Sparse Attention Mechanisms in Large Language Models: Applications, Classification, Performance Analysis, and Optimization

Jingxuan Bai

doi:10.23977/acss.2024.080618

Sparse Attention Mechanisms in Large Language Models: Applications, Classification, Performance Analysis, and Optimization

Download as PDF

DOI: 10.23977/acss.2024.080618 | Downloads: 27 | Views: 1182

Author(s)

Jingxuan Bai ¹

Affiliation(s)

¹ School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China

Corresponding Author

Jingxuan Bai

ABSTRACT

This paper explores the application and performance analysis of sparse attention mechanisms in large language models (LLMs), highlighting their ability to reduce the computational complexity of the traditional Transformer architecture for long sequences, it also reviews various sparse attention strategies that enhance efficiency by minimizing token interactions while preserving model performance, addressing the limitations of conventional models. A novel classification framework categorizes these mechanisms into global, local, and hybrid strategies. Through performance analyses of key models such as Longformer, Reformer, and BIGBIRD, this paper demonstrates their advantages in tasks like document understanding, information extraction, and image generation. Additionally, this paper proposes strategies for performance enhancement, including multimodal potential, integration with knowledge distillation, and anchor-based methods, to further optimize the effectiveness of sparse attention mechanisms in large language models and identify their potential pathways for development. These contributions provide a comprehensive understanding for beginners studying sparse attention mechanisms and offer possible directions for future research to improve performance and efficiency in large-scale NLP tasks.

KEYWORDS

Sparse Attention Mechanism, Large Language Models, Performance Improvement Strategies, Transformer Model, Time Complexity

CITE THIS PAPER

Jingxuan Bai, Sparse Attention Mechanisms in Large Language Models: Applications, Classification, Performance Analysis, and Optimization. Advances in Computer, Signals and Systems (2024) Vol. 8: 130-136. DOI: http://dx.doi.org/10.23977/acss.2024.080618.

REFERENCES

[1] Patil R, Gudivada V. A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs). Appl Sci. 2024; 14(5):2074.
[2] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention Is All You Need. Adv Neural Inf Process Syst. 2017; 30: 5998–6008.
[3] Beltagy I, Peters ME, Cohan A. Longformer: The Long-Document Transformer. arXiv preprint [cs.CL]. 2020.
[4] Zaheer M, Guruganesh G, Dubey KA, Ainslie J, Alberti C, Ontanon S, Pham P, Ravula A, Wang Q, Yang L, Ahmed A. Big Bird: Transformers for Longer Sequences. Adv Neural Inf Process Syst. 2020; 33: 17283-17297.
[5] Hao C, Zhang P, Xie M, Zhao D. Recurrent Transformers for Long Document Understanding. In: CCF International Conference on Natural Language Processing and Chinese Computing. Cham: Springer Nature Switzerland; 2023. p. 57-68.
[6] Kitaev N, Kaiser Ł, Levskaya A. Reformer: The Efficient Transformer. arXiv preprint [cs.LG]. 2020.
[7] Child R, Gray S, Radford A, Sutskever I. Generating Long Sequences with Sparse Transformers. arXiv preprint [cs.LG]. 2019.
[8] Griffiths TL, Steyvers M. (2004) Distributional semantics and the problem of semantic similarity. Proc Natl Acad Sci U S A, 101: 8171-8176.
[9] Tan, H., & Bansal, M. (2019). LXMERT: Learning Cross-Modality Encoder Representations from Transformers. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1-14.
[10] Tay Y, Dehghani M, Abnar S, Shen Y, Bahri D, Pham P, Rao J, Yang L, Ruder S, Metzler D. Long range arena: A benchmark for efficient transformers. arXiv preprint arXiv:2011.04006. 2020 Nov 8.
[11] Mostafa H, Wang X. Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization. InInternational Conference on Machine Learning 2019 May 24 (pp. 4646-4655). PMLR.
[12] Hinton G. Distilling the Knowledge in a Neural Network. arXiv preprint arXiv:1503.02531. 2015.
[13] De Kergorlay HL, Higham DJ. Consistency of anchor-based spectral clustering. Information and Inference: A Journal of the IMA. 2022 Sep; 11(3):801-822.

Subscription

E-Mail Alert

Downloads:	38553
Visits:	697918

Sparse Attention Mechanisms in Large Language Models: Applications, Classification, Performance Analysis, and Optimization

Author(s)

Affiliation(s)

Corresponding Author

ABSTRACT

KEYWORDS

CITE THIS PAPER

REFERENCES

RESOURCES

JOIN US

PUBLICATION SERVICES

CONTACT US