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What does PASS@K represent in evaluation metrics?

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שאלה #134479

		The percentage of correct predictions made by the model
		The average ranking of correct predictions made by the model
done		The percentage of predictions where the correct answer is found within the top K ranked options
		The number of incorrect predictions made by the model at position K

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How does PASS@K differ from traditional accuracy metrics?

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שאלה #134480

done		PASS@K considers a prediction as correct if the correct answer is within the top K predictions, whereas traditional accuracy metrics consider a prediction as correct only if it exactly matches the ground truth
		PASS@K and traditional accuracy metrics are synonymous and represent the same evaluation measure
		PASS@K evaluates the model's performance based on the ratio of true positives to false positives, while traditional accuracy metrics focus on overall correctness
		PASS@K evaluates the model's performance based on the ratio of true positives to true negatives, while traditional accuracy metrics focus on precision and recall

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If a model achieves a PASS@5 score of 0.8, what does this indicate?

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שאלה #134481

		The model correctly predicted 80% of the total instances in the dataset
done		In 80% of the predictions made by the model, the correct answer was found within the top 5 ranked options
		The model achieved an accuracy of 5% on the dataset
		The model achieved a precision score of 0.8 on the dataset

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In One-Shot Prompting, how many examples of a prompt are provided to the model during training?

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שאלה #134482

		None
done		One
		Multiple
		It depends on the complexity of the prompt

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What distinguishes Few-Shot Prompting from One-Shot Prompting?

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שאלה #134483

		Few-Shot Prompting provides more training data than One-Shot Prompting
done		Few-Shot Prompting involves providing the model with multiple examples of a prompt during training
		Few-Shot Prompting only allows the model to generate text from a single prompt
		Few-Shot Prompting does not involve training the model on any examples of a prompt

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How does Zero-Shot Prompting differ from Few-Shot Prompting?

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שאלה #134484

done		Zero-Shot Prompting involves providing the model with a prompt during inference that it has not been explicitly trained on, while Few-Shot Prompting involves training the model on a small number of examples of a prompt
		Zero-Shot Prompting provides more training data than Few-Shot Prompting
		Zero-Shot Prompting only allows the model to generate text from a single prompt, while Few-Shot Prompting allows the model to generate text from multiple prompts
		Zero-Shot Prompting and Few-Shot Prompting are synonymous and represent the same training strategy

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What does LoRA stand for in the context of language models?

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שאלה #134485

		Long-Range Attention
		Linguistic Optimization and Reliability Algorithm
done		LOW Rank Adaptation
		Layered Optimization and Ranking Architecture

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How does LoRA make language models more adaptable?

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שאלה #134486

		By increasing the complexity of the pre-trained model
		By fine-tuning the entire pre-trained model for each new task
sentiment_very_satisfied		By freezing the pre-trained model and adding smaller trainable matrices to each model layer
		By reducing the number of layers in the pre-trained model

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What is one benefit of using LoRA in language models?

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שאלה #134487

		It increases the computational complexity of the model
		It requires re-training the entire model for each new task
done		It significantly reduces the number of trainable parameters in the model
		It limits the model's adaptability to new tasks

נערך לפני 5 חודשים Shachar Adam

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What is a characteristic of a Low Rank Matrix?

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שאלה #134488

done		It has a significantly smaller number of linearly independent rows or columns compared to the total number
		It has a rank equal to the number of rows or columns in the matrix

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