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Top-k sampling is a probabilistic method used in natural language processing to generate text by selecting the next word from the k most likely options. It balances creativity and coherence in language models by limiting the choice to a subset of probable candidates.
RoBERTa is a transformer-based language model developed by Facebook AI Research as an optimized variant of BERT. It improves language understanding by training with larger datasets and modified training strategies.
OPT (Open Pre-trained Transformer) is a large-scale language model developed to provide an open alternative to proprietary models. It is designed to facilitate research and transparency in natural language processing by offering publicly accessible weights and code.
Prompt tuning is a technique in natural language processing that adapts large language models to specific tasks by optimizing a small set of prompt parameters instead of fine-tuning the entire model. It offers a parameter-efficient alternative to traditional model fine-tuning, enabling task adaptation with reduced computational resources.
BLOOM is a large open-access multilingual language model developed to facilitate natural language processing tasks across numerous languages. It is notable for its collaborative development and emphasis on openness in artificial intelligence.
Perplexity is a measurement used primarily in the fields of information theory and natural language processing to quantify how well a probability model predicts a sample. It reflects the uncertainty or unpredictability of a probability distribution, often applied in evaluating language models.
GPT (Generative Pre-trained Transformer) is an advanced language model developed by OpenAI that utilizes deep learning to generate human-like text based on input prompts.
A chatbot is a computer program designed to simulate human conversation through text or speech interactions. It uses artificial intelligence and natural language processing to understand user inputs and provide relevant responses, often employed in customer service, information retrieval, and entertainment.
Role prompting is a technique used in artificial intelligence and natural language processing where a system is given a specific role or persona to guide its responses. This method helps shape the behavior and output of AI models by instructing them to adopt particular perspectives or functions.
Zero-shot prompting is a technique in natural language processing where a model is given instructions or queries without prior examples, enabling it to perform tasks it was not explicitly trained on. This approach leverages the generalization capabilities of large language models to interpret and respond to new prompts directly.