NCA-GENL Question 46
Select 3You are tasked with identifying emerging trends and technologies in Large Language Models (LLMs) to guide your organization's AI strategy. After reading a recent research paper, you notice discussions on Retrieval-Augmented Generation (RAG), sparsity in transformer architectures, and fine-tuning with low-rank adaptation (LoRA). Which of the following trends or technologies should you prioritize for improving efficiency and scalability in LLMs?
- A
Implementing sparsity in transformer architectures to reduce computational costs
- B
Using Retrieval-Augmented Generation (RAG) to enhance LLM responses with external knowledge
- C
Deploying LoRA (Low-Rank Adaptation) to enable parameter-efficient fine-tuning
- D
Focusing solely on increasing model size to improve accuracy
- E
Adopting traditional rule-based NLP systems to replace LLMs for language tasks
Show answer and explanation
Correct answers: A, B, C
Explanation
The correct answers focus on emerging trends in LLM research that enhance efficiency and scalability. Sparsity in transformer architectures and LoRA are key for reducing computational costs and enabling parameter-efficient fine-tuning, respectively. RAG enhances the practical utility of LLMs by integrating external knowledge, showing how these trends collectively address the challenges of scalability, efficiency, and adaptability in modern AI systems.
- A. Correct.
Sparsity in transformer architectures is an emerging trend to improve efficiency by reducing the number of active parameters during computation.
- B. Correct.
Retrieval-Augmented Generation (RAG) integrates external knowledge sources with LLMs to improve response quality and reduce reliance on model size alone.
- C. Correct.
Low-Rank Adaptation (LoRA) is a technique for fine-tuning large models efficiently by updating only a small set of parameters, making it suitable for resource-constrained environments.
- D. Incorrect.
Increasing model size is not always the best approach, as it can lead to diminishing returns in accuracy while exponentially increasing computational and financial costs.
- E. Incorrect.
Traditional rule-based NLP systems are generally being replaced by LLMs due to their superior performance in handling diverse and complex language tasks.