Oracle 1Z0-1127-24 Exam Dumps

Soft prompting is an efficient method for modifying LLM behavior without full retraining. Unlike fine-tuning, soft prompting adds learnable embeddings (soft prompts) to guide the model.

When Soft Prompting is Useful:

Enhances model behavior without full retraining.

Uses small trainable prompt tokens, avoiding large parameter updates.

Works well when labeled, task-specific data is unavailable.

Why Other Options Are Incorrect:

(A) is incorrect because continued pretraining involves modifying core model weights.

(C) is incorrect because adapting a model to a new domain is better suited to fine-tuning or full retraining.

(D) is incorrect because soft prompting is designed for low-data scenarios, while full fine-tuning requires labeled datasets.

Oracle Generative AI Reference:

Oracle AI supports efficient adaptation methods, including soft prompting and LoRA, to improve LLM flexibility.

The 'stop sequence' parameter in the OCI Generative AI Generation models is used to specify a string that signals the model to stop generating further content. When the model encounters this string during the generation process, it terminates the response. This parameter is useful for controlling the length and content of the generated text, ensuring that the output meets specific requirements or constraints.

Reference

OCI Generative AI service documentation

General principles of sequence generation in AI models

Prompt injection (jailbreaking) involves manipulating the language model to bypass its built-in restrictions and protocols. The provided scenario (A) exemplifies this by asking the model to find a creative way to provide information despite standard protocols preventing it from doing so. This type of prompt is designed to circumvent the model's constraints, leading to potentially unauthorized or unintended outputs.

Reference

Articles on AI safety and security

Studies on prompt injection attacks and defenses

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

The difference between 'Top k' and 'Top p' in selecting the next token in generative models lies in their selection criteria:

Top k: This method selects the next token from the top k tokens based on their probability scores. It restricts the selection to a fixed number of the most probable tokens, irrespective of their cumulative probability.

Top p: Also known as nucleus sampling, this method selects tokens based on the cumulative probability until it exceeds a certain threshold p. It dynamically adjusts the number of tokens considered, ensuring that the sum of their probabilities meets or exceeds the specified p value. This allows for a more flexible and often more diverse selection compared to Top k.

Reference

Research articles on sampling techniques in language models

Technical documentation for generative AI models in OCI