Explanation: Comprehensive and Detailed In -Depth Explanation= Fine-tuning typically involves updating all parameters of an LLM using labeled, task -specific data to adapt it to a specific task, which is computationally expensive. Parameter Efficient Fine -Tuning (PEFT), such as methods like LoRA (Low -Rank Adaptation), updates only a small subset of parameters (often newly added ones) while still using labeled, task -specific data, making it more efficient. Option C correctly captures this distinction. Option A is wrong because continuous pretraining uses unlabeled data and isn’t task -specific. Option B is incorrect as PEFT and Soft Prompting don’t modify all parameters, and Soft Prompting typically uses labeled examples indirectly. Option D is inaccurate because continuous pretraining modifies parameters, while SoftPrompting doesn’t. : OCI 2025 Generative AI documentation likely discusses Fine-tuning and PEFT under model customization techniques. What is prompt engineering in the context of Large Language Models (LLMs)?
Explanation: Comprehensive and Detailed In -Depth Explanation= In LLMs, "hallucination" refers to the generation of plausible -sounding but factually incorrect or irrelevant content, often presented with confidence. This occurs due to the model’s reliance on patterns in training data rather than factual grounding, making Option D correct. Option A describes a positive trait, not hallucination. Option B is unrelated, as hallucination isn’t a performance - enhancing technique. Option C pertains to multimodal models, not the general definition of hallucination in LLMs. : OCI 2025 Generative AI documentation likely addresses hallucination under model limitations or evaluation metrics. What does in -context learning in Large Language Models involve?
Comprehensive and Detailed In -Depth Explanation= Embeddings in NLP are dense, numerical vectors that represent words, phrases, or sentences in a way that captures their semantic meaning and relationships (e.g., "king" and "queen" being close in vector space). This enables models to process text mathematically, making Option C correct. Option A is false, as embeddings simplify processing, not increase complexity. Option B relates to translation, not embeddings’ primary purpose. Option D is incorrect, as embeddings aren’t primarily for compression but for representation. : OCI 2025 Generative AI documentation likely covers embeddings under data preprocessing or vector databases. What is the main advantage of using few-shot model prompting to customize a Large Language Model (LLM)?
Comprehensive and Detailed In -Depth Explanation= In Dedicated AI Clusters (e.g., in OCI), GPUs are allocated exclusively to a customer for their generative AI tasks, ensuring isolation for security, performance, and privacy. This makes Option B correct. Option A describes shared resources, not dedicated clusters. Option C is false, as GPUs are for computation, not storage. Option D is incorrect, as public Internet connections would compromise security and efficiency. : OCI 2025 Generative AI documentation likely details GPU isolation under DedicatedAI Clusters. What happens if a period (.) is used as a stop sequence in text generation?
contexts). Option D is inaccurate, as penalties aren’t random but frequency -based. : OCI 2025 Generative AI documentation likely covers frequency penalties under output control parameters. Below is the next batch of 10 questions (11 –20) from your list, formatted as requested with detailed explanations. These answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity. Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?
➢ TOTAL QUESTIONS: 168 What is the role of temperature in the decoding process of a Large Language Model (LLM)?
