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How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning enhances efficiency by updating only a small subset of transformer layers or parameters (e.g., via adapters), reducing computational load---Option D is correct. Option A (adding layers) increases complexity, not efficiency. Option B (all layers) describes Vanilla fine-tuning. Option C (excluding layers) is false---T-Few updates, not excludes. This selective approach optimizes resource use.
: OCI 2025 Generative AI documentation likely details T-Few under PEFT methods.
Which is the main characteristic of greedy decoding in the context of language model word prediction?
Comprehensive and Detailed In-Depth Explanation=
Greedy decoding selects the word with the highest probability at each step, optimizing locally without lookahead, making Option D correct. Option A (random low-probability) contradicts greedy's deterministic nature. Option B (high temperature) flattens distributions for diversity, not greediness. Option C (flattened distribution) aligns with sampling, not greedy decoding. Greedy is simple but can lack global coherence.
: OCI 2025 Generative AI documentation likely describes greedy decoding under decoding strategies.
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning, a Parameter-Efficient Fine-Tuning (PEFT) method, updates only a small fraction of an LLM's weights, reducing computational cost and overfitting risk compared to Vanilla fine-tuning (all weights). This makes Option C correct. Option A describes Vanilla fine-tuning. Option B is false---T-Few updates weights, not architecture. Option D is incorrect---T-Few typically reduces training time. T-Few optimizes efficiency.
: OCI 2025 Generative AI documentation likely highlights T-Few under fine-tuning options.
What is the role of temperature in the decoding process of a Large Language Model (LLM)?
Comprehensive and Detailed In-Depth Explanation=
Temperature is a hyperparameter in the decoding process of LLMs that controls the randomness of word selection by modifying the probability distribution over the vocabulary. A lower temperature (e.g., 0.1) sharpens the distribution, making the model more likely to select the highest-probability words, resulting in more deterministic and focused outputs. A higher temperature (e.g., 2.0) flattens the distribution, increasing the likelihood of selecting less probable words, thus introducing more randomness and creativity. Option D accurately describes this role. Option A is incorrect because temperature doesn't directly increase accuracy but influences output diversity. Option B is unrelated, as temperature doesn't dictate the number of words generated. Option C is also incorrect, as part-of-speech decisions are not directly tied to temperature but to the model's learned patterns.
: General LLM decoding principles, likely covered in OCI 2025 Generative AI documentation under decoding parameters like temperature.
When should you use the T-Few fine-tuning method for training a model?
Comprehensive and Detailed In-Depth Explanation=
T-Few is ideal for smaller datasets (e.g., a few thousand samples) where full fine-tuning risks overfitting and is computationally wasteful---Option C is correct. Option A (semantic understanding) is too vague---dataset size matters more. Option B (dedicated cluster) isn't a condition for T-Few. Option D (large datasets) favors Vanilla fine-tuning. T-Few excels in low-data scenarios.
: OCI 2025 Generative AI documentation likely specifies T-Few use cases under fine-tuning guidelines.
