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Efficient Transformer Knowledge Distillation: A Performance Review (2311.13657v1)

Published 22 Nov 2023 in cs.CL and cs.LG

Abstract: As pretrained transformer LLMs continue to achieve state-of-the-art performance, the Natural Language Processing community has pushed for advances in model compression and efficient attention mechanisms to address high computational requirements and limited input sequence length. Despite these separate efforts, no investigation has been done into the intersection of these two fields. In this work, we provide an evaluation of model compression via knowledge distillation on efficient attention transformers. We provide cost-performance trade-offs for the compression of state-of-the-art efficient attention architectures and the gains made in performance in comparison to their full attention counterparts. Furthermore, we introduce a new long-context Named Entity Recognition dataset, GONERD, to train and test the performance of NER models on long sequences. We find that distilled efficient attention transformers can preserve a significant amount of original model performance, preserving up to 98.6% across short-context tasks (GLUE, SQUAD, CoNLL-2003), up to 94.6% across long-context Question-and-Answering tasks (HotpotQA, TriviaQA), and up to 98.8% on long-context Named Entity Recognition (GONERD), while decreasing inference times by up to 57.8%. We find that, for most models on most tasks, performing knowledge distillation is an effective method to yield high-performing efficient attention models with low costs.

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Citations (5)
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Summary

  • The paper presents a comprehensive evaluation of efficient transformer knowledge distillation, preserving up to 98.8% performance with inference time reductions of up to 57.8%.
  • It introduces GONERD, a new long-context NER benchmark, and details the Convert-Then-Distill approach that enables effective compression on models like Longformer-RoBERTa.
  • The study empirically analyzes data utilization in the distillation process, finding that the combination of OSCAR and BookCorpus enhances performance across diverse NLP tasks.

Analysis of Efficient Transformer Knowledge Distillation: A Performance Review

The paper "Efficient Transformer Knowledge Distillation: A Performance Review" explores the intersection between model compression through knowledge distillation (KD) and the application of efficient attention mechanisms in transformer models. Knowledge distillation has been previously established as an effective technique for reducing model size and inference latency, while efficient transformers are designed to handle longer sequences with lower computational overhead.

Contributions and Results

The authors make several noteworthy contributions:

  1. Performance Evaluation: The paper provides an extensive evaluation of a set of pretrained efficient transformer models and their corresponding compressed student models. The evaluation covers a range of NLP tasks including GLUE, SQuAD, HotpotQA, TriviaQA, CoNLL-2003, and GONERD. Impressively, the distilled models preserved up to 98.6% of their original model performance on short-context tasks and up to 98.8% on long-context NER tasks, with a notable reduction in inference times by up to 57.8%.
  2. Introduction of GONERD: The authors introduce GONERD (Giant Oak NER Dataset), a new benchmark dataset specifically designed for evaluating long-context Named Entity Recognition (NER) models. GONERD provides a robust testing ground for models by comprising longer sequences when compared to traditional NER datasets like CoNLL-2003.
  3. Methodology for Efficient Attention Models: Through the Convert-Then-Distill approach, the paper describes a methodology for compressing efficient transformers. The Longformer-RoBERTa models showed particularly promising results, maintaining up to 95.9% of original performance on the GONERD task with significantly reduced inference costs.
  4. Empirical Investigation on Data Utilization: An empirical paper on the impact of different datasets used during the KD process is also presented. Results indicate that the combination of OSCAR and BookCorpus yielded better performance across various benchmarks.

Implications

The research highlights the practicality and effectiveness of using knowledge distillation combined with efficient attention models to address the high computational demands of traditional transformer models. By focusing on transforming pretrained models into efficient students, the paper provides substantial evidence that the KD process can lead to considerable cost savings in inference while still supporting the performance requirements needed for both short- and long-context NLP tasks.

Prospective Directions

The insights from applying KD to efficient transformers open up several directions for future research:

  • Exploration of Distill-Then-Convert Paradigm: The paper focuses on the Convert-Then-Distill methodology, proposing potential benefits but not exploring the reverse process. Understanding whether converting distillation into efficient models could provide better student outcomes warrants further analysis.
  • Customized Distillation Processes: While the distillation approach stems from established methods like those used in DistilBERT, future work could develop techniques specialized for various efficient attention mechanisms, potentially closing any performance gaps observed.
  • Broader Application Across Domains: Given the development of GONERD from web data, extending efficient attention transformers' applications to more diverse domains with domain-specific datasets could expand their utility in practice.

Conclusion

The paper provides a thorough assessment of integrating knowledge distillation with efficient attention mechanisms, marking a noteworthy step towards more computationally efficient NLP models capable of handling extended input sequences. The introduction of GONERD along with meticulous empirical evaluations extends an essential framework for future endeavors in improving the accessibility and practicality of state-of-the-art NLP technologies.

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