understanding and generation. Explanation: Using OCI Data Science with a pre -trained Large Language Model (LLM) for natural language understanding and generation is the best approach for building a chatbot capable of handling diverse patient inquiries. Which of the following best describes the role of a Transformer in the context of Large Language Models (LLMs) and Generative AI?
Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine -tuning involves altering the model itself to improve its performance on specialized tasks. What can Oracle Cloud Infrastructure Document Understanding NOT do?
and effective mechanism to process sequential data in parallel while capturing long-range dependencies. This capability is essential for understanding and generating coherent and contextually appropriate text over extended sequences of input. Sequential Data Processing in Parallel: Traditional models, like Recurrent Neural Networks (RNNs), process sequences of data one step at a time, which can be slow and difficult to scale. In contrast, Transformers allow for the parallel processing of sequences, significantly speeding up the computation and making it feasible to train on large datasets. This parallelism is achieved through the self -attention mechanism, which enables the model to consider all parts of the input data simultaneously, rather than sequentially. Each token (word, punctuation, etc.) in the sequence is compared with every other token, allowing the model to weigh the importance of each part of the input relative to every other part. Capturing Long -Range Dependencies: Transformers excel at capturing long-range dependencies within data, which is crucial for understanding context in natural language processing tasks. For example, in a long sentence or paragraph, the meaning of a word can depend on other words that are far apart in the sequence. The self -attention mechanism in Transformers allows the model to capture these dependencies effectively by focusing on relevant parts of the text regardless of their position in the sequence. This ability to capture long-range dependencies enhances the model's understanding of context, leading to more coherent and accurate text generation. Applications in LLMs: In the context of GPT -4 and similar models, the Transformer architecture allows these models to generate text that is not only contextually appropriate but also maintains coherence across long passages, which is a significant improvement over earlier models. This is why the Transformer is the foundational architecture behind the success of GPT models. Reference: Transformers are a foundational architecture in LLMs, particularly because they enable parallel processing and capture long -range dependencies, which are essential for effective language understanding and generation. Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
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D. To deploy models as HTTP endpoints Explanation: The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment. In machine learning, what does the term "model training" mean?
What role do tokens play in Large Language Models (LLMs)?
