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NVIDIA NCA-GENL Exam - Topic 7 Question 8 Discussion
Topic #: 7
[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)
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.'
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