With the remarkable capabilities, large language models (LLMs) have emerged
as essential elements in numerous NLP applications, while parameter-efficient
finetuning, especially LoRA, has gained popularity as a lightweight approach
for model customization. Meanwhile, various dropout methods, initially designed
for full finetuning with all the parameters updated, alleviates overfitting
associated with excessive parameter redundancy. Hence, a possible contradiction
arises from negligible trainable parameters of LoRA and the effectiveness of
previous dropout methods, which has been largely overlooked. To fill this gap,
we first confirm that parameter-efficient LoRA is also overfitting-prone. We
then revisit transformer-specific dropout methods, and establish their
equivalence and distinctions mathematically and empirically. Building upon this
comparative analysis, we introduce a unified framework for a comprehensive
investigation, which instantiates these methods based on dropping position,
structural pattern and compensation measure. Through this framework, we reveal
the new preferences and performance comparisons of them when involved with
limited trainable parameters. This framework also allows us to amalgamate the
most favorable aspects into a novel dropout method named HiddenKey. Extensive
experiments verify the remarkable superiority and sufficiency of HiddenKey
across multiple models and tasks, which highlights it as the preferred approach
for high-performance and parameter-efficient finetuning of LLMs.

基于对参数高效 LoRA 的研究，我们重新审视了特定于 Transformer 的 Dropout 方法的数学和经验上的等价性和区别，并基于此提出了一个统一的框架，揭示了当涉及到有限可训练参数时它们的新偏好和性能比较。这个框架还允许我们将最有利的方面融合成一个名为 HiddenKey 的新的 Dropout 方法，广泛的实验证实 HiddenKey 在多个模型和任务上具有显著的优势和足够性，将其作为大语言模型高性能和参数高效微调的首选方法。