Dual-Space Knowledge Distillation: A Novel Framework for Compressing Large Language Models

Khái niệm cốt lõi

A new dual-space knowledge distillation (DSKD) framework that unifies the output spaces of the teacher and student models to enhance the similarity between them and enable knowledge transfer, even for models with different vocabularies.

Tóm tắt

The paper proposes a novel framework called dual-space knowledge distillation (DSKD) to address the limitations of the current white-box knowledge distillation (KD) framework for compressing large language models (LLMs).
The key insights are:

The current white-box KD framework leads to low similarity between the teacher and student models on both representation and distribution levels, due to the discrepancy between their output spaces.
The current framework also requires the teacher and student models to have the same vocabulary, which is often not the case for different LLMs.

To address these issues, the DSKD framework unifies the output spaces of the teacher and student models by projecting their hidden states into a shared representation space. This allows the distributions of the two models to be produced by the same prediction head, enhancing their similarity.
Furthermore, the authors develop a cross-model attention (CMA) mechanism to automatically align the tokens between the teacher and student models with different vocabularies. This enables DSKD to support KD between any two LLMs, regardless of their vocabularies.
Experiments on instruction-following benchmarks show that DSKD significantly outperforms the current white-box KD framework with various distance functions. DSKD with CMA also surpasses existing KD methods for LLMs with different vocabularies.

Thống kê

The teacher model GPT2-1.5B outperforms the student model GPT2-120M by 6.8 Rouge-L points on average across the test sets.
The teacher model LLaMA2-7B outperforms the student model TinyLLaMA-1.1B by 4.82 Rouge-L points on average across the test sets.

Trích dẫn

"The current framework usually yields low similarity between the teacher and student models on both representation and distribution levels."
"A key condition for current white-box KD is that the two models should share the same vocabulary, which, however, is hardly satisfied for various LLMs in this era."

Thông tin chi tiết chính được chắt lọc từ

Dual-Space Knowledge Distillation for Large Language Models

by Songming Zha... lúc arxiv.org 10-02-2024

https://arxiv.org/pdf/2406.17328.pdf

Yêu cầu sâu hơn

How can the cross-model attention mechanism be further improved to better align the tokens between teacher and student models with different vocabularies?

The cross-model attention (CMA) mechanism can be enhanced in several ways to improve the alignment of tokens between teacher and student models with different vocabularies. One potential improvement is to incorporate a more sophisticated alignment strategy that leverages contextual embeddings. By utilizing contextualized representations from models like BERT or RoBERTa, the alignment process can be made more sensitive to the semantic meaning of tokens rather than relying solely on their surface forms. This could involve training a separate alignment model that learns to map tokens from the teacher's vocabulary to the student's vocabulary based on their contextual embeddings.
Additionally, integrating a multi-head attention mechanism could allow the model to capture different types of relationships between tokens, such as syntactic and semantic similarities. This would enable the model to create a richer representation of the alignment between tokens, potentially leading to better performance in knowledge distillation.
Another avenue for improvement is to incorporate feedback loops where the alignment is iteratively refined based on the performance of the student model. By evaluating the student’s outputs and adjusting the alignment accordingly, the CMA mechanism can become more adaptive and responsive to the specific challenges posed by different vocabularies.

What other techniques beyond knowledge distillation could be explored to compress large language models while preserving their performance?

Beyond knowledge distillation (KD), several other techniques can be explored to compress large language models (LLMs) while maintaining their performance. One such technique is parameter pruning, which involves removing less important weights from the model. This can be achieved through methods like magnitude pruning, where weights below a certain threshold are set to zero, or more advanced techniques that consider the impact of each weight on the model's performance.
Quantization is another effective method for model compression. This technique reduces the precision of the weights and activations, allowing the model to use lower-bit representations (e.g., converting 32-bit floats to 8-bit integers). This not only decreases the model size but also speeds up inference without significantly sacrificing accuracy.
Low-rank factorization can also be employed, where the weight matrices of the model are approximated using low-rank matrices. This reduces the number of parameters while preserving the model's ability to capture complex patterns in the data.
Additionally, knowledge transfer techniques, such as using smaller models trained on the outputs of larger models, can be explored. This approach can help in creating lightweight models that retain the performance characteristics of their larger counterparts.
Lastly, architecture search and designing more efficient architectures (e.g., using transformer variants like Linformer or Reformer) can lead to inherently smaller models that require fewer parameters while still achieving competitive performance on various tasks.

How can the proposed DSKD framework be extended to other types of neural models beyond language models?

The proposed Dual-Space Knowledge Distillation (DSKD) framework can be extended to other types of neural models beyond language models by adapting its core principles to different domains and architectures. For instance, in computer vision, the DSKD framework can be applied to convolutional neural networks (CNNs) by unifying the output spaces of the teacher and student models through feature maps instead of token distributions. This would involve projecting the feature maps from the teacher model into the representation space of the student model, similar to how hidden states are projected in the original DSKD framework.
In the context of reinforcement learning, DSKD can be adapted to distill knowledge from a more complex policy network (teacher) to a simpler one (student). The framework can be modified to align the action distributions of both models, ensuring that the student learns to mimic the teacher's decision-making process effectively.
Moreover, the cross-model attention mechanism can be generalized to align features or outputs from different types of models, such as aligning the outputs of a generative model with a discriminative model. This would allow for knowledge transfer between models that operate on different principles, enhancing the versatility of the DSKD framework.
Finally, the principles of DSKD can be integrated into multi-modal models, where knowledge is distilled across different modalities (e.g., text, images, audio). By projecting and aligning the outputs from different modalities, the framework can facilitate effective knowledge transfer, leading to improved performance in multi-modal tasks.