Aligning Logit Distributions in Knowledge Distillation for Large Language Models

Temel Kavramlar

Neither forward Kullback-Leibler (FKL) divergence nor reverse Kullback-Leibler (RKL) divergence exhibits the expected mean-seeking or mode-seeking behaviors in knowledge distillation for large language models. Instead, both FKL and RKL converge to the same optimization objective after a sufficient number of epochs. However, due to practical constraints, large language models are rarely trained for such an extensive number of epochs. The authors propose an Adaptive Kullback-Leiber (AKL) divergence method that adaptively allocates weights to combine FKL and RKL, focusing on aligning the head and tail parts of the distributions.

Özet

The paper starts by discussing the use of Kullback-Leibler (KL) divergence in knowledge distillation (KD) for compressing large language models (LLMs). Contrary to previous assertions that reverse KL (RKL) divergence is mode-seeking and thus preferable over the mean-seeking forward KL (FKL) divergence, the authors demonstrate both empirically and theoretically that these properties do not hold for KD in LLMs.

The key insights are:

1. FKL and RKL share the same optimization objective, which is to align the logits of the student model with those of the teacher model. Both converge to the same solution after a sufficient number of epochs (more than 50 in the experiments).
2. However, in practice, LLMs are rarely trained for such an extensive number of epochs (e.g., 10 epochs in prior work). The authors find that FKL focuses on the head part of the distributions, while RKL focuses on the tail part at the beginning epochs.
3. Based on these observations, the authors propose a novel Adaptive Kullback-Leiber (AKL) divergence method, which adaptively allocates weights to combine FKL and RKL to better align the distributions.

The experimental results demonstrate that AKL outperforms the baseline methods on various benchmarks. Additionally, the authors use GPT-4 to evaluate the diversity and quality of the generated responses, showing that AKL can improve both aspects compared to the baselines.

İstatistikler

The training dataset contains 14k samples for training, 500 for validation, and 500 for testing.
The teacher models used are GPT-2 with 1.5B parameters and LLaMA with 6.7B parameters.
The student models are GPT-2 with 120M parameters and TinyLLaMA with 1.1B parameters.

Alıntılar

None

Önemli Bilgiler Şuradan Elde Edildi

Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models

by Taiqiang Wu,... : arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02657.pdf

Daha Derin Sorular

How can the proposed AKL divergence method be extended to other types of language models beyond autoregressive transformers

The Adaptive Kullback-Leiber (AKL) divergence method proposed in the study can be extended to other types of language models beyond autoregressive transformers by adapting the weight allocation strategy based on the specific characteristics of the model. For instance, for models that have different distributional properties or training objectives, the gap calculation and weight assignment in AKL can be customized to suit the requirements of the particular model architecture. By considering the unique features of different language models, such as bidirectional transformers or models with specific attention mechanisms, the AKL method can be tailored to optimize the knowledge distillation process effectively.

What are the potential limitations of the AKL divergence method, and how could it be further improved

One potential limitation of the AKL divergence method is the reliance on the gap calculation between the head and tail parts of the distributions. This approach may not always capture the full complexity of the relationship between the teacher and student models, especially in cases where the distributions exhibit more nuanced patterns. To address this limitation, the AKL method could be further improved by incorporating additional metrics or criteria to assess the alignment between the models. For example, introducing a dynamic weighting mechanism that adapts during the training process based on the model performance could enhance the flexibility and robustness of the AKL method.

How might the insights from this work on the behavior of FKL and RKL divergences in knowledge distillation inform the development of other compression techniques for large language models

The insights gained from the study on the behavior of Forward Kullback-Leiber (FKL) and Reverse Kullback-Leiber (RKL) divergences in knowledge distillation can inform the development of other compression techniques for large language models in several ways. Firstly, understanding that FKL and RKL converge to the same objective after a sufficient number of epochs can guide the design of more efficient compression methods that leverage both divergences effectively. Additionally, the observation that FKL focuses on the head part and RKL on the tail part at the beginning epochs suggests that a combination of these divergences could lead to better model alignment and compression. This insight can be applied to enhance existing compression techniques by incorporating a balanced approach that considers both the mean-seeking and mode-seeking behaviors of the divergences.