LoRA Meets Dropout: A Unified Framework for Parameter-Efficient Finetuning

Khái niệm cốt lõi

LoRA's limited trainable parameters can lead to overfitting, but a unified framework incorporating dropout methods like HiddenKey can mitigate this issue.

Tóm tắt

最近、大規模言語モデル（LLM）が急速に発展し、フルファインチューニングは高いストレージおよび推論コストを伴うため、パラメータ効率の良いファインチューニング（PEFT）手法が注目されています。PEFTは、ほとんどのパラメータを共有しながら競争力のあるパフォーマンスを維持する軽量な代替手法です。一方、Dropoutはトランスフォーマーモデルの性能向上に拡張されており、特定確率で各ニューロンをランダムに無効化します。Zehuiら（2019）は、自己注意メカニズム用に特別に設計された最初の変種であるDropAttentionを提案しています。Chenら（2021）はFeedForwardモジュールで隠れ表現に連続したスパン形式のマスクを適用するHiddenCutを導入しています。最近、Liら（2023）はDrop-before-softmaxスキームであるHiddenKeyを導入しました。

Thống kê

LoRA: Low-rank adaptation on large language models (Hu et al., 2021)
PEFT: Parameter-efficient finetuning methods (Houlsby et al., 2019; Lester et al., 2021; Hu et al., 2021)
Dropout: Random deactivation of neurons during training (Hinton et al., 2012)
DropAttention: Dropout method for self-attention mechanism (Zehui et al., 2019)
HiddenCut: Dropout method for hidden representations in feed-forward module (Chen et al., 2021)
HiddenKey: Drop-before-softmax scheme for key units (Li et al., 2023)

Trích dẫn

"LoRA imposes a low-rank decomposition on weight updates, effectively avoiding the issues of previous methods."
"Dropout randomly deactivates neurons to prevent co-adaptation and has been extended to improve transformer models."
"HiddenKey introduces a drop-before-softmax scheme, enhancing performance in LoRA scenarios."

Thông tin chi tiết chính được chắt lọc từ

LoRA Meets Dropout under a Unified Framework

by Sheng Wang,L... lúc arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.00812.pdf

Yêu cầu sâu hơn

How does the introduction of KL loss impact the training duration and model performance in HiddenKey

HiddenKey introduces KL loss to minimize the gap between training and inference stages, which can impact both the training duration and model performance. The calculation of KL loss requires two forward passes, leading to a longer training time compared to the original process. However, this increase in training duration can be mitigated by parallelizing the two forward passes or merging gradient updates from both branches. Despite potentially longer training times, the introduction of KL loss significantly improves model performance by enhancing dropout insensitivity and reducing overfitting.

What are the potential implications of using only HiddenKey− without additional compensation measures

Using only HiddenKey− without additional compensation measures may have implications on model performance and robustness. While HiddenKey− still outperforms baselines due to its optimized dropping positions and patterns for LoRA scenarios, it may not fully address the gap between training and inference stages as effectively as HiddenKey with KL loss. Without additional compensation measures like bidirectional KL divergence loss or Jensen-Shannon consistency regularization loss, there might be a slight compromise in achieving maximum performance gains across all datasets.

How do lightweight approaches like LoRA and PEFT compare in terms of performance and efficiency with traditional full-finetuning methods

In terms of performance and efficiency, lightweight approaches like LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning) offer significant advantages over traditional full-finetuning methods for large language models (LLMs). These lightweight methods allow for customization with minimal trainable parameters while preserving competitive performance levels similar to full-finetuning approaches. By freezing most parameters during finetuning processes, LoRA and PEFT reduce computational costs associated with updating all model parameters while maintaining high effectiveness in downstream tasks such as natural language understanding (NLU) and generation (NLG). This balance between efficiency and performance makes lightweight approaches a preferred choice for optimizing LLMs in various applications.