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How does ChatGPT work?
ChatGPT works based on the Transformer architecture for natural language processing tasks. It uses a pre-trained deep neural network on a large corpus of text data to generate human-like responses to input text. The model has been fine-tuned for conversation-like language generation and has been trained on a massive amount of conversational data. At inference time, it takes an input sequence, encodes it using self-attention mechanisms, and generates a response by decoding the encoded representation with the help of a language generation model.
How does natural language processing work?
Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) concerned with the interaction between computers and humans using natural language. NLP tasks involve understanding, analyzing, and generating human language text. These tasks are performed using a combination of rule-based and statistical methods.
The process of NLP typically involves the following steps:
- Text Pre-processing: Cleaning, normalizing, and transforming raw text data into a format that can be analyzed by NLP algorithms.
- Tokenization: Breaking down text into individual words, phrases, or symbols.
- Part-of-Speech Tagging: Identifying and labeling the parts of speech (such as nouns, verbs, and adjectives) in the text.
- Parsing: Analyzing the grammar and structure of sentences to determine their meaning.
- Named Entity Recognition: Identifying and extracting named entities (such as people, organizations, and locations) from the text.
- Sentiment Analysis: Determining the emotional tone or opinion expressed in the text.
- Coreference Resolution: Identifying when two or more expressions in the text refer to the same entity.