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NEW QUESTION # 51
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?
Answer: A
Explanation:
The utilization of T-Few transformer layers contributes to the efficiency of the fine-tuning process by restricting updates to only a specific group of transformer layers. This selective updating approach allows the model to adapt to new data without the need to retrain all layers, thus saving computational resources and time. By focusing on the most relevant parts of the model, T-Few fine-tuning achieves efficient and effective performance improvements.
Reference
Research papers on T-Few fine-tuning techniques
Technical guides on optimizing transformer models
NEW QUESTION # 52
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?
Answer: C
Explanation:
Chain-of-Thought prompting involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response. This technique helps the model articulate its thought process and reasoning, leading to more transparent and understandable outputs. By breaking down the problem into smaller, logical steps, the model can provide more accurate and detailed responses.
Reference
Research articles on Chain-of-Thought prompting
Technical guides on enhancing model transparency and reasoning with intermediate steps
NEW QUESTION # 53
Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?
Answer: B
Explanation:
The Oracle Accelerated Data Science (ADS) class that can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment is GenerativeAI. This class provides the necessary tools and functions to work with generative AI models, including deployment, fine-tuning, and inference capabilities. It integrates with OCI Data Science to streamline the process of deploying and managing LLM applications.
Reference
Oracle ADS documentation
Guides on deploying AI models using Oracle Data Science services
NEW QUESTION # 54
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of- Thought, Least-to-most, or Step-Back prompting technique.
L Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50.
2. Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question.
3. To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, well explore how they trap heat in the Earths atmosphere.
Answer: A
NEW QUESTION # 55
How does a presence penalty function in language model generation?
Answer: C
Explanation:
A presence penalty is a mechanism used in language model generation to discourage repetition of words or phrases in generated text. This is crucial for improving diversity in AI-generated responses.
How It Works:
The presence penalty increases the loss associated with words that have already appeared in the output.
The model is less likely to generate the same word multiple times, leading to more diverse responses.
Unlike frequency penalties, which increase with repeated occurrences, presence penalties apply as soon as a word appears.
Key Use Cases:
Avoiding redundant phrases in AI-generated text.
Enhancing creative writing applications where repetitive wording is undesirable.
Making chatbot conversations more engaging and natural.
🔹 Oracle Generative AI Reference:
Oracle's generative AI models implement presence and frequency penalties as part of their fine-tuning and model inference processes to balance text coherence and diversity.
NEW QUESTION # 56
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