that uses a different approach to generate training data and has achieved even better results on some tasks.
3、Transformer-XL: A large language model that uses a novel approach to handling long-range dependencies in text.
4、Ernie: A large language model developed by Baidu that uses a knowledge graph to improve performance on certain tasks.
5、Megatron-LM: A large language model developed by NVIDIA that uses a combination of model parallelism and data parallelism to scale up training.
These are just a few of the many terms and concepts related to large AI models、If you have any specific questions or would like more information on a particular topic, feel free to ask!
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