Key Points
1. Deploying large language models (LLMs) with several billion parameters can be impractical in many industrial use cases due to constraints such as cost, latency limitations, and hardware accessibility. Knowledge distillation (KD) offers a solution by compressing knowledge from resource-intensive large models to smaller ones.
2. Various strategies for knowledge distillation exist, but methods based on logits often require both teacher and student models to share the same tokenizer, limiting their applicability across different LLM families. In response to this limitation, the paper introduces the Universal Logit Distillation (ULD) loss, grounded in optimal transport, to enable distillation across models with different architectures and tokenizers.
3. The prevalent trend in NLP has been the use of large language models (LLMs) such as LLama, Mistral, Falcon, GPTNeoX, or Mixtral. However, challenges associated with resource consumption and deployment complexity have become increasingly prominent due to hardware availability, cost, and latency bottlenecks.
4. Knowledge distillation has been extensively explored and applied mostly on smaller student models derived from BERT. Two approaches are considered: the white-box approach, which reflects the teacher's architecture, and the black-box approach, offering flexibility and simplifying adoption through libraries and APIs.
5. Knowledge distillation for generative models has received less attention, particularly distillation from teacher-generated text and logit distillation. These methods often require the student model to share the same vocabulary and architecture as the teacher, limiting their applicability.
6. The Universal Logit Distillation (ULD) loss is introduced, utilizing the Wasserstein distance metric, to transfer knowledge from a teacher model to a student model, effectively overcoming the limitations of existing methods and enabling cross-architecture distillation.
7. Experimental results demonstrate the consistent effectiveness of the ULD loss across various tasks, including extractive question answering, generative question answering, and summarization, with diverse teacher-student model pairs, vocabularies, and model architectures.
8. By leveraging solely on logit information and adopting a black-box approach, the ULD loss extends its versatility and improves cross-architecture distillation, leading to improved performance of the student models in comparison to the teacher models.
9. Overall, the ULD loss proves to be a novel and effective method for distilling any decoder teacher model into any student model for LLM generative tasks, demonstrating superior performance over standard teacher-generated text distillation methods. The ULD loss enables more efficient and effective transfer of knowledge from large teacher models to smaller student models, addressing the limitations of existing distillation methods.
Summary
The research paper explores the deployment of large language models (LLMs), which face challenges such as cost, latency limitations, and hardware accessibility. The paper introduces the Universal Logit Distillation (ULD) loss, a knowledge distillation technique grounded in optimal transport, to address the limitations of existing methods based on logits. ULD loss enables distillation across models with different architectures and tokenizers, paving the way for a more widespread use of distillation techniques.
The prevailing trend of large language models has presented challenges in terms of resource consumption and deployment complexity due to hardware availability, cost, and latency bottlenecks. The study focuses on knowledge distillation (KD), a technique used to compress knowledge from resource-intensive large models to smaller ones. The paper introduces the ULD loss, which leverages a closed-form solution of optimal transport to facilitate large-scale fine-tuning due to its fast computation. The experimental results demonstrate the effectiveness of ULD loss in enabling distillation across models with different architectures and tokenizers, thereby addressing the challenges associated with deploying large language models.
The research demonstrates the effectiveness of ULD loss in enhancing the performance of various student models across different tasks, datasets, and architectures. The ULD loss consistently improves the performance of student models, even with smaller dataset sizes or student model sizes, and also prevents overfitting. Moreover, the study shows that ULD loss can effectively transfer knowledge from decoder teacher models to encoder-decoder student models, resulting in improved performance. The paper presents comprehensive experimental results and findings that validate the efficacy and versatility of the ULD loss in knowledge distillation for large language models.
Reference: https://arxiv.org/abs/2402.120...