AdaLoRA paper overview
AdaLoRA paper overview
Preface
Pretrained language models (PLMs) have demonstrated superior performance in various natural language processing tasks. To adapt to downstream tasks, the most common approach is to fine-tune all parameters of a pre-trained model (full fine-tuning). However, pre-trained models often require large amounts of memory. For example, the BERT model contains up to 300 million parameters, the T5 model contains up to 11 billion parameters, and the GPT-3 model contains up to 175 billion parameters. When building NLP systems based on these pre-trained models, it is often necessary to handle multiple tasks simultaneously. In the presence of a large number of downstream tasks, full fine-tuning requires each task to maintain a separate copy of the large model, which results in prohibitive memory consumption.
The researchers proposed two main lines of research to reduce fine-tuning parameters while maintaining or even improving the performance of PLM. Specifically, one line of research focuses on adding small neural modules to PLM and only fine-tuning these modules for each task, with the base model remaining frozen and shared between tasks.
Another route, without modifying the model architecture, models incremental updates of pre-trained weights in a parameter-efficient manner, such as lora.
Summary
Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in natural language processing. However, it is common practice to fine-tune all parameters in a pre-trained model, which becomes prohibitive when there are a large number of downstream tasks.
Therefore, many fine-tuning methods have been proposed to learn incremental updates of pre-trained weights in a parameter-efficient manner, such as low-rank increments. These methods usually distribute the incremental update budget evenly across all pre-trained weight matrices, ignoring the different importance of different weight parameters.
Therefore, tuning performance is suboptimal. To bridge this gap, we propose the AdaLoRA algorithm, which adaptively allocates parameter budgets between weight matrices based on their importance scores. In particular, AdaLoRA parameterizes incremental updates in the form of singular value decomposition. This novel approach allows us to efficiently prune singular values of unimportant updates, essentially to reduce their parameter budget but avoid intensive exact SVD computations.
We work onnatural language processing, question answering and< /span>Conducted extensive experiments on several pre-trained models to verify the effectiveness of AdaLoRA. The results show that AdaLoRA shows significant improvements over the baseline, especially under low-budget settings. Natural language generation
Ten questions about the paper
- What problem is the paper trying to solve?
This paper attempts to solve the problem of how to efficiently allocate parameter budgets when fine-tuning pre-trained language models.
- Is this a new problem?
This is a new question. The paper points out that existing methods do not consider the importance differences of different modules and layers, resulting in improper parameter allocation.
- What scientific hypothesis does this article test?
The paper wants to verify the hypothesis that "dynamically allocating parameter budgets based on module importance can improve model performance."
- What are the relevant studies? How to classify? Who are the noteworthy researchers in the field on this topic?
Related research includes full fine-tuning, BitFit, Adapter fine-tuning, LoRA, etc. Among them, LoRA is a state-of-the-art research proposed by noteworthy researchers in the current field.
- What is the key to the solution mentioned in the paper?
The key solution of the paper is: parametric method based on singular value decomposition and adaptive budget allocation based on the importance index of the new design
- How were the experiments in the paper designed?
The experimental design includes comparing the performance of AdaLoRA to multiple baselines on multiple datasets and models.
- What is the data set used for quantitative evaluation? Is the code open source?
The datasets used for evaluation are GLUE, SQuAD, and text summarization datasets. The code has been open sourced.
- Do the experiments and results in the paper well support the scientific hypothesis that needs to be tested?
The experimental results well support the hypothesis that AdaLoRA outperforms other methods at different budget levels.
- What contribution does this paper make?
The main contribution of the paper is to propose the AdaLoRA method to achieve efficient fine-tuning of parameters.
- What’s next? Is there any work that can be further developed?
Next work can continue to explore other ways to dynamically allocate parameter budgets, or verify the effectiveness of AdaLoRA on more tasks.
singular value decomposition
SVD (Singular Value Decomposition) is a matrix decomposition method used to decompose a matrix into three parts: a left singular vector matrix, a diagonal matrix (containing singular values), and a right singular vector matrix. This method can effectively reduce the rank of the matrix, thereby reducing computational complexity and storage requirements. In this context, SVD is used to adjust the incremental updates of the pre-trained weight matrix in order to reduce parameter overhead while maintaining performance.
experiment
Method comparison
Research methods
- Full fine-tuning
- Bitfit
- Adapter tuning
- LoRA
- AdaLoRA
Datasets and models
DeBERTaV3-based
GLUE
natural language processing
Models and datasets
DeBERTaV3-based
GLUE
Experimental results
daLoRA achieves better or equivalent performance on all datasets at all budget levels. For example, when the parameter budget is 0.3M, AdaLoRA's accuracy on RTE reaches 87.36%, which is 1.8% higher than the best-performing baseline. Furthermore, very low-budget AdaLoRA generally performs better than high-budget baselines.
Q&A
Models and datasets
DeBERTaV3-based
SQuAD v1.1
Experimental results
Main results. The figure below summarizes the experimental results of our fine-tuning DeBERTaV3-base under four different budget settings: 0.08%, 0.16%, 0.32% and 0.65% of the pre-training parameters. From the results, we see that AdaLoRA consistently outperforms existing methods in both evaluation metrics (Exact Match (EM) and F1) at all budget levels. Note that the performance of the Houlsby adapter and the Pfeiffer adapter drops significantly as we reduce the parameter budget. In contrast, our method shows consistent performance under different budget levels. For example, AdaLoRA achieves 88.7% F1 on SQuADv2.0 with a minimum budget of 0.08%. It's close to its high-budget performance and also beats the best-performing baseline by 1.2%.
natural language generation
Models and datasets
BART-large
XSum 、 CNN/DailyMail
Experimental results
Main results. The experimental results are shown in the figure below. We compared the fine-tuning performance under four budget levels: the number of trainable parameters was 0.13%, 0.26%, 1.10% and 2.20% of the total number of pre-trained parameters respectively. We see that AdaLoRA achieves better or comparable performance to the baseline on two datasets (XSum and CNN/DailyMail) at all budget levels. For example, when the budget level is 1.10%, AdaLoRA has an R-2 score of 21.13, while LoRA has an R-2 score of 19.89
in conclusion
A parameter-efficient fine-tuning method is proposed: AdaLoRA, which adaptively allocates parameter budgets based on importance scores.
In AdaLoRA, we parameterize the incremental updates of the weight matrix in the form of singular value decomposition.
We then dynamically allocate the parameter budget among delta matrices based on a new importance metric by manipulating singular values. This method effectively improves model performance and parameter efficiency.
We conduct extensive experiments on natural language processing, question answering, and natural language generation tasks. Results show that AdaLoRA outperforms existing methods