Comparing the Effectiveness of Knowledge Distillation and Pretraining from Scratch under a Fixed Computation Budget

المفاهيم الأساسية

Under a fixed computation budget, pretraining from scratch can be as effective as vanilla knowledge distillation, but more advanced distillation strategies like TinyBERT and MiniLM still outperform pretraining from scratch.

الملخص

The study compares the performance of pretraining from scratch (No-KD) against various knowledge distillation (KD) strategies for masked language modeling (MLM) under a fixed computation budget.

In the optimal setting with unlimited pretraining tokens, No-KD performs comparably to vanilla KD, exhibiting an average improvement of 0.4 and 0.1 points for 6-layer and 12-layer models on the GLUE benchmark, respectively. However, No-KD falls short of surpassing more advanced KD strategies like TinyBERT and MiniLM.

When the available data is limited within the fixed compute budget, KD strategies outperform No-KD by a larger margin. No-KD, though faster, needs more epochs, whereas KD strategies can extract more information from the limited data.

The results suggest that while No-KD can be as effective as vanilla KD under a fair setup, more sophisticated KD strategies still outperform No-KD, even when accounting for the compute budget. The study also highlights the importance of CoLA performance, which benefits significantly from masked language modeling and scales well with the number of tokens encountered during pretraining.

الإحصائيات

No-KD6 processes 4.6B tokens within the fixed compute budget.
Vanilla-KD6, MiniLM6, and TinyBERT6 process 2.6B tokens within the fixed compute budget.
No-KD12 processes 4.6B tokens within the fixed compute budget.
Vanilla-KD12, MiniLM12, and TinyBERT12 process 2.6B tokens within the fixed compute budget.
No-KD6 with limited data processes 27.9B tokens within the fixed (increased) compute budget.
Vanilla-KD6, MiniLM6, and TinyBERT6 with limited data process 15.4B, 15.6B, and 15.6B tokens, respectively, within the fixed (increased) compute budget.

اقتباسات

"Scaling laws of LM pretraining suggest that smaller models can close the gap to larger counterparts if trained on more data (i.e., processing more tokens)—and under a fixed computation budget, smaller models are able be process more data than larger models."
"Downstream results on GLUE, however, do not confirm our hypothesis: while pretraining from scratch performs comparably to ordinary KD under a fixed computation budget, more sophisticated KD strategies, namely TinyBERT (Jiao et al., 2020) and MiniLM (Wang et al., 2023), outperform it by a notable margin."
"We further find that KD yields larger gains over pretraining from scratch when the data must be repeated under the fixed computation budget."

الرؤى الأساسية المستخلصة من

Knowledge Distillation vs. Pretraining from Scratch under a Fixed (Computation) Budget

by Minh... في arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19319.pdf

استفسارات أعمق

How would the results change if the teacher model's computation budget was also considered in the fair comparison setup

Considering the teacher model's computation budget in the fair comparison setup could potentially alter the results significantly. The computation budget of the teacher model directly impacts the quality and quantity of knowledge that can be distilled into the student model. If the teacher model has a significantly larger computation budget, it may have access to more diverse and extensive data during pretraining, leading to a richer knowledge base. In such a scenario, the distillation process could be more effective, resulting in better performance of the student model compared to pretraining from scratch. On the other hand, if the teacher model's computation budget is limited, the distilled knowledge may not be as comprehensive, potentially narrowing the performance gap between No-KD and KD strategies. Therefore, accounting for the teacher model's computation budget in the comparison setup is crucial for a more comprehensive evaluation of the effectiveness of knowledge distillation.

What are the potential drawbacks or limitations of the scaling law assumptions used in this study, and how might they impact the generalizability of the findings

The scaling law assumptions utilized in this study, while informative, come with certain drawbacks and limitations that could impact the generalizability of the findings. One limitation is the assumption that smaller models can compensate for their lower learning efficiency by processing more tokens within the same budget. This assumption may not hold true in all cases, as the relationship between model size, data processing capacity, and performance can be influenced by various factors such as model architecture, task complexity, and dataset characteristics. Additionally, the scaling laws may not account for the non-linear complexities of language modeling tasks, where certain tasks may benefit more from larger models regardless of the computation budget. Moreover, the scaling laws may not consider the impact of hyperparameters, optimization techniques, or specific model configurations on the performance of language models. These limitations could affect the applicability of the scaling laws to different scenarios and models, potentially impacting the generalizability of the study's findings.

Could the performance differences between No-KD and KD strategies be attributed to factors beyond just the computation budget, such as the ability to better leverage the teacher's knowledge or the inherent advantages of distillation techniques

The performance differences between No-KD and KD strategies observed in the study could be attributed to various factors beyond just the computation budget. While the computation budget plays a significant role in determining the amount of data processed and the model's learning capacity, other factors may also contribute to the performance disparities. One key factor is the ability of KD strategies to leverage the teacher's knowledge effectively during the distillation process. Advanced KD strategies like TinyBERT and MiniLM employ sophisticated techniques to distill knowledge from the teacher model more efficiently, leading to enhanced performance in the student model. Additionally, the inherent advantages of distillation techniques, such as transferring knowledge from a larger, pretrained model to a smaller model, can contribute to improved performance by leveraging the teacher's expertise and generalization capabilities. Furthermore, the specific distillation objectives and methodologies used in KD strategies can also impact the performance differences, as they determine how effectively the student model can capture and utilize the distilled knowledge from the teacher model.