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Parameter-Efficient Fine-Tuning (PEFT) in Large Language Models (LLM)
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Parameter-Efficient Fine-Tuning (PEFT) in Large Language Models (LLM)

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Questions and Answers

What is the main obstacle to the widespread application of large-scale models in various scenarios?

  • Significant computational resources and training time (correct)
  • Limited set of global parameters
  • Inability to combine different computational modules
  • Random routing phenomenon
  • What is a prominent paradigm in recent research to address the issue of demanding computational resources for fine-tuning large language models?

  • Parameter-Efficient Fine-Tuning (PEFT) (correct)
  • LoRA
  • Mixture of Experts (MoE)
  • Contrastive learning
  • Which method was introduced in the text to address the random routing phenomenon observed in Mixture of Experts (MoE)?

  • LoRA
  • MoELoRA
  • PEFT
  • Contrastive learning (correct)
  • How did MoELoRA perform compared to LoRA in math reasoning tasks?

    <p>Achieved 4.2% higher performance than LoRA</p> Signup and view all the answers

    Which approach outperformed LoRA significantly with the same number of parameters in the experiments conducted on math and common-sense reasoning benchmarks?

    <p>MoELoRA</p> Signup and view all the answers

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