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BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models.

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This study introduces Budget-guided Iterative search strategy for automatic Parameter Efficient Fine-Tuning (PEFT), improving efficiency and performance. BIPEFT optimizes automatic PEFT by disentangling search spaces and using budget-guided early selection.

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Parameter Efficient Fine-Tuning (PEFT) is crucial for adapting large language models.
  • Manual PEFT design often leads to suboptimal results.
  • Existing automatic PEFT methods struggle with search space complexity and efficiency.

Purpose of the Study:

  • To develop a more efficient and effective automatic PEFT strategy.
  • To address challenges in search space entanglement and parameter budget integration.
  • To enhance the performance of PEFT for downstream tasks.

Main Methods:

  • Introduced Budget-guided Iterative search strategy for automatic PEFT (BIPEFT).
  • Employed an iterative search to disentangle binary module and rank dimension search spaces.
  • Designed early selection strategies guided by parameter budgets.

Main Results:

  • BIPEFT significantly enhances search efficiency in automatic PEFT.
  • Early selection strategies accelerate learning by removing unimportant modules.
  • Demonstrated superior performance of BIPEFT on public benchmarks with low parameter budgets.

Conclusions:

  • BIPEFT offers an efficient and effective solution for automatic PEFT.
  • The budget-guided iterative approach overcomes limitations of previous methods.
  • Achieves high performance in downstream tasks using minimal parameters.