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NVIDIA NCA-GENL Exam - Topic 7 Question 8 Discussion

[Fundamentals of Machine Learning and Neural Networks]What are the main advantages of instructed large language models over traditional, small language models (< 300M parameters)? (Pick the 2 correct responses)
D) Cheaper computational costs during inference. and E) Single generic model can do more than one task.
A) Trained without the need for labeled data.
B) Smaller latency, higher throughput.
C) It is easier to explain the predictions.

NVIDIA NCA-GENL Exam - Topic 7 Question 8 Discussion

Actual exam question for NVIDIA's NCA-GENL exam
Question #: 8
Topic #: 7
[All NCA-GENL Questions]

[Fundamentals of Machine Learning and Neural Networks]

What are the main advantages of instructed large language models over traditional, small language models (< 300M parameters)? (Pick the 2 correct responses)

Show Suggested Answer Hide Answer
Suggested Answer: D, E

Instructed large language models (LLMs), such as those supported by NVIDIA's NeMo framework, have significant advantages over smaller, traditional models:

Option D: LLMs often have cheaper computational costs during inference for certain tasks because they can generalize across multiple tasks without requiring task-specific retraining, unlike smaller models that may need separate models per task.

Option E: A single generic LLM can perform multiple tasks (e.g., text generation, classification, translation) due to its broad pre-training, unlike smaller models that are typically task-specific.

Option A is incorrect, as LLMs require large amounts of data, often labeled or curated, for pre-training. Option B is false, as LLMs typically have higher latency and lower throughput due to their size. Option C is misleading, as LLMs are often less interpretable than smaller models.


NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

Brown, T., et al. (2020). 'Language Models are Few-Shot Learners.'

by Vivienne at Jul 09, 2025, 03:24 AM

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Colby
8 months ago
E is a game changer for multitasking!
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Raina
8 months ago
Not sure about A, labeled data is still important in many cases.
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Zona
8 months ago
I think B is more relevant than people realize.
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Jacki
9 months ago
Surprised that people still doubt the efficiency of large models!
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Frederic
9 months ago
A and E are definitely the main advantages!
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Alaine
9 months ago
I definitely remember that larger models can perform various tasks with a single model, which seems like a big advantage over smaller ones.
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Gladis
9 months ago
I feel like smaller latency and higher throughput might be a characteristic of smaller models, but I can't recall the specifics.
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Chau
9 months ago
I'm not entirely sure, but I remember something about larger models being trained without labeled data. That might be an advantage?
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Jess
10 months ago
I think one of the advantages is that larger models can handle multiple tasks better, like in that practice question we did about multi-task learning.
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Alecia
10 months ago
I've got this! Large language models are more versatile, able to handle multiple tasks with a single generic model. And they're more cost-effective during inference compared to smaller models. Those are the two key advantages I'm confident about.
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Lisbeth
10 months ago
Ugh, I'm really struggling with this question. The differences between large and small language models are kind of fuzzy in my mind. I'll have to guess and hope for the best.
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Glenna
10 months ago
Okay, let's see. I remember learning that large language models can be trained without labeled data, which is a big advantage. And I think they also have lower latency and higher throughput than smaller models. I'll go with those two options.
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Clarence
10 months ago
Hmm, I'm a bit unsure about this one. I know large language models have some advantages over smaller models, but I'm not sure I can recall all the specifics. I'll have to think this through carefully.
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Gilma
11 months ago
This seems like a straightforward question about the advantages of large language models. I'm pretty confident I can identify the two correct responses.
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Joana
1 year ago
I believe A and E are the main advantages because large language models can be trained without labeled data and can perform multiple tasks.
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Kina
1 year ago
I'm not sure, I think D and E are the correct responses.
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Cheryl
1 year ago
I agree with Deane, A and E make sense.
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William
1 year ago
Hmm, tough choice. I'll go with A and E. Trained without labeled data? That's like getting a free lunch. And a model that can do more than one task? That's the AI version of a Renaissance man.
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Hannah
1 year ago
E. A model that can do more than one task? That's the AI version of a Renaissance man.
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Cordelia
1 year ago
A. Trained without labeled data? That's like getting a free lunch.
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Lorean
1 year ago
I'm going with C and E. Explainability is key, and a single model to rule them all? Sign me up! Although, I do wonder if the model will also do my laundry...
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Tanesha
1 year ago
B and D? What is this, a trick question? Everyone knows that bigger is better when it comes to language models. Latency and cost-efficiency are for the small fry.
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Mike
1 year ago
A and E are the clear winners here. Large language models are like the Swiss Army knives of AI - they can do it all with just a few parameters. No need for all that labeled data nonsense.
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Vincent
1 year ago
I agree, traditional small language models just can't compete with the versatility and power of instructed large language models.
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Herminia
1 year ago
A and E are definitely the way to go. Large language models can handle a wide range of tasks with ease.
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Deane
1 year ago
I think A and E are the main advantages.
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